JP6677975B2 - Medical image diagnostic apparatus and medical image processing apparatus - Google Patents

Description

  An embodiment of the present invention relates to a medical image diagnostic device and a medical image processing device.

  In recent years, medical image diagnostic apparatuses have made remarkable developments. Examples of the medical image diagnostic apparatus include an ultrasonic diagnostic apparatus, an X-ray diagnostic apparatus, an X-ray CT apparatus, a nuclear medicine diagnostic apparatus, and an MRI apparatus. According to the medical image diagnostic apparatus, it is possible to measure the inside of the subject without performing a surgical procedure.

  Among medical image diagnostic apparatuses, for example, an ultrasonic diagnostic apparatus transmits ultrasonic waves generated from an ultrasonic probe to a subject. The ultrasonic diagnostic apparatus obtains a so-called reception signal for a scanning line by performing a reception process on a reflection signal reflected by the subject. The ultrasonic diagnostic apparatus acquires image data based on the acquired reception signal.

  By the way, the ultrasonic diagnostic apparatus has a mode in which image data for each scanning line is acquired as line data, and the acquired line data is arranged in time series to express a temporal change in the scanning line. In this type of mode, data between line data acquired at predetermined time intervals is interpolated by a predetermined interpolation process. However, when observing a fast-moving target, the interpolation process does not function effectively, and the target sometimes becomes a discontinuous image in the time direction.

  In this way, when data interpolation between two line data is attempted, a phenomenon in which suitable interpolation data cannot be obtained can occur not only in the ultrasonic diagnostic apparatus but also in other medical image diagnostic apparatuses. .

JP 2005-58587 A

  Therefore, an object is to obtain suitable interpolation data between two line data.

  According to the embodiment, the medical image diagnostic apparatus includes a scanning unit, an image data generation unit, and an interpolation processing unit. The scanning unit scans the inside of the subject. The image data generating section generates first line data and second line data which are linear image data based on a scanning result by the scanning section. The interpolation processing unit is configured to interpolate between the first line data and the second line data by using an interpolation data generation method determined according to the similarity between the first line data and the second line data. Generates interpolation data.

FIG. 2 is a block diagram illustrating a functional configuration of the ultrasonic diagnostic apparatus according to the first embodiment. FIG. 2 is a diagram illustrating a functional configuration of an image generating unit illustrated in FIG. 1. FIG. 3 is a diagram showing a flex M mode image and a B mode image displayed to generate the flex M mode image. FIG. 4 is a diagram illustrating an example of similarity determination by the index value calculation unit illustrated in FIG. 3. FIG. 4 is a diagram illustrating an example of a first interpolation process performed by the interpolation processing unit illustrated in FIG. 3. FIG. 4 is a diagram illustrating an example of similarity determination by the index value calculation unit illustrated in FIG. 3. FIG. 4 is a diagram illustrating an example of similarity determination by the index value calculation unit illustrated in FIG. 3. FIG. 4 is a diagram illustrating an example of a second interpolation process based on an optical flow by the interpolation processing unit illustrated in FIG. 3. FIG. 4 is a diagram illustrating an example of a second interpolation process based on a path framework by the interpolation processing unit illustrated in FIG. 3. FIG. 2 is a diagram illustrating an example of a flowchart illustrating an operation in a flex M mode of the ultrasonic diagnostic apparatus illustrated in FIG. 1. FIG. 4 is a diagram showing a flex M-mode image created by interpolating data by the method according to the first embodiment. FIG. 4 is a diagram showing a flex M-mode image created by interpolating data by the nearest neighbor method. FIG. 3 is a diagram illustrating a flex M-mode image created by interpolating data by a one-dimensional linear interpolation method. FIG. 14A is a diagram showing a flex M mode image created by interpolating data by the nearest neighbor method, and FIG. 14B is a flex M mode image created by interpolating data based only on the optical flow. FIG. FIG. 15A is a diagram showing a flex M mode image created by