JP5489602B2 - Cerebral blood flow calculation program, recording medium, and cerebral blood flow calculation method - Google Patents

Description

  The present invention relates to a cerebral blood flow calculation program for calculating cerebral blood flow by cerebral blood flow SPECT using a split dose method.

Conventionally, nuclear medicine imaging methods such as single photon emission computed tomography (SPECT) have been used for diagnosis of diseases in which local cerebral blood flow changes. For example, as a representative load test performed in cerebral blood flow SPECT, there is Diamox (registered trademark) load test, and ¹²³ I-IMP (N-isopropyl-p- [ ¹²³ I] iodoamphetamine having excellent blood flow linearity) ) Is used as a tracer. In particular, Diamox (registered trademark) loading was performed by split dose technique or one-day protocol using ¹²³ I-IMP SPECT twice in a short time of one day, and local in rest and load conditions. An inspection method for obtaining a cerebral blood flow distribution is used (see Non-Patent Document 1).

38 (A) and (B) show the protocol of the conventional divided administration method using ¹²³ I-IMP cerebral blood flow SPECT. In FIGS. 38A and 38B, the horizontal axis represents time, and the vertical axis in FIG. 38B represents brain radioactivity (count value). As shown in FIG. 38 (A), first, ¹²³ I-IMP (111MBq) is injected with the first intravenous injection (iv). Hereinafter, the time of the 1st intravenous injection is set to time 0, and it demonstrates using the elapsed time from time 0. FIG. From the first intravenous injection of ¹²³ I-IMP, continuous arterial blood sampling is performed after 6 minutes, and dynamic SPECT (1st. SPECT shown in FIG. 38B) is performed. After 8 minutes, Diamox® is infused intravenously. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 38B) is performed. In FIG. 38B, the residual radioactivity (background) by the first ¹²³ I-IMP intravenous injection at this time is indicated by a dotted line.

  For the calculation of cerebral blood flow, the MS (Microsphere model) method that requires continuous arterial blood sampling in the cerebral blood flow SPECT has been evaluated for its high accuracy (see Non-Patent Document 2).

Next, a local cerebral blood flow calculation method using an IMP / MS (Microsphere model) method using ¹²³ I-IMP (hereinafter abbreviated as “IMP”) will be described (see Non-Patent Document 3). Under certain conditions, the radioactivity Cb (μCi / g) in brain tissue for t minutes after IMP intravenous injection is expressed as shown in Formula 1A. Here, t is preferably 5 minutes or 6 minutes.

  Here, F is the cerebral blood flow (ml / 100 g / min), and Ca (t) is the arterial blood concentration of IMP at time t. Assume that arterial blood is continuously collected from an artery at a constant blood collection rate R (ml / min) from immediately after IMP intravenous injection to t minutes. When the total radioactivity of arterial blood (R · t (ml)) obtained in t minutes is A (μCi) and the ratio of true IMP excluding IMP metabolites in arterial blood is N, N · A is t It becomes radioactivity of IMP in arterial blood collected for 1 minute. If the cerebral blood flow F is constant during the measurement, the following formula 1B is obtained.

  Formula 1B mimics cerebral blood flow by continuously collecting arterial blood, and mimics the radioactivity Cb in brain tissue after t F and N · A / R are led to be equal. From Equation 1B, the following Equation 1C can be obtained.

The radioactivity Cb in the brain tissue for t minutes after IMP intravenous injection is measured using SPECT, and the total radioactivity A of arterial blood obtained in t minutes is measured with a well-type scintillation counter. However, since the counting efficiency of the two is different, a coefficient for performing the correction (cross calibration) is required. FIG. 39 is a diagram for explaining the measurement of cross calibration. As shown in FIG. 39, first, ¹²³ I solution (IMP-citrate buffer (0.1 mol / l) adjusted to 7.4 to 14.8 kBq / ml) is put into a cylindrical phantom 160. Subsequently, the SPECT device 162 takes an image. Here, the collection conditions, radius of rotation, steps, image processing, and the like are performed in the same manner as clinical imaging conditions. Next, ROI (position is close to the outer periphery, shape is arbitrary, size is 10 pixels or more) is set in the reconstructed image, and a count number S (count / pixel) per pixel is obtained. Finally, 1.0 ml of the ¹²³ I solution in the cylindrical phantom 160 is taken into a test tube 164 and the radioactivity W (cpm) is measured with a well-type scintillation counter 166. The coefficient for performing the cross calibration (cross calibration factor: CCF) is expressed by the following equation 1D.

  Expression 1C is expressed in units of 100 g of brain tissue, and the expression used when actually determining cerebral blood flow with various corrections and the like is expressed as the following expression 1E in the case of 5-minute continuous arterial blood sampling.

  Here, A ′ of the denominator is the count number (cpm) of the collected arterial blood 1.0 ml, (5R + D) is a term for dilution correction, and 5R is a blood collection speed R in the aspirator (syringe) by blood sampling for 5 minutes. D (Dead Space) is the amount of arterial blood sucked into the extension tube from the arm to the aspirator. A term for dilution correction was provided because arterial blood was in the aspirator and extension tube after 5 minutes from the start of collection. The total radioactivity A of arterial blood (R · t (ml)) obtained in t minutes with A ′ · (5R + D). N is the ratio of true IMP excluding the above-mentioned IMP metabolites in arterial blood, and is the octanol extraction rate. The octanol extraction rate N is considered to be about 80% (0.8) on average, although there are individual differences.

Although SPECT imaging is desired at the time point t = 5 minutes, the imaging is actually performed 30 minutes later for three-dimensional imaging. Therefore, the radioactivity Cb in the brain tissue at t = 5 minutes after IMP intravenous injection is adjusted as Cb = S ₃₀ · (H ₅ / H ₃₀ ). Here, S ₃₀ is a SPECTROI count (counts) at 30 minutes, H ₅ is a whole brain count (counts) at 5 minutes, and H ₃₀ is a whole brain count (counts) at 30 minutes. For _example, in the case of _{(H 5 / H 30) =} 1/2, it is adjusted to half the Cb _{= S 30/2.} Alternatively, it is possible to take an image from 10 minutes to 30 minutes and adjust Sb and S ₂₀ and (H ₅ / H ₂₀ ) using S ₂₀ and H ₂₀ when (10 + 30) / 2 = 20 minutes. Good. The Cb obtained as described above may be divided by the above-described cross calibration factor CCF. Since it was arranged in Equation 1E, the whole brain count H ₃₀ and the cross calibration factor CCF at 30 minutes appear in the denominator.

As an example of Formula 1E, R = 1.03 (ml / min), D = 0.5 (ml), A ′ = 53, 214 (cpm), S ₃₀ = 53.6 (counts), N = 0. 8. Substituting H ₅ = 41,750 (counts), H ₃₀ = 45,700 (counts), and CCF = 0.00051, F = 41.1 (ml / 100 g brain / min).

  As described above, in the IMP / MS method, it was necessary to perform continuous arterial blood sampling and measure the radioactivity of the collected arterial blood. In recent years, a one-point arterial blood sampling method (ARG (autoradiography) method) that is inferior in accuracy but simple is also used in cerebral blood flow calculation (see Non-Patent Document 1).

  In the MS method that requires continuous arterial blood sampling in the above-described cerebral blood flow SPECT, a continuous arterial blood sampling is required for 6 minutes in the examination of the subject patient in order to calculate cerebral blood flow. It was. Since the procedure is complicated in clinical settings such as hospital facilities, there is a problem that the burden on the staff is large. Although the ARG method is minimally invasive, there is a problem that accuracy in calculating cerebral blood flow is inferior.

  Accordingly, an object of the present invention has been made to solve the above-described problems, and is a minimally invasive method for calculating cerebral blood flow at rest or load by cerebral blood flow SPECT using a divided administration method. Another object of the present invention is to provide a cerebral blood flow calculation program or the like that can obtain highly accurate cerebral blood flow using a radioactivity count value obtained by a noninvasive and simple technique.

The cerebral blood flow calculation program according to the present invention is a cerebral blood flow calculation program for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. A brain radioactivity count value recording unit in which a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject during SPECT are recorded, and the subject is observed by continuous arterial blood sampling for a predetermined time in a microsphere method. A plurality of intracerebral radioactivity counts recorded in the intracerebral radioactivity count value recording unit. A multiple regression equation having multiple regression coefficients obtained by multiple regression analysis using the value as an explanatory variable and the radioactivity count value in arterial blood recorded in the arterial blood radioactivity count value recording unit as an objective variable is obtained. Regression equation acquisition means, 1st. Intracerebral radioactivity count value input means for inputting a plurality of intracerebral radioactivity count values measured every predetermined time for the subject subject in SPECT, input by the intracerebral radioactivity count value input means By applying the intracerebral radioactivity count value to the multiple regression equation obtained by the multiple regression equation acquisition means, the radioactivity count in arterial blood that can be observed by arterial blood sampling for a predetermined time in the microsphere method for the subject subject estimating means for estimating a value, brain and radioactivity count in arterial blood estimated by intracerebral radioactivity count value input by the brain radioactivity count value input means, said estimating means according IMP · microsphere model Law By applying to the blood flow calculation formula, it functions as a cerebral blood flow calculation means for calculating the cerebral blood flow for the subject subject. This is a cerebral blood flow calculation program.

  Here, in the cerebral blood flow rate calculation program of the present invention, as an explanatory variable of the multiple regression analysis in the multiple regression equation acquisition means, a sample obtained by one-point vein blood collection after a predetermined time after the first administration of a predetermined tracer Can be further used.

  Here, in the cerebral blood flow calculation program of the present invention, as an explanatory variable of the multiple regression analysis in the multiple regression equation acquisition means, a sample obtained by one-point arterial blood sampling after a predetermined time has elapsed after the first administration of a predetermined tracer Can be further used.

  Here, in the cerebral blood flow calculation program of the present invention, the 1st. The plurality of brain radioactivity count values measured at predetermined time intervals for subjects in SPECT are relative values based on the brain radioactivity count values measured at the second administration of the predetermined tracer. be able to.

  Here, the cerebral blood flow calculation program according to the present invention may further include a cerebral blood flow image display means for displaying a cerebral blood flow image based on the cerebral blood flow calculated by the cerebral blood flow calculation means.

  Here, in the cerebral blood flow rate calculation program according to the present invention, the collected image display means for displaying a collected image based on the intracerebral radioactivity count value input by the intracerebral radioactivity count value input means, and the collected image display means ROI setting means for setting a predetermined shape on the collected image displayed by the above and setting an ROI based on a predetermined threshold in the predetermined shape, and within the ROI set by the ROI setting means or in an arbitrary region A time / radioactivity curve display means for displaying a time / radioactivity curve based on a brain radioactivity count value input by the brain radioactivity count value input means, and a display by the time / radioactivity curve display means Sagittal section direction collected image display means for setting a predetermined range on the measured time / radioactivity curve and displaying a collected image in the sagittal section direction in the predetermined range The tomographic image after reconstruction is configured to set a predetermined range using the acquired image displayed by the acquired image display means in the sagittal section direction and display a tomographic image in the cross-sectional direction after image reconstruction in the predetermined range. Display means.

  The cerebral blood flow calculation program according to the present invention is a cerebral blood flow calculation program for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. In SPECT and 2nd. A brain radioactivity count value recording unit in which a radioactivity count value in a brain measured every predetermined time for a subject in SPECT is recorded, and a computer is used to record the radioactivity count value recording unit in the brain. Recorded in 2nd. The remaining radioactivity count value by the first intravenous injection in the brain radioactivity count value during SPECT is 1st. From the second intravenous injection to a predetermined elapsed time. The radioactivity count value obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times during SPECT is assumed, and after the predetermined elapsed time, a plateau is estimated at the radioactivity count value obtained by simple regression at the predetermined elapsed time. Residual radioactivity estimation means, 2nd. Recorded in the intracerebral radioactivity count value recording unit. The value obtained by subtracting the residual radioactivity count value estimated by the residual radioactivity estimation means from the intracerebral radioactivity count value in SPECT is 2nd. Estimated as the brain radioactivity count value by the second intravenous injection during SPECT. 2nd. SPECT brain radioactivity count value estimating means, 2nd. SPECT estimated by SPECT brain radioactivity count value estimation means 2nd. It is a cerebral blood flow calculation program for functioning as cerebral blood flow calculation means for calculating cerebral blood flow for a subject subject based on the intracerebral radioactivity count value by the second intravenous injection during SPECT.

  The recording medium of the present invention is a computer-readable recording medium on which any cerebral blood flow calculation program of the present invention is recorded.

The cerebral blood flow calculation method of the present invention is a cerebral blood flow calculation method for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. A brain radioactivity count value recording unit in which a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject during SPECT are recorded, and the subject is observed by continuous arterial blood sampling for a predetermined time in a microsphere method. The arterial blood radioactivity count value recording unit in which the arterial radioactivity count value is recorded is described, and a plurality of intracerebral radioactivity count values recorded in the intracerebral radioactivity count value recording unit are described. Multiple regression equation acquisition for obtaining a multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using the radioactivity count value in the arterial blood recorded in the arterial blood radioactivity count value recording unit as a variable Step, 1st. In the brain radioactivity count value input step for inputting a plurality of intracerebral radioactivity count values measured every predetermined time for a subject subject during SPECT, and input in the intracerebral radioactivity count value input step The radioactivity in arterial blood that can be observed by continuous arterial blood sampling for a predetermined time in the microsphere method for the subject subject by applying the brain radioactivity count value to the multiple regression equation acquired in the multiple regression equation acquisition step an estimation step of estimating a count value, IMP · microsphere model method and radioactivity counting value of the brain radioactivity count input arterial blood estimated by said estimating step with the inputted brain radioactivity count value in step Is applied to the cerebral blood flow calculation formula according to It is characterized by having a top.

  Here, in the method for calculating cerebral blood flow according to the present invention, as an explanatory variable of the multiple regression analysis in the multiple regression equation obtaining step, a sample obtained by one-point vein blood collection after a predetermined time has elapsed after the first administration of a predetermined tracer Can be further used.

  Here, in the method for calculating cerebral blood flow according to the present invention, as an explanatory variable of the multiple regression analysis in the multiple regression equation acquisition step, a sample obtained by one-point arterial blood sampling after a predetermined time has elapsed after the first administration of a predetermined tracer Can be further used.

  Here, in the method for calculating cerebral blood flow according to the present invention, the 1st. The plurality of brain radioactivity count values measured at predetermined time intervals for subjects in SPECT are relative values based on the brain radioactivity count values measured at the second administration of the predetermined tracer. be able to.

  Here, the cerebral blood flow calculation method of the present invention may further include a cerebral blood flow image display step for displaying a cerebral blood flow image based on the cerebral blood flow calculated in the cerebral blood flow calculation step.

  Here, in the cerebral blood flow rate calculation method according to the present invention, after the intracerebral radioactivity count value input step, a collected image based on the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step is displayed. A collected image display step, a ROI setting step for setting a predetermined shape on the collected image displayed in the collected image display step, and setting an ROI based on a predetermined threshold in the predetermined shape, and the ROI setting Time / radioactivity curve display step for displaying a time / radioactivity curve based on the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step within the ROI set in step or in an arbitrary region And a predetermined range is set on the time / radioactivity curve displayed in the time / radioactivity curve display step, and the sagittal cross-section direction in the predetermined range is set. A sagittal cross-sectional direction acquired image display step for displaying the acquired image, and a predetermined range is set using the acquired image displayed in the sagittal cross-sectional direction acquired image display step, and after image reconstruction in the predetermined range And a post-reconstructed tomographic image display step for displaying a tomographic image in the cross-sectional direction.

  The cerebral blood flow calculation method of the present invention is a cerebral blood flow calculation method for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. In SPECT and 2nd. Using a brain radioactivity count value recording unit in which a brain radioactivity count value measured every predetermined time for a subject in SPECT is recorded, and recorded in the brain radioactivity count value recording unit 2nd. The remaining radioactivity count value by the first intravenous injection in the brain radioactivity count value during SPECT is 1st. From the second intravenous injection to a predetermined elapsed time. The radioactivity count value obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times during SPECT is assumed, and after the predetermined elapsed time, a plateau is estimated at the radioactivity count value obtained by simple regression at the predetermined elapsed time. Residual radioactivity estimation step, 2nd. Recorded in the brain radioactivity count value recording unit. A value obtained by subtracting the residual radioactivity count value estimated in the residual radioactivity estimation step from the intracerebral radioactivity count value in SPECT is 2nd. Estimated as the brain radioactivity count value by the second intravenous injection during SPECT. 2nd. SPECT brain radioactivity count value estimation step, 2nd. 2nd. Estimated in the SPECT brain radioactivity count estimation step. And a cerebral blood flow calculating step for calculating a cerebral blood flow for the subject subject based on the intracerebral radioactivity count value of the second intravenous injection during SPECT.

According to the cerebral blood flow calculation program or the like of the present invention, the multiple regression equation acquisition unit obtains a sample obtained by one-point vein blood collection after 55 minutes after the first administration of the drug ( ¹²³ I-IMP (111MBq)). Radioactivity count value in arterial blood recorded in arterial blood radioactivity count value recording section with radioactivity count value and multiple brain radioactivity count values recorded in brain radioactivity count value recording section as explanatory variables To obtain a multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using as a target variable. The brain radioactivity count value input unit is 1st. A plurality of brain radioactivity count values measured every predetermined time for a subject subject in a predetermined period during SPECT are input. The estimation unit obtains the multiple regression equations (intercepts b ₀ and b ₁ to b) obtained by the multiple regression equation obtaining unit for the subject's brain radioactivity count value input by the brain radioactivity count value input unit. Arterial blood that can be observed by continuous arterial blood sampling for a predetermined time in the MS method for the subject subject, preferably 6 minutes (may be 5 minutes, and so on) by applying to the multiple regression equation having a regression coefficient of ₁₀ ) Estimate the radioactivity count value in it. For comparison, the inventors have also performed 6-minute continuous arterial blood sampling in the MS method together with single-point vein blood sampling, and measured the radioactivity count value in arterial blood. Distribution of radioactivity count values in arterial blood estimated (predicted) for a plurality of subject subjects, and radioactivity count values in arterial blood actually observed by continuous arterial blood sampling for 6 minutes for a plurality of subject subjects Since it was found to correlate well with the distribution, it has the effect of being suitable for clinical application. The cerebral blood flow rate calculation unit calculates the brain for the subject subject based on the radioactivity count value in the brain input by the intracerebral radioactivity count value input unit and the radioactivity count value in arterial blood estimated by the estimation unit. Calculate blood flow. As described above, the used patient factors are the intracerebral SPECT change amount and the radioactivity count of single-point venous blood sampling at 55 minutes after administration of ¹²³ I-IMP, and the test can be completed within one hour as a whole. For this reason, when calculating the cerebral blood flow volume by cerebral blood flow SPECT using the divided administration method, the radioactivity count value obtained by a minimally invasive and simple technique using single-point vein blood collection is used to obtain high accuracy. There is an effect that it is possible to provide a cerebral blood flow calculation program or the like that can determine the cerebral blood flow.

Furthermore, according to the cerebral blood flow calculation program or the like of the present invention, the multiple regression equation acquisition unit was obtained by one-point arterial blood sampling after 8 minutes from the first administration of the drug ( ¹²³ I-IMP (111 MBq)). Radioactivity in the arterial blood recorded in the arterial blood radioactivity count value recording section, with the sample radioactivity count value and the multiple brain radioactivity count values recorded in the brain radioactivity count value recording section as explanatory variables A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using the count value as an objective variable is acquired. In this case as well, each function of the cerebral blood flow rate calculation program can be used in substantially the same manner as in the case of the one-point vein blood collection. The radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit, and the 6-minute continuous arterial blood sampling in the MS method for the same subject. According to the comparison with the radioactivity count value in arterial blood observed by the above, there is an effect that the distribution of both is well correlated, so that it is suitable for clinical application. For this reason, when calculating the cerebral blood flow volume by cerebral blood flow SPECT using the divided administration method, the radioactivity count value obtained by a minimally invasive and simple technique using one-point arterial blood sampling is used. There is an effect that it is possible to provide a cerebral blood flow calculation program or the like that can determine the cerebral blood flow.

In addition, according to the cerebral blood flow calculation program or the like of the present invention, the multiple regression equation acquisition unit records in the cerebral radioactivity count value recording unit after the first administration of the drug ( ¹²³ I-IMP (111 MBq)). The partial regression coefficients obtained by multiple regression analysis using the measured radioactivity count values in the brain as explanatory variables and the radioactivity count values in arterial blood recorded in the arterial blood radioactivity count value recording unit as objective variables Obtain the multiple regression equation. In this case as well, each function of the cerebral blood flow rate calculation program can be used in substantially the same manner as in the case of the single-point vein blood collection and single-point arterial blood collection. The radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit, and the same subject actually by 6-minute continuous arterial blood sampling in the MS method According to the comparison with the observed radioactivity count value in arterial blood, it has been found that the distribution of both is well correlated, so that it is effective for clinical application. For this reason, when calculating the cerebral blood flow by cerebral blood flow SPECT using the divided administration method, a highly accurate brain is obtained by using the radioactivity count value obtained by a noninvasive and simple method of non-blood collection. There is an effect that it is possible to provide a cerebral blood flow calculation program or the like that can determine the blood flow.

  As described above, according to the cerebral blood flow calculation program and the like of the present invention, when calculating the cerebral blood flow by cerebral blood flow SPECT using the divided administration method, it is obtained by a minimally invasive or non-invasive and simple method. There is an effect that it is possible to provide a cerebral blood flow rate calculation program or the like that can obtain a highly accurate cerebral blood flow rate by using the obtained radioactivity count value.

It is a figure which shows the protocol of the division administration method in Example 1 of this invention using ¹²³ I-IMP cerebral blood flow SPECT. It is a figure which shows the functional block diagram 20 etc. of the cerebral blood flow rate calculation program in Example 1 of this invention. It is a figure which illustrates the several brain radioactivity count value measured for every predetermined time progress about the test subject in 1st.SPECT recorded in the brain radioactivity count value recording part 12. FIG. It is a figure which shows the regression coefficient obtained by performing the multiple regression analysis in Example 1. FIG. The radioactivity count value in the arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit 23 in Example 1, and the subject actually in the MS method 6 It is a figure which shows the comparison (correlation) with the radioactivity count value in arterial blood observed by continuous arterial blood sampling for 1 minute. It is a figure which shows the result of having performed the multiple regression analysis about 78 cases. It is a figure which shows the scatter diagram matrix which shows the correlation with the radioactivity count value and prediction value which were used by multiple regression analysis. It is a figure which shows the comparison with the radioactivity count value (prediction value) in the arterial blood estimated by the estimation part 23, and an observation value. It is a figure which shows distribution of the difference (residual) of the vertical axis | shaft (observed value) of FIG. 8, and a horizontal axis (predicted value). It is a figure which shows distribution of the observed value shown by FIG. It is a figure which shows the result of having examined the multiple regression analysis about 78 cases. It is a figure which shows the result of having examined the multiple regression analysis about 78 cases which took out five explanatory variables with a small P value in FIG. Shows a scatterplot matrix showing the correlation of Fig. 12 at a selected explanatory variables _{(x 1, x 4, x} 8, x 9 and x ₁₀₎ and radioactivity count value used in the multiple regression analysis using the predicted value FIG. The radioactivity count value (predicted value) in the arterial blood estimated (predicted) by the estimating unit 23 using the explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) selected in FIG. It is a figure which shows the comparison with an observed value. It is a figure which shows distribution of the difference (residual) of the vertical axis | shaft (observed value) of FIG. 14, and a horizontal axis (predicted value). It is a figure which shows distribution of the observed value shown by FIG. It is a figure which shows distribution of the predicted value shown by FIG. It is a figure which shows the protocol of the divided administration method in Example 2 of this invention using ¹²³ I-IMP cerebral blood flow SPECT. It is a figure which shows the regression coefficient obtained by performing the multiple regression analysis in Example 2. FIG. Radioactivity count value in the arterial blood (horizontal axis) that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit 23 in Example 2, and the subject actually It is a figure which shows the comparison (correlation) with the radioactivity count value (vertical axis) in arterial blood observed by 6-minute continuous arterial blood sampling in MS method. It is a figure which shows the protocol of the divided administration method in Example 3 of this invention using ¹²³ I-IMP cerebral blood-flow SPECT. The radioactivity count value (vertical axis) in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit 23 in Example 3, and the subject is actually It is a figure which shows the comparison (correlation) with the radioactivity count value (horizontal axis) in arterial blood observed by 6-minute continuous arterial blood sampling in MS method. It is a flowchart which shows operation | movement of an example of a cerebral blood flow rate calculation program and a cerebral blood flow rate calculation method. It is a figure which shows the display image at the time of selecting the data of the test subject used as object. It is a figure which shows the display image immediately after starting the cerebral blood flow rate calculation program. It is a figure which shows the display image of the state which has set ROI. It is a figure which shows the display image of the state which has set the threshold value (threshold) ROI. It is a figure which shows the display image of a time and a radioactivity curve. It is a figure which shows the display image which expanded a part of FIG. It is a figure which shows the display image of the state which displayed the collection image. It is a figure which shows the display image of the state which displayed the tomogram. It is a figure which shows the display image which displays a cerebral blood flow image. It is a block diagram which shows the internal circuit 140 of the computer 1 which performs the program of this invention. It is a figure which shows the problem of the protocol of the conventional divided administration method using ¹²³ I-IMP cerebral blood-flow SPECT. It is a figure which shows the estimation method of the residual radioactivity used in Example 6 of this invention in the protocol of the division | segmentation administration method using ¹²³ I-IMP cerebral blood flow SPECT. It is a flowchart which shows the flow of a process of the cerebral blood flow rate calculation program and method in Example 6 of this invention. It is a figure which shows the display image which displays the cerebral blood flow rate image in Example 6 of this invention. It is a figure which shows the protocol of the conventional divided administration method using ¹²³ I-IMP cerebral blood-flow SPECT. It is a figure for demonstrating the measurement of cross calibration.

  The outline of the cerebral blood flow rate calculation program of the present invention is as follows. First, the radioactivity count value in arterial blood measured by continuous arterial blood sampling for a predetermined time, preferably 6 minutes (5 minutes may be used) for the subject by the MS method, and 1st. A plurality of intracerebral radioactivity count values measured at predetermined time intervals during SPECT are measured. These data are accumulated in advance for a plurality of subjects. The cerebral blood flow rate calculation program of the present invention obtains a multiple regression equation by performing multiple regression analysis based on these accumulated data. Next, for the subject who is the target of cerebral blood flow calculation, 1st. A plurality of intracerebral radioactivity count values measured at predetermined time intervals during SPECT, and a minimally invasive technique using one-point vein blood sampling, a non-invasive technique using one-point arterial blood sampling, or a non-invasive technique using non-blood sampling By applying the radioactivity count values obtained by various techniques to the multiple regression equations, the radioactivity count values in arterial blood that can be measured by 6-minute continuous arterial blood sampling for the subject by the MS method are predicted. A cerebral blood flow is calculated based on the predicted radioactivity count value in the arterial blood and the plurality of brain radioactivity count values measured as described above. That is, the cerebral blood flow calculation program of the present invention is characterized in that when calculating the cerebral blood flow by cerebral blood flow SPECT using the divided administration method, invasive 6-minute continuous arterial blood sampling is not performed, but instead a single point vein Based on the radioactivity count value obtained by non-invasive technique by blood sampling, minimally invasive technique by single-point arterial blood sampling or non-invasive technique by non-blood sampling, continuous arterial blood sampling was performed for 6 minutes by multiple regression equation In this case, the radioactivity count value in the arterial blood is predicted, and the cerebral blood flow volume at rest or load (Example 6 is loaded) is calculated based on the prediction. Hereinafter, each embodiment will be described in detail with reference to the drawings.

In the first embodiment, a case in which the cerebral blood flow is calculated using a radioactivity count value obtained by a minimally invasive technique using one-point vein blood collection will be described. 1 (A) and 1 (B) show the protocol of the divided administration method in Example 1 of the present invention using ¹²³ I-IMP cerebral blood flow SPECT. As shown in FIG. 1 (A), first, a first intravenous injection of ¹²³ I-IMP (111 MBq) is performed. Hereinafter, the time of the first intravenous injection is defined as time 0, and the time elapsed from time 0 will be used for description. First, dynamic SPECT (1st. SPECT shown in FIG. 1B) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 1B) is performed. In FIG. 2 (B), as in FIG. 30 (B), the residual radioactivity (background) due to the first ¹²³ I-IMP intravenous injection at this time is indicated by a dotted line. After 55 minutes, single-point vein blood collection is performed from the hand opposite to the administration of the drug ( ¹²³ I-IMP (111MBq)) (for example, the left hand when administered from the right hand). The radioactivity count value is measured from the collected venous blood. As described above, the patient factors used are the amount of change in brain SPECT and the radioactivity count of single-point venous blood sampling at the time point of 55 minutes after ¹²³ I-IMP administration, and the test is completed within 1 hour as a whole. That is, the inspection can be performed with a minimally invasive and simple technique.

FIG. 2 shows a functional block diagram 20 of the cerebral blood flow calculation program of the present invention. In FIG. 1, reference numeral 1 is a computer that executes the cerebral blood flow calculation program of the present invention, 2 is a display device such as a display connected to the computer 1, and 10 is a recording device such as a disk connected to the computer 1. The recording device 10 records various data for obtaining a multiple regression equation used in the present invention. Reference numeral 12 denotes a brain radioactivity count value recording unit which is recorded in the recording device 10 and records a plurality of brain radioactivity count values measured at predetermined time intervals for a subject in 1st.SPECT. Reference numeral 14 denotes the arterial blood recorded in the recording device 10 and the radioactivity count value in the arterial blood observed by continuous arterial blood sampling for 6 minutes from the first ¹²³ I-IMP intravenous injection in the MS method for the subject. It is a medium radioactivity count value recording part. In order to obtain a multiple regression equation used in the cerebral blood flow rate calculation program of the present invention, a continuous arterial blood collection result of 6 minutes from the above-described first ¹²³ I-IMP intravenous injection is used. If obtained, the measurement result by continuous blood sampling for 6 minutes is not necessary when executing the cerebral blood flow rate calculation program of the present invention as shown in the protocol of FIG.

Next, imaging conditions will be described. A three-detector SPECT apparatus (Toshiba (registered trademark) GCA9300A / GMS5500-DI) was used as an imager, and a fan beam collimator was used. The distance from each fan beam collimator to the rotation center was fixed at 132 mm (minimum value). For the intra-brain SPECT change amount, 9 frames, which are frames that the imager deserves in front of the brain, were used. Matrix was 64 × 64, 6 ° × 20 × 2/2 × 20 × 3 (= 6 ° × 1200) was continuously collected and reconstructed to 4 ° × 1800. 120 ° / 75sec × 2
addition (120 ° round trip / 2'30 ") (In fact, there is a time lag of 120 ± 3 ° and reversal, and it takes 1 round trip / about 2'43"). The drug used (predetermined tracer) is ¹²³ I-IMP (111 MBq) × 2 Diamox (registered trademark) 1000 mg. The subjects of imaging are cerebrovascular disorders and the like, and there are a total of 78 cases (including pre-operative and post-operative follow-up cases) in which cerebral blood flow quantitative examination was performed by administration of ¹²³ I-IMP. Specifically, 55 males (aged 33 to 83 years, average 63.8 years (33-83 yom: 63.8yo)), 23 females (aged 36 to 84 years old, average 62. 0-year-old (36-84 yom: 62.0 yo) The imaging results were recorded in the brain radioactivity count value recording section 6. Continuous arterial blood sampling for 6 minutes to obtain a multiple regression equation Was performed on all cases and recorded in the arterial blood radioactivity count recording unit 14. One point venous blood sampling after 55 minutes for prediction was performed on 78 cases, and the radioactivity count of each sample was performed. The values are appropriately recorded in the recording apparatus 10. These numbers are merely examples, and are not limited to these numbers when the cerebral blood flow rate calculation program of the present invention is applied.

FIG. 3 illustrates a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject in 1st. SPECT recorded in the intracerebral radioactivity count value recording unit 12. In FIG. 3, the vertical axis is the brain radioactivity count value, and the horizontal axis is the frame, and the imaging results of the nine frames are shown. Specifically, the 9 frames are 1.35 minutes, 4.05 minutes, 6.75 minutes, 9.45 minutes, 12.15 minutes after the first ¹²³ I-IMP intravenous injection, It is the radioactivity count value in the brain after 14.85 minutes, after 17.55 minutes, after 20.25 minutes, and after 22.95 minutes (every predetermined period of time in 1st. SPECT). Although it is not clear because it is displayed in black and white in FIG. 3, the radioactivity count value in the brain is displayed in different colors for each of a plurality of subjects in the original drawing.

Returning to FIG. 2, the multiple regression equation acquisition unit (multiple regression equation acquisition means) 21 shown in the functional block diagram 20 of the cerebral blood flow calculation program of the present invention is the drug ( ¹²³⁾ described for the protocol shown in FIG. 1. I-IMP (111MBq)) is recorded in the radioactivity count value and brain radioactivity count value recording unit 12 of the sample obtained by single point venous blood sampling after 55 minutes (after a predetermined time) after the first administration. The partial regression obtained by multiple regression analysis using the plurality of intracerebral radioactivity count values as explanatory variables and the radioactivity count values in arterial blood recorded in the arterial blood radioactivity count recording unit 14 as objective variables Obtain a multiple regression equation with coefficients. In general, multiple regression equation objective variable y, explanatory variable x, when the b _i and the regression coefficient (b ₀ Sections), is given by the following multiple regression equation 2.

Here, for example, the multiple regression equation acquisition unit 21 sets the explanatory variable x ₁ as the radioactivity count value of the sample obtained by one-point blood collection after 55 minutes, and sets the explanatory variable x _i (i = 2 to k) in the brain. A plurality of (for example, the above nine frames, where k = 10) brain radioactivity count values recorded in the radioactivity count value recording unit 12 are used, and the objective variable y is recorded in the arterial blood radioactivity count value recording unit 14. As the radioactivity count value in the arterial blood, multiple regression analysis is performed on the data recorded in the intracerebral radioactivity count value recording unit 12 and the arterial blood radioactivity count value recording unit 14. FIG. 4 shows the regression coefficient obtained by conducting the multiple regression analysis in Example 1. As shown in FIG. 4, the regression coefficients _{b 0} (intercept) is 524.2023, explanatory variables _x 1 regression coefficient _{b 1} corresponding to the (one point venous blood after the lapse of 55 minutes) is 1.496654, the explanatory variable _{x 2} Regression coefficient b ₁ corresponding to (cerebral radioactivity count value after 1.35 minutes) is −3.05188,..., Explanatory variable x ₁₀ (cerebral radioactivity count value after 22.95 minutes have elapsed) The regression coefficient b ₁₀ corresponding to) was obtained as 5.692109.

  Returning to FIG. 2, the intracerebral radioactivity count value input unit (intracerebral radioactivity count value input means) 22 shown in the functional block diagram 20 of the cerebral blood flow rate calculation program of the present invention is 1st. A plurality of brain radioactivity count values measured every predetermined time for a subject subject in a predetermined period during SPECT are input. Here, the subject test subject does not perform invasive 6-minute continuous arterial blood collection, but instead uses a radioactivity count value obtained by a minimally invasive technique using single-point venous blood collection after 55 minutes. This is the meaning of the subject who calculates the blood flow. What is necessary is just to be for every elapsed time in nine frames mentioned above, for example for every predetermined time progress.

Subsequently, the estimation unit (estimation means) 23 shown in the functional block diagram 20 of the cerebral blood flow rate calculation program of the present invention is the brain in the subject of the subject inputted by the intracerebral radioactivity count value input unit 22. By applying the radioactivity count value to the multiple regression equation 2 (multiple regression equation 2 having regression coefficients of the above-described intercepts b ₀ and b _{1 to} b ₁₀ ) acquired by the multiple regression equation acquisition unit 21, the subject subject Estimate the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method. 5A and 5B show the radioactivity count values in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimating unit 23 in Example 1. The comparison (correlation) with the radioactivity count value in arterial blood actually observed by continuous blood sampling for 6 minutes in the MS method for the subject is shown. For comparison, the inventors have also performed 6-minute continuous arterial blood sampling in the MS method together with single-point vein blood sampling, and measured the radioactivity count value in arterial blood. In FIG. 5 (A), the horizontal axis is the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject who is estimated (predicted), and the vertical axis is the subject. It is a radioactivity count value in arterial blood actually observed by continuous arterial blood sampling for 6 minutes in the MS method. In FIG. 5A, a square (red square in the original drawing) is a distribution of radioactivity count values in arterial blood estimated (predicted) for a plurality of subjects, and a diamond (purple diamond in the original drawing) is a target. The distribution of radioactivity count values in arterial blood actually observed by continuous arterial blood sampling for 6 minutes for a plurality of subjects. As shown in FIG. 5A, it can be seen that the distributions of both are well correlated. As shown in the regression statistics of FIG. 5B, for example, multiple correlation coefficient R = 0.828219, and both have a very good correlation. Therefore, the estimation by the estimation unit 23 using the multiple regression equation 2 is It turns out that it is suitable for clinical application. That is, the multiple regression equation 2 having the above regression coefficient obtained by the multiple regression equation acquisition unit 21 sufficiently estimates (predicts) the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method. Is something that can be done. Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation 2 is not just a selection example but an essential selection that has made the above estimation (prediction) remarkable. In FIG. 5B, the number of observations is 64, which is different from the number of cases described above = 78. This is because the number of observations was arbitrarily extracted (the same applies hereinafter).

  Subsequently, the cerebral blood flow rate calculation unit (cerebral blood flow rate calculation means) 24 shown in the functional block diagram 20 of the cerebral blood flow rate calculation program according to the present invention is input to the intracerebral radioactivity count value input unit 22. Based on the radioactivity count value and the radioactivity count value in the arterial blood estimated by the estimation unit 23, the cerebral blood flow rate for the subject subject is calculated. The formula 1E described in the background art is reprinted.

Here, as explained in the background art, A ′ · (5R + D) is the total radioactivity A of arterial blood (R · t (ml)) obtained in t minutes, that is, radiation in arterial blood when continuous arterial blood is collected. It becomes the performance count value. Therefore, the radioactivity count value in the arterial blood estimated by the estimation unit 23 may be used for the term. Since S ₃₀ · (H ₅ / H ₃₀ ) is the radioactivity Cb in the brain tissue t = 5 minutes after the IMP intravenous injection, the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 is Use it. The blood collection rate R = 1.03 (ml / min), the octanol extraction rate N = 0.8, and the cross calibration factor CCF = 0.00051 may be the values of the background art as they are, or may be reset as appropriate. Good. As described above, the cerebral blood flow rate calculation unit 24 can calculate the cerebral blood flow rate F for the subject subject.

In the above description, 1st. Actual measured values were used as the plurality of brain radioactivity count values measured at predetermined time intervals for the subjects in SPECT. However, a relative value based on the brain radioactivity count value measured at the second administration of the drug can also be used. For example, the 1st. Actual values in SPECT are 3000, 4000, 5000, etc., 2nd. First time brain radioactivity count (Fig. 1 (during the first round of ¹²³ I-IMP IV from second round of after 24.5 minutes ¹²³ I-IMP IV shown in A)) of the SPECT If the value is 10,000, the above 3000, 4000, 5000, etc. may be set to relative values of 0.3, 0.4, 0.5, etc., with 10000 as a reference (for example, 1). Actually, the sensitivity of the image pickup device varies from hospital facility to hospital facility. Therefore, the robustness (robustness) of the cerebral blood flow rate calculation program may be higher when such a relative value (proportional value) is used.

FIG. 6 shows the results of multiple regression analysis for 78 cases. As shown in the upper left table of FIG. 6, the multiple correlation R represents a multiple correlation coefficient, and R = 0.9304062, indicating that the multiple regression equation 2 is very well applied. Multiple determination R2 is the coefficient of determination ^{R 2,} since it is R 2 = .872472, of variations in the measured value, it can be seen that about 87% can be explained by the multiple regression equation 2. The correction R2 represents a determination coefficient having a degree of freedom corrected, and this determination coefficient is 0.853438. Therefore, when the influence of the explanatory variable is discounted, about 85% of the fluctuation of the actual measurement value is the multiple regression equation 2. It can be seen that can be explained. The standard error represents the standard error of the so-called Y evaluation value indicating the standard deviation of the difference (residual) between the observed value and the predicted value, and the standard error is 3093.073. Number of observations = 78. In the table below in FIG. 6, "slices" of the first column is the value of the multiple regression equation 2 explanatory variables _{x 2} is 81.37 under "veins", is 1383.28 from below 244.11 each It is a brain radioactivity count value of explanatory variables x _i (i = 3 to 10) (9 frames from x _{2 to} x ₁₀ ). The second column is a regression coefficient of the multiple regression equation 2, and intercepts b = −2717.49, b ₁ = 1.739875, b ₂ = 0.261201,. . . , B ₁₀ = 0.734727. The third column is the standard error that is the estimation error of the regression coefficient of the second column, the fourth column is the t value that is the t-test of the (partial) regression coefficient of the multiple regression equation 2, and the fifth column is the t value of the fourth column The P-values of the two-sided probabilities obtained from the t distribution and the sixth and seventh columns show the lower and upper limits of the 95% confidence limit of the regression coefficients in the second column. The eighth and ninth columns are the same as the sixth and seventh columns, respectively.

  FIG. 7 (A) is a scatter diagram matrix showing the correlation between the radioactivity count value and the predicted value used in the multiple regression analysis, and FIG. 7 (B) enlarges a part of FIG. 7 (A) for explanation. Shows the scatter plot matrix. In the scatter diagram matrix shown in FIG. 7 (A), the horizontal axis of the graph shown in the (1, 1) element is the radioactivity count value of the sample obtained by one-point vein blood collection after 55 minutes for each subject, The vertical axis represents the number of people (frequency) that became the radioactivity count value. As shown in the enlarged graph of the (1,1) element in FIG. 7B, it can be seen that the frequency distribution is almost normal.

  In the scatter diagram matrix shown in FIG. 7A, in each graph shown in each diagonal element from the (2, 2) element to the (10, 10) element, the horizontal axis is measured for each subject in the order of the above elements. 1st. The brain radioactivity count values from the first frame to the ninth frame during SPECT, and the vertical axis represents the number of people (frequency). For example, as shown in the expanded (2,2) element graph of FIG. 7B, the element is 1.35 minutes (81.37 s) after the first administration of the drug. The brain radioactivity count value (horizontal axis) and the number of people (vertical axis) in the frame) are shown. As shown in the graph of each diagonal element from (2, 2) elements to (10, 10) elements, it can be seen that the frequency distribution is almost normal.

In the scatter diagram matrix shown in FIG. 7A, the horizontal axis of the graph shown in the (11, 11) element is a continuous artery for 6 minutes from the first ¹²³ I-IMP intravenous injection measured in advance for each subject. It is the radioactivity count value of the collected blood sample, and the vertical axis is the number of people (frequency) that became the radioactivity count value. It can be seen that the frequency distribution is almost normal.

In scatterplot matrix shown in FIG. 7 (A), (1,2) graph radioactive count of the samples obtained by the one-dot vein bled after 55 minutes for each subject represented in element (explanatory variables x ₁ Value (vertical axis) and 1st. The correlation with the brain radioactivity count value (horizontal axis) in the first frame during SPECT is shown. See the enlarged (1,2) element in FIG. 7 (B). Similarly, the graphs shown from the (1,3) element to the (1,10) element show the radioactivity count values of the samples obtained by single point blood sampling after 55 minutes measured for each subject in the order of the above elements. (Vertical axis, common to each element) and 1st. The correlation with the brain radioactivity count value (horizontal axis of each element) from the second frame to the ninth frame during SPECT is shown. The graph shown in the last (1,11) element shows the radioactivity count value (vertical axis) of the sample obtained by one-point venous blood collection after 55 minutes measured for each subject and the first measured in advance for each subject. The correlation with the radioactivity count value (horizontal axis) of the 6-minute continuous arterial blood collection from the ^123rd I-IMP intravenous injection is shown. Both show good correlation.

In the scatter diagram matrix shown in FIG. 7A, the graphs shown from the (2,3) element to the (2,10) element are in the order of the above elements, after the first administration of the drug measured for each subject. The brain radioactivity count value (vertical axis, common to each element) in the 1.35 minute (81.37 s) elapsed frame (first frame) and the 1st. The correlation with the brain radioactivity count value (horizontal axis of each element) from the second frame to the ninth frame during SPECT is shown. The graph shown in the last (2,11) element is the radioactivity count value in the brain in the frame (first frame) at 1.35 minutes (81.37 s) after the first administration of the drug measured for each subject. (Vertical axis) shows the correlation between the radioactivity count value (horizontal axis) of a sample collected continuously for 6 minutes from the first intravenous injection of ¹²³ I-IMP measured in advance for each subject. Both show good correlation. Since the (3, 4) element to the (3, 11) element, the (4, 5) element to the (4, 11) element, etc. in the scatter diagram matrix shown in FIG. Description is omitted. Since the lower triangular element of the scatter diagram matrix shown in FIG. 7A is displayed with the vertical axis and horizontal axis in the upper triangular element replaced, the description thereof is omitted.

  FIG. 8 shows a comparison between the radioactivity count value (predicted value) in arterial blood estimated (predicted) by the estimating unit 23 and the observed value. In FIG. 8, the horizontal axis represents the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated by the estimation unit 23 using the multiple regression equation 2, and the vertical axis represents the same. It is a radioactivity count value (observed value) in arterial blood actually observed by 6-minute continuous arterial blood sampling in the MS method for a subject. As shown in FIG. 8, the both show a very good correlation. Therefore, the multiple regression equation 2 having the above regression coefficient obtained by the multiple regression equation acquisition unit 21 estimates the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method (prediction). ) For this reason, it is considered that the 6-minute continuous arterial blood sampling in the MS method with invasiveness can be omitted.

  FIG. 9 shows a distribution of differences (residuals) between the vertical axis (observed values) and the horizontal axis (predicted values) in FIG. In FIG. 9, the horizontal axis represents the residual radioactivity count value, and the vertical axis represents the frequency. For reference, the normal distribution is shown superimposed (red curve in the original figure). It can be seen that, except for some frequencies, the distribution is close to a normal distribution with an average of zero residuals.

  FIG. 10 shows the distribution of observed values shown in FIG. In FIG. 10, the horizontal axis represents the radioactivity count value in arterial blood observed by 6-minute continuous arterial blood sampling in the MS method for the subject, and the vertical axis represents the number of subjects (frequency) that became the radioactivity count value. . As shown in FIG. 9, it can be seen that the distribution of observed values is almost normal.

FIG. 11 shows the result of examination of multiple regression analysis for 78 cases. In the table on the upper right of FIG. 11, the number of data (number of observations), multiple correlation coefficient R, determination coefficient R ² , standard error (standard error of Y evaluation value) and degree of freedom corrected determination coefficient are shown in the upper left of FIG. Since it is the same as a table | surface, description is abbreviate | omitted. In addition to these, the table at the upper right of FIG. 11 shows the Durbin-Watson ratio. Since the Durbin-Watson ratio is close to 2, it can be said that there is no correlation when the residual is viewed as a time series, and it is random. In the lower table of FIG. 11 (test of regression coefficient significance and confidence interval), the first column is the same as the first column of FIG. 6, and “81” below “constant term” and “vein” in the first column. .37 is the value of the explanatory variable _{x 2} multiple regression equation 2, below 244.11 from 1,383.28 are each brain radioactivity count value of the explanatory variable _{x i (i = 3~10) (} x 2 ~ 9 for ₁₀ frames). Similarly, the second column is the (partial) regression coefficient of the multiple regression equation 2 as in the second column of FIG. 6, and the third column is a standard error that is an estimation error of the (partial) regression coefficient of the second column. The fourth column is the standard regression coefficient when the data is standardized and analyzed, the fifth column is the t value which is a t-test of the (partial) regression coefficient of the multiple regression equation 2, and the sixth column is the t column in the fifth column. The P value of the two-sided probability obtained from the value and the t distribution, the seventh column is t (0.975) which is the t value when the risk rate is 5% by the two-sided test, the eighth column and the ninth column are The lower limit value and the upper limit value of the 95% confidence limit of two columns of (partial) regression coefficients are shown. Although FIG. 11 is monochrome, it cannot be determined, but the P value (probability that the corresponding explanatory variable is 0) is small (the explanatory variable in the first column is vein (x ₁ ), 406.85 (x ₄ ). ), Which are 1057.8 (x ₈ ), 1220.54 (x ₉ ) and 1383.28 (x ₁₀ )) are displayed in red.

FIG. 12 shows five explanatory variables (x ₁ , x ₄ , x ₈ , x _9, and x ₁₀ ) having a small P value in FIG. 11, in other words, having a high probability of affecting the predicted value. The result of having examined the multiple regression analysis about 78 cases is shown. As shown in the upper left table of FIG. 12, the number of data is 78, and the multiple correlation coefficient R = 0.924422, which is slightly lower than the multiple correlation coefficient R of FIG. It turns out that there is not much change in the goodness of. The coefficient of determination R ² = 0.854555, which is slightly lower than the coefficient of determination R ^{2 of} FIG. 11. However, it can be understood that about 85% of the fluctuation of the actual measurement value can be explained by the multiple regression equation 2. The degree-of-freedom-corrected determination coefficient = 0.844455, which is slightly lower than the degree-of-freedom-corrected determination coefficient in FIG. 11, but when considering the influence of explanatory variables, about 85% It can be seen that this can be explained by regression equation 2. The standard error was 3186.457, which was slightly larger than the standard error of FIG. Although the Durbin-Watson ratio is slightly larger than 2 from the examination result of FIG. 11 but is close to 2, there is no correlation when the residual is viewed as a time series, and it can be said that it is random.

  The middle table of FIG. 12 shows an analysis of variance table. As shown in the analysis of variance table, it is divided into a row of fluctuations that follow the regression line (regression) and a row of fluctuations that do not follow (residual). In the regression line, the deviation sum of squares (42952688853) is The sum of squares of the deviation from the average value of the objective variable y is represented, and the value obtained by dividing this value by the degree of freedom (5) is the unbiased variance (859053770.5). In the residual row, the deviation sum of squares (731052441.5) represents the sum of squares of the deviations on the residual side, and the value obtained by dividing this value by the degree of freedom (72) is the unbiased variance (10153506.13). The ratio of the unbiased dispersion of both is the F value (observed dispersion ratio) = 84.60661. Since the F value is larger than the boundary value of F (F (0.95). Value of 5% level) = 2.341828, it can be seen that the effect of the regression coefficient is significant. Since the P value is sufficiently small compared to the rejection range of 1%, it is significant at the 1% level.

The table in the lower part of FIG. 12 has almost the same items as the table in the lower part of FIG. 11, and the first column has explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) have been removed. The second column is the (partial) regression coefficient of the multiple regression equation 2, the third column is the standard error that is the estimation error of the (partial) regression coefficient of the second column, and the fourth column is standardized and analyzed The fifth column is the F value which is the variance ratio of the (partial) regression coefficient in the second column. Specifically, the F value is the square of the value obtained by dividing the (partial) regression coefficient in the second column by the standard error in the third column. As shown in the upper right table of FIG. 12, the variable selection method used was a variable increase method in which the explanatory variables start from 0 and increase the explanatory variables sequentially. As shown by the selection criterion value of F value = 2, an explanatory variable having an F value of 2 or more for each (partial) regression coefficient was taken in while creating multiple regression equation 2. As shown in the F value column in the lower table of FIG. 12, the F values of the explanatory variables x ₁ , x ₄ , x ₈ , x _9, and x ₁₀ were all greater than 2. Therefore, as shown in the number of selected variables in the upper right table of FIG. 12, there are 5 explanatory variables (1 × ₁ by venous blood sampling and the radioactivity count value in the brain of the corresponding frame in 1st.SPECT. 4 x ₄ , x ₈ , x ₉ and x ₁₀ ) were selected.

FIG. 13A shows the correlation between the radioactivity count value and the predicted value used in the multiple regression analysis using the explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) selected in FIG. FIG. 13B shows a scatter diagram matrix obtained by enlarging a part of FIG. 13A for explanation. In the scatter diagram matrix shown in FIG. 13 (A), the horizontal axis of the graph shown in the (1, 1) element is the radioactivity count value of the sample obtained by one-point blood collection after 55 minutes for each subject, The vertical axis represents the number of people (frequency) that became the radioactivity count value. As shown in the enlarged (1, 1) element in FIG. 13B, it can be seen that the frequency distribution is almost normal.

In the scatter diagram matrix shown in FIG. 13A, in each graph shown in each diagonal element from the (2, 2) element to the (5, 5) element, the horizontal axis is measured for each subject in the order of the above elements. 1st. It is the radioactivity count value in the brain of each frame (third, seventh, eighth and ninth frames) corresponding to the explanatory variables x ₄ , x ₈ , x ₉ and x ₁₀ in SPECT, Number of people (frequency). For example, as shown in the enlarged graph of the (2,2) element in FIG. 13B, the element is in the brain at the 406.85 minute frame (third frame) after the first administration of the drug. The radioactivity count value (horizontal axis) and the number of persons (vertical axis) are shown. As shown in the graph of each diagonal element from the (2, 2) element to the (5, 5) element, it can be seen that the frequency distribution is almost a normal distribution.

In the scatter diagram matrix shown in FIG. 13 (A), the horizontal axis of the graph shown in the (6, 6) element is a continuous artery for 6 minutes from the first ¹²³ I-IMP intravenous injection measured in advance for each subject. It is the radioactivity count value of the collected blood sample, and the vertical axis is the number of people (frequency) that became the radioactivity count value. It can be seen that the frequency distribution is almost normal.

In scatterplot matrix shown in FIG. 13 (A), (1,2) graph radioactive count of the samples obtained by the one-dot vein bled after 55 minutes for each subject represented in element (explanatory variables x ₁ Value (vertical axis) and 1st. The correlation with the brain radioactivity count value (horizontal axis) in the third frame during SPECT is shown. See the enlarged (1,2) element in FIG. 13 (B). Similarly, the graphs shown from the (1,3) element to the (1,5) element show the radioactivity count values of the samples obtained by single point blood sampling after 55 minutes measured for each subject in the order of the above elements. (Vertical axis, common to each element) and 1st. The correlation with the brain radioactivity count value (each horizontal axis) of the seventh frame, the eighth frame, and the ninth frame in SPECT is shown. The graph shown in the last (1, 6) element shows the radioactivity count value (vertical axis) of the sample obtained by single-point blood collection after 55 minutes measured for each subject and the first measured in advance for each subject. The correlation with the radioactivity count value (horizontal axis) of the 6-minute continuous arterial blood collection from the ^123rd I-IMP intravenous injection is shown. Both show good correlation.

In the scatter diagram matrix shown in FIG. 13 (A), the graphs shown from the (2, 3) element to the (2, 5) element are in the order of the above elements, after the first administration of the drug measured for each subject. The radioactivity count value in the brain (vertical axis, common to each element) in the frame at the time of 6.75 minutes (406.85 s) (third frame) and the 1st. The correlation with the brain radioactivity count value (each horizontal axis) of the seventh frame, the eighth frame, and the ninth frame in SPECT is shown. The graph shown in the last (2,6) element shows the radioactivity count value in the brain in the frame (third frame) at 6.75 minutes (406.85 s) after the first administration of the drug measured for each subject. (Vertical axis) shows the correlation between the radioactivity count value (horizontal axis) of a sample collected continuously for 6 minutes from the first intravenous injection of ¹²³ I-IMP measured in advance for each subject. Both show good correlation. Hereinafter, the (3, 4) element to the (3, 6) element, the (4, 5) element to the (4, 6) element, etc. in the scatter diagram matrix shown in FIG. Description is omitted. The lower triangular element of the scatter diagram matrix shown in FIG. 13A is a display in which the vertical axis and horizontal axis in the upper triangular element described above are interchanged, and thus the description thereof is omitted.

FIG. 14 shows the radioactivity count value in arterial blood estimated (predicted) by the estimation unit 23 using the explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) selected in FIG. Comparison between predicted values and observed values. In FIG. 14, the horizontal axis indicates the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated by the estimation unit 23 using the multiple regression equation 2, and the vertical axis indicates the same. It is a radioactivity count value (observed value) in arterial blood actually observed by 6-minute continuous arterial blood sampling in the MS method for a subject. As shown in FIG. 14, the both show a very good correlation. Therefore, the multiple regression equation 2 having the regression coefficients corresponding to the explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) obtained by the multiple regression equation acquisition unit 21 is maintained for 6 minutes in the MS method. It can be seen that the radioactivity count value in arterial blood that can be observed by arterial blood sampling can be sufficiently estimated (predicted). For this reason, it is considered that the 6-minute continuous arterial blood sampling in the MS method with invasiveness can be omitted.

  FIG. 15 shows the distribution of the difference (residual) between the vertical axis (observed value) and the horizontal axis (predicted value) in FIG. In FIG. 15, the horizontal axis represents the residual radioactivity count value, and the vertical axis represents the frequency. For reference, the normal distribution is shown superimposed (red curve in the original figure). It can be seen that, except for some frequencies, the distribution is close to a normal distribution with an average of zero residuals.

  FIG. 16 shows the distribution of observed values shown in FIG. In FIG. 16, the horizontal axis represents the radioactivity count value in arterial blood actually observed by 6-minute continuous arterial blood sampling in the MS method for the test subject, and the vertical axis represents the number of subjects (frequency) that became the radioactivity count value. . As shown in FIG. 16, it can be seen that the distribution of observed values is almost normal.

FIG. 17 shows a distribution of predicted values shown in FIG. In FIG. 17, the horizontal axis represents the arterial blood estimated (predicted) by the estimation unit 23 using the explanatory variables (x ₁ , x ₄ , x ₈ , x ₉ and x ₁₀ ) selected for the subject in FIG. The radioactivity count value (predicted value), and the vertical axis is the number of subjects (frequency) having the radioactivity count value. As shown in FIG. 17, it can be seen that the distribution of predicted values is almost normal.

As described above, according to the first embodiment of the present invention, the cerebral blood flow can be calculated using the radioactivity count value obtained by the minimally invasive technique using single point blood collection. ^In the protocol of the split administration method in Example 1 using ¹²³ I-IMP cerebral blood flow SPECT, first, a first intravenous injection of a drug ( ¹²³ I-IMP (111 MBq)) was performed, and dynamic SPECT (FIG. 1 (B 1st.SPECT) shown in FIG. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 1B) is performed. After 55 minutes, venous blood is collected from the hand opposite to the drug administration, and the radioactivity count value is measured from the collected venous blood. As described above, the patient factors used are the amount of SPECT change in the brain and the radioactivity count of single-point venous blood sampling at 55 minutes after ¹²³ I-IMP administration, and the test is completed within 1 hour as a whole. That is, the inspection can be performed with a minimally invasive and simple technique.

Data for obtaining the multiple regression equation 2 used in the present invention is recorded in the recording device 10. The intracerebral radioactivity count value recording unit 12 records a plurality of intracerebral radioactivity count values measured every predetermined time for a subject in 1st.SPECT. The arterial radioactivity count value recording unit 14 records the radioactivity count value in arterial blood observed by continuous arterial blood sampling for 6 minutes after the first ¹²³ I-IMP intravenous injection in the MS method for the subject. Yes. In order to obtain the multiple regression equation 2 used in the cerebral blood flow rate calculation program of the present invention, the continuous arterial blood collection result for 6 minutes from the first ¹²³ I-IMP intravenous injection measured in advance is used. As shown in the protocol of FIG. 1, when the cerebral blood flow calculation program of the present invention is executed, the measurement result by continuous arterial blood sampling for 6 minutes is not necessary. The imaging result was recorded in the brain radioactivity count value recording unit 12. One point arterial blood sampling after 8 minutes for prediction was performed in 76 cases, and one point vein blood sampling after 55 minutes elapsed in 68 cases, and the radioactivity count value of each sample was recorded in the recording device 10 as appropriate. It is.

The multiple regression equation acquisition unit 21 explained the above protocol, the radioactivity count value of the sample obtained by single-point vein blood collection after 55 minutes after the first administration of the drug ( ¹²³ I-IMP (111MBq)) and the brain The intracerebral radioactivity count values recorded in the internal radioactivity count value recording unit 12 are used as explanatory variables, and the radioactivity count values in arterial blood recorded in the arterial blood radioactivity count value recording unit 14 are used as objective variables. A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis is acquired. In general, multiple regression equation objective variable y, explanatory variable x, when the b _i and the regression coefficient (b ₀ Sections), is given by the multiple regression equation 2 described above. The multiple regression equation acquisition unit 21 uses, for example, the explanatory variable x ₁ as the radioactivity count value of a sample obtained by one-point blood collection after 55 minutes, and the explanatory variable x _i (i = 2 to k) as the intracerebral radioactivity count. A plurality of (for example, the above nine frames, where k = 10) brain radioactivity count values recorded in the value recording section 12 are used, and the objective variable y is recorded in the arterial blood radioactivity count value recording section 14. As the radioactivity count value, multiple regression analysis is performed on the data recorded in the brain radioactivity count value recording unit 12 and the arterial blood radioactivity count value recording unit 14.

  The brain radioactivity count value input unit 22 is 1st. A plurality of brain radioactivity count values measured every predetermined time for a subject subject in a predetermined period during SPECT are input. Here, the subject test subject does not perform invasive 6-minute continuous arterial blood collection, but instead uses a radioactivity count value obtained by a minimally invasive technique using single-point venous blood collection after 55 minutes. This is the meaning of the subject who calculates the blood flow. What is necessary is just to be for every elapsed time in nine frames mentioned above, for example for every predetermined time progress.

The estimation unit 23 uses the multiple regression equation 2 (the intercept b ₀ described above) acquired by the multiple regression equation acquisition unit 21 for the brain radioactivity count value of the subject subject input by the brain radioactivity count value input unit 22. And the multiple regression equation 2) having regression coefficients of b _{1 to} b ₁₀ to estimate the radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method for the subject subject . For comparison, the inventors have also performed 6-minute continuous arterial blood sampling in the MS method together with single-point vein blood sampling, and measured the radioactivity count value in arterial blood. Distribution of radioactivity count values in arterial blood estimated (predicted) for a plurality of subject subjects, and radioactivity count values in arterial blood actually observed by continuous arterial blood sampling for 6 minutes for a plurality of subject subjects The distribution was well correlated. Since the multiple correlation coefficient R = 0.828219 and a very good correlation is recognized between them, it can be seen that the estimation by the estimation unit 23 using the multiple regression equation 2 is suitable for clinical application. That is, the multiple regression equation 2 having the above regression coefficient obtained by the multiple regression equation acquisition unit 21 estimates the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method (prediction). ). Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation is not a mere selection example but an essential selection that has made the above estimation (prediction) remarkably successful.

The cerebral blood flow rate calculation unit 24 is based on the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 and the radioactivity count value in the arterial blood estimated by the estimation unit 23. Calculate cerebral blood flow for. In Equation 1E described in the background art, A ′ · (5R + D) is the total radioactivity A of arterial blood (R · t (ml)) obtained during t minutes, that is, the radioactivity count in arterial blood when continuous arterial blood is collected. Value. Therefore, the radioactivity count value in the arterial blood estimated by the estimation unit 23 may be used for the term. Since S ₃₀ · (H ₅ / H ₃₀ ) is the radioactivity Cb in the brain tissue t = 5 minutes after the IMP intravenous injection, the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 is Use it. The blood collection rate R = 1.03 (ml / min), the octanol extraction rate N = 0.8, and the cross calibration factor CCF = 0.00051 may be the values of the background art as they are, or may be reset as appropriate. Good. As a result, the cerebral blood flow rate calculation unit 24 can calculate the cerebral blood flow rate F for the subject subject. As mentioned above, according to Example 1 of this invention, when calculating | requiring the calculation of cerebral blood flow volume by the cerebral blood flow SPECT using a division | segmentation administration method, the radiation obtained by the minimally invasive and simple method by one-point vein blood collection It is possible to provide a cerebral blood flow calculation program or the like that can obtain a highly accurate cerebral blood flow using the performance count value.

In the second embodiment, a case will be described in which cerebral blood flow is calculated using a radioactivity count value obtained by a minimally invasive technique using one-point arterial blood sampling. 18 (A) and 18 (B) show the protocol of the divided administration method in Example 2 of the present invention using ¹²³ I-IMP cerebral blood flow SPECT. As shown in FIG. 18 (A), the first intravenous injection of ¹²³ I-IMP (111MBq) is first performed, and dynamic SPECT (1st. SPECT shown in FIG. 18 (B)) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After the elapse of 8 minutes (after the elapse of a predetermined time), single-point arterial blood sampling is performed from the hand (for example, the left hand) opposite to the administration of the drug ( ¹²³ I-IMP (111MBq)). The radioactivity count value is measured from the collected arterial blood. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 18B) is performed. In FIG. 18B, as in FIG. 1B, the residual radioactivity (background) by the first ¹²³ I-IMP intravenous injection at this time is indicated by a dotted line. As described above, the patient factors used are the intracerebral SPECT change amount and the radioactivity count of single-point arterial blood sampling after 8 minutes of ¹²³ I-IMP administration, and the test is completed within 1 hour as a whole. That is, the inspection can be performed with a minimally invasive and simple technique.

  Since the imaging conditions and the imaging target are the same as those in the first embodiment, description thereof is omitted. The imaging results were recorded in the brain radioactivity count value recording unit 12 as in Example 1. As the 6-minute continuous arterial blood collection data for obtaining the multiple regression equation 2, the data recorded in the arterial blood radioactivity count value recording unit 14 in Example 1 was used. Alternatively, it may be applied to all cases in the same manner as in Example 1 and recorded again in the arterial blood radioactivity count value recording unit 14. One-point arterial blood sampling after 8 minutes for prediction was performed on 76 cases, and the radioactivity count value of each sample was appropriately recorded in the recording device 10. These numbers are merely examples, and the application of the cerebral blood flow rate calculation program of the present invention is not limited to these numbers.

In the second embodiment, the functions of the multiple regression equation acquisition unit 21 and the like shown in the functional block diagram 20 of the cerebral blood flow calculation program in FIG. The multiple regression equation obtaining unit 21 in FIG. 2 is one point after 8 minutes (after a predetermined time) after the first administration of the drug ( ¹²³ I-IMP (111MBq)), which explains the protocol shown in FIG. Using the radioactivity count value of the sample obtained by arterial blood sampling and the plurality of brain radioactivity count values recorded in the brain radioactivity count value recording unit 12 as explanatory variables, the arterial blood radioactivity count value recording unit 14 A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using the recorded radioactivity count value in arterial blood as an objective variable is obtained. Form of the multiple regression equation is similar to the multiple regression equation 2 in Example 1, the regression coefficient b _i is different. The multiple regression equation acquisition unit 21 uses, for example, the explanatory variable x ₁ as the radioactivity count value of the sample obtained by blood sampling at one point after 8 minutes, and the explanatory variable x _i (i = 2 to k) as the radioactivity count in the brain. A plurality of (for example, the above nine frames, where k = 10) brain radioactivity count values recorded in the value recording section 12 are used, and the objective variable y is recorded in the arterial blood radioactivity count value recording section 14. As the radioactivity count value, multiple regression analysis is performed on the data recorded in the brain radioactivity count value recording unit 12 and the arterial blood radioactivity count value recording unit 14. FIG. 19 shows the regression coefficient obtained by conducting the multiple regression analysis in Example 2. As shown in FIG. 19, the regression coefficient _{b 0} (intercept) is -538.821 explanatory variables _x 1 regression coefficient _{b 1} corresponding to the (one point arterial blood sampling after the lapse of 8 minutes) is 1.803811, the explanatory variable x ₂ (regression coefficient b ₂ in the brain after 1.35 minutes) is 4.571971,..., Explanatory variable x ₁₀ (radioactivity count value in the brain after 22.95 minutes) regression coefficient _{b 10} corresponding to) were obtained and the like -0.12704.

  The brain radioactivity count value input unit 22 in FIG. A plurality of brain radioactivity count values measured every predetermined time for a subject subject in a predetermined period during SPECT are input. Here, the subject test subject does not perform invasive 6-minute continuous arterial blood sampling, but instead uses a radioactivity count value obtained by a minimally invasive technique using single-point arterial blood sampling after 8 minutes. This is the meaning of the subject who calculates the blood flow. What is necessary is just to be for every elapsed time in nine frames mentioned above, for example for every predetermined time progress.

Subsequently, the estimation unit 23 in FIG. 2 obtains the multiple regression equation obtained by the multiple regression equation obtaining unit 21 from the brain radioactivity count value of the subject subject inputted by the brain radioactivity count value input unit 22. 2 (multiple regression equation 2 having regression coefficients of the above-described intercepts b ₀ and b _{1 to} b ₁₀ ), radiation in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject in question Estimate the performance count value. 20A and 20B are radioactivity count values in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method for subjects to be estimated (predicted) by the estimation unit 23 in Example 2. A comparison (correlation) between the radioactivity count value (vertical axis) in arterial blood actually observed by 6-minute continuous arterial blood sampling in the MS method is shown. For comparison, the inventors have also performed 6-minute continuous arterial blood sampling in the MS method together with single-point vein blood sampling, and measured the radioactivity count value in arterial blood. In FIG. 20A, a square (red square in the original drawing) is a distribution of radioactivity count values in arterial blood estimated (predicted) for a plurality of subjects, and a diamond (purple diamond in the original drawing) is a target. The distribution of radioactivity count values in arterial blood actually observed by continuous arterial blood sampling for 6 minutes for a plurality of subjects. As shown in FIG. 20A, it can be seen that the distributions of both are well correlated. As shown in the regression statistics of FIG. 20 (B), for example, multiple correlation coefficient R = 0.852719, and both have a very good correlation. Therefore, the estimation by the estimation unit 23 using the multiple regression equation 2 is It turns out that it is suitable for clinical application. That is, the multiple regression equation 2 having the above regression coefficient obtained by the multiple regression equation acquisition unit 21 sufficiently estimates (predicts) the radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method. Is something that can be done. Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation 2 is not just a selection example, but an essential selection that has made the above estimation (prediction) remarkably successful.

  Subsequently, the cerebral blood flow rate calculation unit 24 of FIG. 2 converts the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 and the radioactivity count value in the arterial blood estimated by the estimation unit 23. Based on the subject, cerebral blood flow is calculated for the subject. Since the calculation method of the cerebral blood flow is calculated based on Formula 1E as in the first embodiment, the description thereof is omitted. In the above description, 1st. Actual measured values were used as the plurality of brain radioactivity count values measured at predetermined time intervals for the subjects in SPECT. However, a relative value based on the brain radioactivity count value measured at the second administration of the drug can also be used. Since this point is the same as that of Example 1, description is abbreviate | omitted.

As described above, according to the second embodiment of the present invention, it is possible to calculate the cerebral blood flow using the radioactivity count value obtained by the minimally invasive technique using single-point arterial blood sampling. ^In the protocol of the divided administration method in Example 2 of the present invention using ¹²³ I-IMP cerebral blood flow SPECT, first, a first intravenous injection of a drug ( ¹²³ I-IMP (111 MBq)) is performed. Dynamic SPECT (1st. SPECT shown in FIG. 18B) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After the elapse of 8 minutes (after a predetermined time), one-point arterial blood sampling is performed from the hand opposite to the drug ( ¹²³ I-IMP (111MBq)) (for example, the left hand when administered from the right hand). The radioactivity count value is measured from the collected arterial blood. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 18B) is performed. As described above, the patient factors used are the SPECT variation in the brain and the radioactivity count of single-point arterial blood sampling at 8 minutes after ¹²³ I-IMP administration, and the test is completed within 1 hour as a whole. That is, the inspection can be performed with a minimally invasive and simple technique.

In the second embodiment, the functions of the multiple regression equation acquisition unit 21 and the like shown in the functional block diagram 20 of the cerebral blood flow calculation program in FIG. The multiple regression equation acquisition unit 21 is a sample obtained by single-point arterial blood sampling after 8 minutes (after a predetermined time) after the first administration of the drug ( ¹²³ I-IMP (111 MBq)) described for the above protocol. Radioactivity count values recorded in the arterial blood radioactivity count value recording section 14 with the radioactivity count values of the brain and a plurality of intracerebral radioactivity count values recorded in the intracerebral radioactivity count value recording section 12 as explanatory variables. A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis with the performance count value as an objective variable is acquired. Form of the multiple regression equation is similar to the multiple regression equation 2 in Example 1, the regression coefficient b _i is different. The multiple regression equation acquisition unit 21 uses, for example, the explanatory variable x ₁ as the radioactivity count value of the sample obtained by blood sampling at one point after 8 minutes, and the explanatory variable x _i (i = 2 to k) as the radioactivity count in the brain. A plurality of (for example, the above nine frames, where k = 10) brain radioactivity count values recorded in the value recording section 12 are used, and the objective variable y is recorded in the arterial blood radioactivity count value recording section 14. As the radioactivity count value, multiple regression analysis is performed on the data recorded in the brain radioactivity count value recording unit 12 and the arterial blood radioactivity count value recording unit 14.

The estimation unit 23 uses the multiple regression equation 2 (the intercept b ₀ described above) acquired by the multiple regression equation acquisition unit 21 for the brain radioactivity count value of the subject subject input by the brain radioactivity count value input unit 22. And the multiple regression equation 2) having regression coefficients of b _{1 to} b ₁₀ to estimate the radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method for the subject subject . Radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit 23, and 6-minute continuous arterial blood sampling in the MS method for the same subject. According to the comparison with the radioactivity count value in arterial blood observed by the above, it can be seen that the distribution of both is well correlated. Since the multiple correlation coefficient R = 0.852719 and very good correlation is recognized between the two, it can be seen that the estimation by the estimation unit 23 using the multiple regression equation 2 is suitable for clinical application. As described above, the multiple regression equation 2 having the regression coefficient obtained by the self-weight regression equation acquisition unit 21 in Example 2 is similar to that in Example 1 in the arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method. The radioactivity count value can be sufficiently estimated (predicted). Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation 2 is not just a selection example, but an essential selection that has made the above estimation (prediction) remarkably successful.

  The cerebral blood flow rate calculation unit 24 is based on the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 and the radioactivity count value in the arterial blood estimated by the estimation unit 23. Calculate cerebral blood flow for. Since the calculation method of the cerebral blood flow is calculated based on Formula 1E as in the first embodiment, the description thereof is omitted. In the above description, 1st. Actual measured values were used as the plurality of brain radioactivity count values measured at predetermined time intervals for the subjects in SPECT. However, a relative value based on the brain radioactivity count value measured at the second administration of the drug can also be used. Since this point is the same as that of Example 1, description is abbreviate | omitted. As described above, according to Example 2 of the present invention, when calculating the cerebral blood flow volume by cerebral blood flow SPECT using the divided administration method, the radiation obtained by a minimally invasive and simple technique using one-point arterial blood sampling It is possible to provide a cerebral blood flow calculation program or the like that can obtain a highly accurate cerebral blood flow using the performance count value.

In the third embodiment, the case where the cerebral blood flow is calculated using only the radioactivity count value obtained by the noninvasive technique of non-blood collection will be described. FIGS. 21 (A) and (B) show the protocol of the divided administration method in Example 3 of the present invention using ¹²³ I-IMP cerebral blood flow SPECT. As shown in FIG. 21, first, ¹²³ I-IMP (111MBq) is first injected intravenously, and dynamic SPECT (1st. SPECT shown in FIG. 21B) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 21B) is performed. In FIG. 21B, as in FIG. 1B, the residual radioactivity (background) by the first ¹²³ I-IMP intravenous injection at this time is indicated by a dotted line. As described above, the only patient factor used is the brain SPECT change amount, and the test is completed within one hour as a whole. That is, since the test is non-blood collection, it is non-invasive and can be performed by a simple method.

  Since the imaging conditions and the imaging target are the same as those in the first and second embodiments, the description thereof is omitted. The imaging results were recorded in the brain radioactivity count value recording unit 12 as in Examples 1 and 2. For the continuous arterial blood sampling for 6 minutes to obtain the multiple regression equation 2, the data recorded in the arterial blood radioactivity count value recording unit 14 in Example 1 was used. Alternatively, it may be applied to all cases in the same manner as in Example 1 and recorded again in the arterial blood radioactivity count value recording unit 14.

In the third embodiment, the functions of the multiple regression equation acquisition unit 21 and the like shown in the functional block diagram 20 of the cerebral blood flow calculation program in FIG. 2 can be used in substantially the same manner as in the first and second embodiments. The multiple regression equation acquisition unit 21 in FIG. 2 is recorded in the brain radioactivity count value recording unit 12 after the first administration of the drug ( ¹²³ I-IMP (111 MBq)), which is described for the protocol shown in FIG. A partial regression coefficient obtained by multiple regression analysis using a plurality of intracerebral radioactivity count values as explanatory variables and the radioactivity count values in arterial blood recorded in the arterial blood radioactivity count recording unit 14 as objective variables. Obtain the multiple regression equation. The form of the multiple regression equation is the same as the multiple regression equation 2 in the first and second embodiments, but the regression coefficients b _i are different. The multiple regression equation acquisition unit 21 records a plurality of explanatory variables x _i (i = 1 to k−1) recorded in the brain radioactivity count value recording unit 12 (for example, for the above nine frames, where k = 10). The radioactivity count value in the brain is used as the radioactivity count value in the brain, and the target variable y is the radioactivity count value in the arterial blood recorded in the arterial blood radioactivity count value recording unit 14. Multiple regression analysis is performed on the data recorded in the recording unit 14.

  In the brain radioactivity count value input unit 22 of FIG. A plurality of brain radioactivity count values measured every predetermined time for a subject subject in a predetermined period during SPECT are input. Here, the subject subject is a subject that calculates cerebral blood flow using the radioactivity count value input by the intracerebral radioactivity count value input unit 22 without performing invasive 6-minute continuous arterial blood sampling. Means the subject. What is necessary is just to be for every elapsed time in nine frames mentioned above, for example for every predetermined time progress.

Subsequently, the estimation unit 23 in FIG. 2 obtains the multiple regression equation obtained by the multiple regression equation obtaining unit 21 from the brain radioactivity count value of the subject subject inputted by the brain radioactivity count value input unit 22. 2 (multiple regression equation 2 having a regression coefficient of b _{1 to} b ₉ above), the radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method for the subject subject presume. FIG. 22 shows the radioactivity count value (vertical axis) in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method for the subject subject estimated (predicted) by the estimation unit 23 in Example 3. The comparison (correlation) with the radioactivity count value (horizontal axis) in arterial blood actually observed by 6-minute continuous arterial blood sampling in the MS method is shown. For comparison, the inventors also performed continuous arterial blood sampling for 6 minutes in the MS method for subject subjects, and measured the radioactivity count value in arterial blood. In FIG. 22A, a square (red square in the original drawing) is a distribution of radioactivity count values in arterial blood estimated (predicted) for a plurality of subjects, and a diamond (purple diamond in the original drawing) is a target. The distribution of radioactivity count values in arterial blood actually observed by continuous arterial blood sampling for 6 minutes for a plurality of subjects. As shown in FIG. 22, since the multiple correlation coefficient R = 0.7529 and very good correlation is recognized between them, the estimation by the estimation unit 23 using the multiple regression equation 2 may be suitable for clinical application. Recognize. From the above, the multiple regression equation 2 having the regression coefficient obtained by the multiple regression equation acquisition unit 21 sufficiently estimates (predicts) the radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling for 6 minutes in the MS method. Is something that can be done. Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation 2 is not just a selection example, but an essential selection that has made the above estimation (prediction) remarkably successful.

  Subsequently, the cerebral blood flow rate calculation unit 24 of FIG. 2 converts the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 and the radioactivity count value in the arterial blood estimated by the estimation unit 23. Based on the subject, cerebral blood flow is calculated for the subject. Since the calculation method of the cerebral blood flow is calculated based on Formula 1 as in the first and second embodiments, the description thereof is omitted. In the above description, 1st. Actual measured values were used as the plurality of brain radioactivity count values measured at predetermined time intervals for the subjects in SPECT. However, a relative value based on the brain radioactivity count value measured at the second administration of the drug can also be used. Since this point is the same as in the first and second embodiments, description thereof is omitted.

As described above, according to the third embodiment of the present invention, it is possible to calculate the cerebral blood flow using only the radioactivity count value obtained by the non-invasive technique of non-blood collection. ^In the protocol of the divided administration method in Example 3 of the present invention using ¹²³ I-IMP cerebral blood flow SPECT, first, a first intravenous injection of a drug ( ¹²³ I-IMP (111 MBq)) was performed, and dynamic SPECT (FIG. 21st (SPECT) shown in 21 (B) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 21B) is performed. As described above, the only patient factor used is the brain SPECT change amount, and the test is completed within one hour as a whole. That is, since the test is non-blood collection, it is non-invasive and can be performed by a simple method.

In the third embodiment, the functions of the multiple regression equation acquisition unit 21 and the like shown in the functional block diagram 20 of the cerebral blood flow calculation program in FIG. 2 can be used in substantially the same manner as in the first and second embodiments. The multiple regression equation acquisition unit 21 describes a plurality of intracerebral radioactivity recorded in the intracerebral radioactivity count value recording unit 12 after the first administration of the drug ( ¹²³ I-IMP (111 MBq)) described for the protocol. A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using the count value as an explanatory variable and the radioactivity count value in arterial blood recorded in the arterial blood radioactivity count value recording unit 14 as an objective variable is acquired. . The form of the multiple regression equation is the same as the multiple regression equation 2 in the first and second embodiments, but the regression coefficients b _i are different. The multiple regression equation acquisition unit 21 has, for example, a plurality of (for example, the above nine frames, where k = 10) in which the explanatory variables x _i (i = 1 to k−1) are recorded in the brain radioactivity count value recording unit 12. ) The radioactivity count value in the brain and the radioactivity count value recording portion 12 in the brain and the arterial blood radioactivity count as the radioactivity count value in the arterial blood recorded in the arterial blood radioactivity count value recording portion 14 as the brain radioactivity count value Multiple regression analysis is performed on the data recorded in the value recording unit 14.

The estimation unit 23 uses the multiple regression equation 2 (the above b ₁ to b) obtained by the multiple regression equation acquisition unit 21 for the brain radioactivity count value of the subject subject input by the brain radioactivity count value input unit 22. by applying the multiple regression equation 2) with a regression coefficient of b _9, estimating the radioactivity count in the arterial blood can be observed by a continuous arterial blood sampling 6 minutes in MS method for subjects of interest. Radioactivity count value in arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method for the subject subject estimated (predicted) by the estimation unit 23, and 6-minute continuous arterial blood sampling in the MS method for the same subject. According to the comparison with the radioactivity count value in arterial blood observed by the above, it can be seen that the distribution of both is well correlated. Since the multiple correlation coefficient R = 0.7529 and a very good correlation is recognized between them, it can be seen that the estimation by the estimation unit 23 using the multiple regression equation 2 is suitable for clinical application. As described above, the multiple regression equation 2 having the regression coefficient obtained by the self-weight regression equation acquisition unit 21 in Example 3 is similar to that in Example 1 in the arterial blood that can be observed by 6-minute continuous arterial blood sampling in the MS method. The radioactivity count value can be sufficiently estimated (predicted). Therefore, it can be said that the method of taking each explanatory variable used in the multiple regression equation 2 is not just a selection example, but an essential selection that has made the above estimation (prediction) remarkably successful.

  The cerebral blood flow rate calculation unit 24 is based on the intracerebral radioactivity count value input by the intracerebral radioactivity count value input unit 22 and the radioactivity count value in the arterial blood estimated by the estimation unit 23. Calculate cerebral blood flow for. Since the calculation method of the cerebral blood flow is calculated based on Formula 1E as in the first embodiment, the description thereof is omitted. In the above description, 1st. Actual measured values were used as the plurality of brain radioactivity count values measured at predetermined time intervals for the subjects in SPECT. However, a relative value based on the brain radioactivity count value measured at the second administration of the drug can also be used. Since this point is the same as in the first and second embodiments, description thereof is omitted. As mentioned above, according to Example 3 of this invention, when calculating | requiring the calculation of cerebral blood flow volume by the cerebral blood flow SPECT using a division | segmentation administration method, the radioactivity obtained by the non-invasive and simple technique called non-blood collection. It is possible to provide a cerebral blood flow calculation program or the like that can obtain a highly accurate cerebral blood flow using the count value.

  In the fourth embodiment, the cerebral blood flow calculation program and the cerebral blood flow calculation method described above will be described using a flowchart and a display image. For convenience, the cerebral blood flow rate calculation program of Example 1 (single-point vein blood collection) is taken as an example. Since other Examples 2 and 3 can be described in the same manner, description thereof is omitted.

FIG. 23 is a flowchart showing operations of an example of a cerebral blood flow calculation program and a cerebral blood flow calculation method. 24 to 32 show display images displayed on the display device 2 (described later) such as a display for explaining the operation, output and the like of the cerebral blood flow calculation program. In the following description of FIGS. 24 to 32, the same reference numerals indicate the same elements, and therefore, duplicate description is omitted. Hereinafter, description will be made using the flowchart shown in FIG. 23 and the display images shown in FIGS. As shown in FIG. 23, first, one point is given after 55 minutes (after a predetermined time has elapsed) after the first administration of the drug ( ¹²³ I-IMP (111MBq)) described for the above-described protocol (FIG. 1). Using the radioactivity count value of the sample obtained by venous blood sampling and the plurality of brain radioactivity count values recorded in the brain radioactivity count value recording unit 12 as explanatory variables, the arterial blood radioactivity count value recording unit 14 A multiple regression equation having a partial regression coefficient obtained by multiple regression analysis using the recorded radioactivity count value in arterial blood as an objective variable is acquired (multiple regression equation acquisition step, step S10). The multiple regression equation is given by the multiple regression equation 2 described above. For example, the explanatory variable x ₁ is the radioactivity count value of a sample obtained by one-point venous blood collection after 55 minutes, and the explanatory variable x _i (i = 2 to k) is recorded in the brain radioactivity count value recording unit 12. A plurality of (for example, the above 9 frames, where k = 10) brain radioactivity count values, and the target variable y is the radioactivity count value in arterial blood recorded in the arterial blood radioactivity count value recording unit 14. Multiple regression analysis is performed on the data recorded in the internal radioactivity count value recording unit 12 and the arterial blood radioactivity count value recording unit 14.

  Next, as shown in FIG. A plurality of intracerebral radioactivity count values measured every predetermined time for the subject subject to SPECT are input (intracerebral radioactivity count value input step, step S12). FIG. 24 shows a display image when selecting subject subject data. FIG. 24A shows an examination folder in which a plurality of brain radioactivity count values measured for a predetermined time for a subject subject are recorded. As shown in FIG. 24A, in the examination folder, there is a file (for example, the file name is P001) in which the brain radioactivity count value is recorded for each subject. In the file, dynamic projection data (collected image) which is three-dimensional raw brain radioactivity count data collected 360 ° while rotating the camera of the imager is recorded. The camera starts from the (face) front and rotates in a number of steps. If two detectors rotate 180 ° in two minutes (one frame), the camera will come to the front of the (face) every two minutes. For example, when 20 frames are imaged, it can be set to take out only the front-collected images of the subject every 2 minutes, 4 minutes, 6 minutes, and every 2 minutes (every predetermined time). As shown in FIG. 24A, a file (for example, file name P001) in which a collected image of a desired subject is recorded is selected by clicking with an input operation unit 149 such as a mouse (described later). As a result, the selected file name 40, the input data format, and the like are displayed as shown in FIG. Here, when the OK button 41 is clicked with the input operation unit 149 such as a mouse, the cerebral blood flow calculation program is activated, and for example, a front collection image of the selected subject is input.

Subsequently, as shown in FIG. 23, a collected image based on the brain radioactivity count value input in the brain radioactivity count value input step (step S12) is displayed (collected image display step, step S14). This corresponds to the function of the collected image display unit 32 (collected image display means) shown in the functional block diagram 20 of FIG. FIG. 25 shows a display image immediately after the cerebral blood flow calculation program is started. FIG. 25A shows the regression coefficient and the like of the multiple regression equation 2 acquired in the multiple regression equation acquisition step (step S10). As shown in the column 50 of FIG. 25 (A), the columns indicated by f1 corresponds to the regression coefficient _{b 2} of multiple regression equation 2, the value is displayed as -6.18238, the elapsed time under the 1.35 is displayed. string indicated in the right f2 of the column indicated by f1 corresponds to the regression coefficient b ₃ of the multiple regression equation 2, the value is displayed as 9.43639, time thereunder are displayed 4.05 Yes. Since it is the same below, explanation is omitted. FIG. 25B shows the collected image 51 created and displayed in the collected image display step (step S14).

  As shown in FIG. 23, a predetermined shape is set on the collected image 51 displayed in the collected image display step (step S14), and an ROI based on a predetermined threshold is set in the predetermined shape (ROI setting). Step, step S16). This corresponds to the function of the ROI setting unit 33 (ROI setting means) shown in the functional block diagram 20 of FIG. FIG. 26 shows a display image in a state where the ROI is set. As shown in FIG. 26, an ellipse ROI 60 can be selected and set as a predetermined shape by clicking with an input operation unit 149 such as a mouse from the ROItype 61 box. As ROType 61, a polygon (Poligon) or a rectangle (Rectamgle) can be selected and set in addition to an ellipse. Of course, it can be modified to use other shapes. E of FIG. 26: Vein count 62 (= 6977) indicates the radioactivity count value of the sample obtained by single point blood sampling after 55 minutes of the selected subject. The radioactivity count value can be recorded in a desired file and automatically displayed in E: Vein Count 62. It can also be manually input in the E: Vein Count 62 field in this display image. is there. FIG. 27 shows a display image in a state where a threshold ROI is set. The threshold ROI is defined as necessary in the ROI 60 set as described above. More specifically, as shown in FIG. 27, a threshold level (Threshold Level (%)) box 70 is clicked with an input operation unit 149 such as a mouse, and a desired threshold level is input to the box 71 from an input operation unit 149 such as a keyboard. By inputting (%) and clicking the GO button 72 with the input operation unit 149 such as a mouse, the threshold ROI can be defined. Here, the value of the desired threshold level refers to% of the desired threshold when the maximum value of the brain radioactivity count value is 100%. As a result, as shown in the collected image 51 of FIG. 27, the defined threshold ROI is displayed with shading (shaded with a portion where the brain radioactivity count value is 50% or more).

As shown in FIG. 23, the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step (step S12) in the ROI 60 set in the ROI setting step (step S16) or in an arbitrary region. The time / radioactivity curve (Time Activity Curve: TAC) based on this is displayed (time / radioactivity curve display step. Step S18) This corresponds to the function of the time / radioactivity curve display unit 34 (time / radioactivity curve display means) shown in the functional block diagram 20 of FIG. FIG. 28 shows a display image of a time / radioactivity curve. As shown in FIG. 28, when the PLOT button 80 is clicked with the input operation unit 149 such as a mouse, a time / radioactivity curve 81 is displayed. FIG. 29 shows an enlarged display image of a part of FIG. As shown in FIG. 29, the frame number (6 in FIG. 29, 14.85 minutes after the first ¹²³ I-IMP intravenous injection) is input into the box 90 in the box 90 at rest (1st.SPECT). Input from the operation unit 149, and the frame number at rest (9 in FIG. 29, 22.95 minutes after the first ¹²³ I-IMP intravenous injection) is input into the box 91 from the input operation unit 149 such as a keyboard. When the Linear Fit button 92 is clicked with the input operation unit 149 such as a mouse, an approximate line 93 based on the brain radioactivity count values from the sixth frame to the ninth frame is displayed. That is, boxes 90 and 91 are used to specify a linear approximation range. The time / radioactivity curve display unit 34 obtains the approximate line 93 by performing linear regression on the time / activity curve 81 at the four points from the sixth frame to the ninth frame, which are the designated linear approximation range. The approximate straight line 93 corresponds to the residual radioactivity (background) indicated by the dotted line in FIG.

  As shown in FIG. 23, a predetermined range is set on the time / radioactivity curve 81 displayed in the time / radioactivity curve display step (step S18), and the acquired images in the sagittal section direction in the predetermined range are displayed. (Sagittal section direction collected image display step. Step S20). FIG. 30 shows a display image in a state where the collected image is displayed. This corresponds to the function of the sagittal section direction collected image display unit 35 (sagittal section direction collected image display means) shown in the functional block diagram 20 of FIG. The sagittal cross-sectional direction acquired image display unit 35 sets the range (predetermined range) for creating the sagittal cross-sectional direction acquired image in the resting (1st. SPECT) region on the time / radioactivity curve 81. The start position (straight line S1, shown in red in the original drawing) of the range for creating the collected image is set by appropriately dragging the Start scroll bar 100 to the left and right with the input operation unit 149 such as a mouse, and the end position (straight line F1). In the original drawing, it is shown in green.) Is set by appropriately dragging the Fin scroll bar 101 to the left and right with the input operation unit 149 such as a mouse. Further, the sagittal section direction collected image display unit 35 starts a range (line S2) in which the collected image is created in a region on load (2nd.SPECT) on the time / radioactivity curve 81 (shown in red in the original drawing). ) Is set by appropriately dragging the Start scroll bar 100 to the left and right using the input operation unit 149 such as a mouse, and the end position (straight line F2; shown in green in the original drawing) is set to the Fin scroll bar. The setting is made by appropriately dragging 101 to the left and right. The specification of the above range at rest can be started by, for example, clicking the rest region on the time / radioactivity curve 81 with the input operation unit 149 such as a mouse. The same applies to the start of specification of the above range at the time of load. Sagittal cross-section direction collected image display unit 35 is a range (line S1 to F1) for creating a collected image at rest set as described above when Projection button 102 is clicked by input operation unit 149 such as a mouse. 30 is created and displayed as a resting collected image indicated by reference numeral 103 in FIG. 30, and a collected image in a range (straight line S2 to F2) for creating the collected image at the time of load set as described above is created. Then, it is displayed as an on-load collection image indicated by reference numeral 104 in FIG. Here, when creating the collection image 104 at the time of loading, subtraction correction is performed using the residual radioactivity (background) estimated by the approximate straight line 93. The sagittal section direction collection image display unit 35 rotates the collection image 103 at rest in the horizontal direction (horizontal direction) by appropriately dragging the scroll bar 105 shown in FIG. 30 to the left and right with the input operation unit 149 such as a mouse. At the same time, the on-load collected image 104 can be rotated and displayed in the horizontal direction. The collected image rotation function by the scroll bar 105 is preferably used for confirming the data of the collected image.

  As shown in FIG. 23, a predetermined range is set using the acquired image displayed in the sagittal cross-sectional direction acquired image display step (step S20), and the cross-sectional direction after image reconstruction in the predetermined range is set. A tomographic image is displayed (post-reconstructed tomographic image displaying step, step S22). FIG. 31 shows a display image in a state where a tomographic image is displayed. This corresponds to the function of the post-reconstruction tomogram display unit 36 (post-reconstruction tomogram display means) shown in the functional block diagram 20 of FIG. In the resting collected image 103 shown in FIG. 31, a range (predetermined range) above and below (from the top of the head to the cerebellum) is set by appropriately operating the input operation unit 149 such as a mouse. The same range as the range set in the rest collection image 103 is automatically set in the collection image 104 when loaded. Next, when the Recon button 110 shown in FIG. 31 is clicked by the input operation unit 149 such as a mouse, the post-reconstruction tomographic image display unit 36 displays the image after reconstruction of the resting collected image 103 in the set range. A tomographic image 111 in the cross-sectional direction and a tomographic image 112 in the cross-sectional direction after image reconstruction of the loaded acquired image 104 in the set range are displayed as shown in FIG. As an image reconstruction method, an existing backprojection method, a high-speed successive approximation image formation method (Ordered Subset Expectation Maximization: OSEM), or the like may be used. The tomographic image 111 and the tomographic image 112 in the cross-sectional direction shown in FIG. 31 are cross-sectional views in the cross-sectional direction. After reconfiguration, the scroll bar 113 is appropriately dragged to the left and right by the input operation unit 149 such as a mouse. The tomographic image display unit 36 can switch and display the upper and lower slices from the top of the head to the cerebellum, that is, it can switch and display the wheel-cut view in the vertical direction. At this time, the tomographic images 111 and 112 are switched and displayed at the same time. It is preferable to use the function of displaying the wheel-cut drawing by scroll bar 113 by switching in the vertical direction in order to confirm a desired slice. If necessary, clicking the Reformat button 114 shown in FIG. 31 with the input operation unit 149 such as a mouse makes it possible to correct the angle in the forward tilt direction of the tomographic image 111 or the like.

  As shown in FIG. 23, the above-mentioned brain radioactivity count value of the subject subject to be input in the brain radioactivity count input step (step S12) is acquired in the multiple regression equation acquisition step (step S10). By applying the multiple regression equation 2, the radioactivity count value in the arterial blood that can be observed by 6-minute continuous arterial blood sampling in the microsphere method for the subject subject is estimated (estimation step, step S24). This corresponds to the function of the estimating unit 23 (estimating means) shown in the functional block diagram 20 of FIG.

  Subsequently, the target is based on the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step (step S12) and the radioactivity count value in the arterial blood estimated in the estimation step (step S24). The cerebral blood flow for the subject is calculated (cerebral blood flow calculation step, step S26). This corresponds to the function of the cerebral blood flow rate calculation unit 24 (cerebral blood flow rate calculation means) shown in the functional block diagram 20 of FIG. Finally, a cerebral blood flow (CBF) image based on the cerebral blood flow calculated in the cerebral blood flow calculation step (step S26) is displayed (cerebral blood flow image display step, step S28). This corresponds to the function of the cerebral blood flow image display unit 31 (cerebral blood flow image display means) shown in the functional block diagram 20 of FIG. FIG. 32 shows a display image for displaying a cerebral blood flow image. When the GO button 120 shown in FIG. 32 is clicked with the input operation unit 149 such as a mouse, the cerebral blood flow image display unit 31 uses the cerebral blood flow calculated by the cerebral blood flow calculation unit 23 to qualitatively. The tomographic images 111 and 112 based on the count value can be converted into quantitative images (CBF images) 121 and 122 based on quantitative values, respectively, and displayed. The quantitative images 121 and 122 shown in FIG. 32 are images showing the cerebral blood flow for the subject to be predicted by the cerebral blood flow calculation program of the present invention. The cerebral blood flow image display unit 31 switches the vertical slices (cross-sectional direction) slices of the quantitative images 121 and 122 by appropriately dragging the scroll bar 123 shown in FIG. 32 to the left and right with the input operation unit 149 such as a mouse. Can be displayed.

  FIG. 33 is a block diagram showing an internal circuit 140 of the computer 1 that executes the cerebral blood flow calculation program of the present invention. As shown in FIG. 33, the CPU 141, ROM 142, RAM 143, image control unit 145, controller 146, input control unit 148, and external I / F unit 150 are connected to a bus 151. In FIG. 33, the above-described cerebral blood flow rate calculation program of the present invention is recorded in a recording medium (including a removable recording medium) such as ROM 142, disk 10 or DVD or CD-ROM 147. On the disk 10, the above-described brain radioactivity count value recording unit 12, arterial blood radioactivity count value recording unit 14 and the like can be recorded. The cerebral blood flow calculation program is loaded into the RAM 143 from the ROM 142 via the bus 151 or from the recording medium such as the disk 10 or DVD or CD-ROM 147 via the controller 146 via the bus 151. The image control unit 145 displays image data of various display images displayed by the collected image display unit 32, the time / radioactivity curve display unit 34, the sagittal section direction collected image display unit 36, the cerebral blood flow image display unit 31, and the like. Is sent to the VRAM 144. The display device 2 displays the above data sent from the VRAM 144. The VRAM 144 is an image memory having a capacity corresponding to the data capacity of one screen of the display device 2. The input operation unit 149 is an input device such as a mouse and a keyboard for performing input, designation and the like on the computer. The input control unit 148 is connected to the input operation unit 149 and performs input control and the like. The external I / F unit 150 has an interface function when connecting to the outside of the computer (CPU) 141.

  As described above, the computer (CPU) 141 executes the cerebral blood flow calculation program of the present invention, thereby achieving the object of the present invention. The cerebral blood flow calculation program can be supplied to the computer (CPU) 141 in the form of a recording medium such as a DVD or a CD-ROM 147 as described above, and a recording such as a DVD or CD-ROM 177 on which the cerebral blood flow calculation program is recorded. The medium similarly constitutes the present invention. As a recording medium on which the cerebral blood flow rate calculation program is recorded, for example, a memory card, a memory stick, an optical disk, an FD, or the like can be used in addition to the recording medium described above.

In Example 6, another method for calculating the cerebral blood flow using the radioactivity count value obtained by a non-invasive technique will be described. It has the same technical significance in that a radioactivity count value obtained by a non-invasive technique is used. As shown in FIG. 1 (B) of Example 1, FIG. 18 (B) of Example 2, and FIG. 21 (B) of Example 3, the residual radioactivity due to the first ¹²³ I-IMP intravenous injection ( The background (indicated by a dotted line) is added to the radioactivity count value of the second ¹²³ I-IMP intravenous injection. Therefore, in order to correctly obtain the radioactivity count value by the second ¹²³ I-IMP intravenous injection, it is necessary to subtract the residual radioactivity (background) (take a difference). For this reason, it is important to accurately estimate the residual radioactivity (background).

FIG. 34 shows the problems of the conventional split administration protocol using ¹²³ I-IMP cerebral blood flow SPECT. As shown in FIG. 34, the first intravenous injection of ¹²³ I-IMP (111MBq) is first performed, and dynamic SPECT (1st. SPECT shown in FIG. 34 (B)) is performed. Next, after 8 minutes, Diamox (registered trademark) is intravenously injected. After 24.5 minutes, the second intravenous injection of ¹²³ I-IMP (111MBq) is performed, and dynamic SPECT (2nd.SPECT shown in FIG. 34 (B)) is performed. In FIG. 34 (B), the residual radioactivity (background) by the first ¹²³ I-IMP intravenous injection at this time is indicated by a dotted line. As shown in FIG. 34 (B), assuming that the radioactivity in the brain is y and the time is x, the conventional technique is 1st. It was estimated that the regression equation y = ax + b obtained by single regression from four points in SPECT represents the remaining radiation brain (single regression model). However, in practice, patient A as shown in FIG. 34B (a person who shows a residual radiation brain higher than the estimated value based on the single regression model), or conversely, patient B (residual radiation lower than the estimated value based on the single regression model). There is also a problem that the single regression model does not fit clinically because there is a person who shows the brain. In the regression equation of the single regression model, the residual radioactivity continues to rise, so there was a problem that the above difference was taken too much.

FIG. 35 shows a method for estimating the residual radioactivity used in Example 6 of the present invention in the split administration protocol using ¹²³ I-IMP cerebral blood flow SPECT. The protocol of the divided administration method is the same as the protocol described above shown in FIG. 1st. In SPECT and 2nd. It is assumed that the brain radioactivity count value measured every predetermined time for the subject during SPECT is recorded in the brain radioactivity count value recording unit 12 as in the above-described embodiments. 35, 2nd. Recorded in the brain radioactivity count value recording unit 12. From the second ¹²³ I-IMP intravenous injection to the predetermined elapsed time (24.5 minutes + x _p ) as the residual radioactivity count value by the first ¹²³ I-IMP intravenous injection in the brain radioactivity count value during SPECT 1st. A radioactivity count value (regression equation: y = ax + b) obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times (four points in FIG. 35) during SPECT is used. That is, after lapse of 24.5 minutes 24.5 minutes + _{x p} using a single regression model. The predetermined number is preferably 4 points from experience, but is not limited to 4 points. On the other hand, after a predetermined elapsed time (24.5 minutes + x _p ), it is estimated that a plateau (y = y _p ) is reached with a single-regressed radioactivity count value (y _p ) at the predetermined elapsed time (residual radiation Performance estimation means). The predetermined elapsed time (24.5 minutes + x _p ) is preferably 30 to 40 minutes empirically, and 35 minutes is particularly easy to obtain clinically good results.

The subsequent processing is the same as in the prior art, and the 2nd. Recorded in the brain radioactivity count value recording unit 12 is processed. The value obtained by subtracting the residual radioactivity count value estimated by the residual radioactivity estimation means from the intracerebral radioactivity count value in SPECT is 2nd. It is presumed to be the radioactivity count value in the brain by the second ¹²³ I-IMP intravenous infusion during SPECT (2nd. SPECT brain radioactivity count value estimation means). 2nd. SPECT estimated by SPECT brain radioactivity count value estimation means 2nd. Based on the intracerebral radioactivity count value of the second ¹²³ I-IMP intravenous injection during SPECT, the cerebral blood flow is calculated for the subject subject (cerebral blood flow calculation means). As the cerebral blood flow rate calculation means, for example, the relationship between the cerebral blood flow rate and the SPECT value is calculated in advance and a table (table) is obtained, and then the IMP− An ARG method or the like may be used.

FIG. 36 is a flowchart showing the process flow of the cerebral blood flow rate calculation program and method according to the sixth embodiment of the present invention. 36, 2nd. Recorded in the brain radioactivity count value recording unit 12. As a first ¹²³ I-IMP residual radioactivity count value of IV in brain radioactivity count in SPECT, from the second round ¹²³ I-IMP IV until a predetermined elapsed time 1st. The radioactivity count value obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times during SPECT is assumed, and after the predetermined elapsed time, a plateau is estimated at the radioactivity count value obtained by simple regression at the predetermined elapsed time. (Residual radioactivity estimation step. Step S30). Next, 2nd. Recorded in the brain radioactivity count value recording unit 12. The value obtained by subtracting the residual radioactivity count value estimated in the residual radioactivity estimation step (step S30) from the intracerebral radioactivity count value in SPECT is 2nd. It is presumed to be the brain radioactivity count value by the second ¹²³ I-IMP intravenous injection during SPECT (2nd. SPECT brain radioactivity count value estimation step, step S32). 2nd. The SPND brain radioactivity count value estimation step (step S32) estimated at 2nd. Based on the intracerebral radioactivity count value of the second ¹²³ I-IMP intravenous injection during SPECT, the cerebral blood flow is calculated for the subject subject (cerebral blood flow calculation step, step S34).

  The sixth embodiment of the present invention can also be described using a display image as in the fourth embodiment. Hereinafter, only some display images will be described for convenience. FIG. 37 shows a display image for displaying a cerebral blood flow image in the sixth embodiment of the present invention. 37, the same reference numerals as those in FIG. 32 of the fourth embodiment denote the same elements, and thus the description thereof is omitted. As shown in FIG. 32, the residual radioactivity is an approximate straight line 93 (in this case, a straight line based on a single regression model) until a predetermined elapsed time, and a plateau straight line 93P after the predetermined elapsed time. The result of taking the above-described difference is displayed as a time / radioactivity curve 94.

As described above, according to the sixth embodiment of the present invention, 2nd. From the second ¹²³ I-IMP intravenous injection to the predetermined elapsed time (24.5 minutes + x _p ) as the residual radioactivity count value by the first ¹²³ I-IMP intravenous injection in the brain radioactivity count value during SPECT 1st. A radioactivity count value (regression equation: y = ax + b) obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times (four points in FIG. 35) during SPECT is used. On the other hand, after a predetermined elapsed time (24.5 minutes + x _p ), it is estimated that a plateau (y = y _p ) is reached with a single-regressed radioactivity count value (y _p ) at the predetermined elapsed time. The predetermined elapsed time (24.5 minutes + x _p ) is preferably 30 to 40 minutes empirically, and 35 minutes is particularly easy to obtain clinically good results. As described above, it is possible to solve the problem of taking too much difference in the single regression model by estimating that the residual radioactivity becomes a plateau (y = y _p ) after a predetermined elapsed time (24.5 minutes + x _{p has} elapsed). And can be clinically combined.

  As an application example of the present invention, the present invention can be applied to the case where the calculation of cerebral blood flow is obtained by cerebral blood flow SPECT using a divided administration method.

DESCRIPTION OF SYMBOLS 1 Computer, 2 Display apparatus, 10 Recording apparatus, 12 Brain radioactivity count value recording part, 14 Arterial blood radioactivity count value recording part, 20 Functional block diagram, 21 Multiple regression type acquisition part, 22 Brain radioactivity count value Input section, 23 estimation section, 24 cerebral blood flow calculation section, 31 cerebral blood flow image display section, 32 collected image display section, 33 ROI setting section, 34 time / radioactivity curve display section, 35 sagittal section direction collected image display Part, 36 tomographic image display part after reconstruction, 40 file name, 41 OK button, 50 column, 51 acquired image, 60 ROI, elliptical ROI, 61 ROItype, 62 E: vein count, 70, 71, 90, 91 box, 72 GO button, 80 PLOT button, 81, 94 Time / radioactivity curve, 92 Linear Fit button, 93, 93P Approximate straight line 100 Start scroll bar, 101
Fin scroll bar, 102 Projection button, 103 Resting collected image, 104 Load collecting image, 105, 113, 123 Scroll bar, 110 Recon button, 111 Rest tomographic image after reconstruction of resting collected image 103 112, tomogram in the cross-sectional direction after image reconstruction of the acquired image 104 under load, 114 Reformat button, 120 GO button, 121, 122 quantitative image, 140 internal circuit, 141 CPU, 142 ROM, 143 RAM, 144 VRAM, 145 Image control unit, 146 controller, 147 CD-ROM, 148 input control unit, 149 input operation unit, 150 external I / F unit, 151 bus, 160 cylindrical phantom, 162 SPECT equipment, 164 test tube, 166 well type Inflation counter.

Edited by Tsunehiko Nishimura, “Revised Latest Brain SPECT / PET Clinical—Testing Methods for Brain Function”, published on June 20, 2002, Medical View Inc. Edited by Tsunehiko Nishimura, “SPECT Functional Image Quantification Fundamentals and Clinics”, issued July 10, 1999, Medical View Inc. K. Kuda, M. Furudate, Y. Sasaki, K. Konishi, “Latest Clinical Nuclear Medicine (Revised 2nd Edition)”, published February 10, 1994 (4th edition), Kanehara Publishing Co., Ltd.

Claims (15)

A cerebral blood flow calculation program for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. A brain radioactivity count value recording unit in which a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject during SPECT are recorded, and the subject is observed by continuous arterial blood sampling for a predetermined time in a microsphere method. Arterial blood radioactivity count value recording section in which the arterial blood radioactivity count value is recorded, and a computer,
A plurality of intracerebral radioactivity count values recorded in the intracerebral radioactivity count value recording unit are used as explanatory variables, and the radioactivity count value in arterial blood recorded in the arterial blood radioactivity count value recording unit is set as a target variable. Multiple regression equation acquisition means for acquiring a multiple regression equation having a partial regression coefficient obtained by multiple regression analysis
1st. Intracerebral radioactivity count value input means for inputting a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject subject during SPECT,
Applying the intracerebral radioactivity count value input by the intracerebral radioactivity count value input means to the multiple regression equation acquired by the multiple regression equation acquisition means, thereby providing a predetermined time in the microsphere method for the subject subject. An estimation means for estimating a radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling;
Applying the intracerebral radioactivity count value input by the intracerebral radioactivity count value input means and the radioactivity count value in arterial blood estimated by the estimation means to the cerebral blood flow calculation formula by the IMP / microsphere model method the cerebral blood flow rate calculation program for functioning as a cerebral blood flow rate calculation means for calculating a cerebral blood flow of a subject of interest.
  The cerebral blood flow calculation program according to claim 1, wherein the sample obtained by one-point venous blood sampling after a predetermined time has elapsed after the first administration of a predetermined tracer as an explanatory variable of the multiple regression analysis in the multiple regression equation acquiring means. A cerebral blood flow calculation program characterized by further using a radioactivity count value.   The cerebral blood flow calculation program according to claim 1, wherein the sample obtained by single-point arterial blood sampling after a predetermined time has elapsed after the first administration of a predetermined tracer as an explanatory variable of the multiple regression analysis in the multiple regression equation acquiring means. A cerebral blood flow calculation program characterized by further using a radioactivity count value.   The cerebral blood flow rate calculation program according to any one of claims 1 to 3, wherein the 1st. The plurality of brain radioactivity count values measured at predetermined time intervals for subjects in SPECT are relative values based on the brain radioactivity count values measured at the second administration of the predetermined tracer. A program for calculating cerebral blood flow.   The cerebral blood flow calculation program according to any one of claims 1 to 4, further comprising a cerebral blood flow image display means for displaying a cerebral blood flow image based on the cerebral blood flow calculated by the cerebral blood flow calculation means. A program for calculating cerebral blood flow. In the cerebral blood flow calculation program according to any one of claims 1 to 5,
Collected image display means for displaying a collected image based on the brain radioactivity count value input by the brain radioactivity count value input means;
ROI setting means for setting a predetermined shape on the collected image displayed by the collected image display means, and setting an ROI based on a predetermined threshold in the predetermined shape;
Time / radioactivity for displaying a time / radioactivity curve based on the intracerebral radioactivity count value input by the intracerebral radioactivity count value input means within the ROI set by the ROI setting means or in an arbitrary region A curve display means;
Sagittal section direction collected image display means for setting a predetermined range on the time / radioactivity curve displayed by the time / radioactivity curve display means and displaying a collected image in the sagittal section direction in the predetermined range; ,
Reconstructed tomographic image display for setting a predetermined range using the acquired image displayed by the sagittal cross-sectional direction acquired image display means and displaying a tomographic image in the cross-sectional direction after image reconstruction in the predetermined range And a cerebral blood flow rate calculation program. A cerebral blood flow calculation program for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method. In SPECT and 2nd. Using the brain radioactivity count value recording unit in which the brain radioactivity count value measured every predetermined time for the subject during SPECT is recorded, the computer,
2nd. Recorded in the brain radioactivity count value recording unit. The remaining radioactivity count value by the first intravenous injection in the brain radioactivity count value during SPECT is 1st. From the second intravenous injection to a predetermined elapsed time. The radioactivity count value obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times during SPECT is assumed, and after the predetermined elapsed time, a plateau is estimated at the radioactivity count value obtained by simple regression at the predetermined elapsed time. Residual radioactivity estimation means,
2nd. Recorded in the brain radioactivity count value recording unit. The value obtained by subtracting the residual radioactivity count value estimated by the residual radioactivity estimation means from the intracerebral radioactivity count value in SPECT is 2nd. Estimated as the brain radioactivity count value by the second intravenous injection during SPECT. 2nd. SPECT brain radioactivity count estimation means,
2nd. SPECT estimated by SPECT brain radioactivity count value estimation means 2nd. A cerebral blood flow calculation program for functioning as a cerebral blood flow calculation means for calculating cerebral blood flow for a subject subject based on the intracerebral radioactivity count value of the second intravenous injection during SPECT.   A computer-readable recording medium in which the cerebral blood flow rate calculation program according to claim 1 is recorded. A cerebral blood flow calculation method for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method, comprising: A brain radioactivity count value recording unit in which a plurality of intracerebral radioactivity count values measured at predetermined time intervals for a subject during SPECT are recorded, and the subject is observed by continuous arterial blood sampling for a predetermined time in a microsphere method. And the arterial blood radioactivity count value recording part in which the radioactivity count value in the arterial blood is recorded,
A plurality of intracerebral radioactivity count values recorded in the intracerebral radioactivity count value recording unit are used as explanatory variables, and the radioactivity count value in arterial blood recorded in the arterial blood radioactivity count value recording unit is set as a target variable. A multiple regression equation obtaining step for obtaining a multiple regression equation having a partial regression coefficient obtained by multiple regression analysis;
1st. Intracerebral radioactivity count value input step for inputting a plurality of intracerebral radioactivity count values measured every predetermined time for a subject subject in SPECT;
By applying the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step to the multiple regression equation acquired in the multiple regression equation acquisition step, a predetermined time in the microsphere method for the subject subject An estimation step for estimating a radioactivity count value in arterial blood that can be observed by continuous arterial blood sampling;
Applying the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step and the radioactivity count value in arterial blood estimated in the estimation step to the cerebral blood flow calculation formula by the IMP / microsphere model method the cerebral blood flow rate calculation method is characterized in that a cerebral blood flow rate calculation step of calculating a cerebral blood flow of a subject of interest.   10. The method for calculating cerebral blood flow according to claim 9, wherein, as an explanatory variable of the multiple regression analysis in the multiple regression equation obtaining step, a sample obtained by one-point vein blood collection after a predetermined time has elapsed after the first administration of a predetermined tracer. A method for calculating cerebral blood flow, further comprising using a radioactivity count value.   10. The method for calculating cerebral blood flow according to claim 9, wherein an explanatory variable of the multiple regression analysis in the multiple regression equation acquisition step is a sample obtained by one-point arterial blood sampling after a predetermined time has elapsed after the first administration of a predetermined tracer. A method for calculating cerebral blood flow, further comprising using a radioactivity count value.   12. The method for calculating cerebral blood flow according to claim 9, wherein the 1st. The plurality of brain radioactivity count values measured at predetermined time intervals for subjects in SPECT are relative values based on the brain radioactivity count values measured at the second administration of the predetermined tracer. A method of calculating cerebral blood flow characterized by the above.   The cerebral blood flow calculation method according to any one of claims 9 to 12, further comprising a cerebral blood flow image display step for displaying a cerebral blood flow image based on the cerebral blood flow calculated in the cerebral blood flow calculation step. A method of calculating cerebral blood flow characterized by the above. The cerebral blood flow calculation method according to any one of claims 9 to 13, wherein after the intracerebral radioactivity count value input step,
A collected image display step for displaying a collected image based on the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step;
A ROI setting step for setting a predetermined shape on the collected image displayed in the collected image display step, and setting an ROI based on a predetermined threshold in the predetermined shape;
Time / radioactivity for displaying a time / radioactivity curve based on the intracerebral radioactivity count value input in the intracerebral radioactivity count value input step in the ROI set in the ROI setting step or in an arbitrary region A curve display step;
Sagittal section direction collection image display step for setting a predetermined range on the time / radioactivity curve displayed in the time / radioactivity curve display step and displaying a collected image in the sagittal section direction in the predetermined range; ,
Reconstructed tomographic image display for setting a predetermined range using the acquired image displayed in the sagittal cross-sectional direction acquired image display step and displaying a tomographic image in the cross-sectional direction after image reconstruction in the predetermined range And a step of calculating cerebral blood flow. A cerebral blood flow calculation method for calculating cerebral blood flow by cerebral blood flow SPECT using a divided administration method, comprising: In SPECT and 2nd. Using the brain radioactivity count value recording unit in which the brain radioactivity count value measured every predetermined time for the subject during SPECT is recorded,
2nd. Recorded in the brain radioactivity count value recording unit. The remaining radioactivity count value by the first intravenous injection in the brain radioactivity count value during SPECT is 1st. From the second intravenous injection to a predetermined elapsed time. The radioactivity count value obtained by simple regression from the radioactivity count value in the brain at a predetermined number of elapsed times during SPECT is assumed, and after the predetermined elapsed time, a plateau is estimated at the radioactivity count value obtained by simple regression at the predetermined elapsed time. A residual radioactivity estimation step;
2nd. Recorded in the brain radioactivity count value recording unit. A value obtained by subtracting the residual radioactivity count value estimated in the residual radioactivity estimation step from the intracerebral radioactivity count value in SPECT is 2nd. Estimated as the brain radioactivity count value by the second intravenous injection during SPECT. 2nd. SPECT brain radioactivity count estimation step;
2nd. 2nd. Estimated in the SPECT brain radioactivity count estimation step. A cerebral blood flow rate calculation method comprising: a cerebral blood flow rate calculation step for calculating a cerebral blood flow rate for a subject subject based on the intracerebral radioactivity count value of the second intravenous injection during SPECT.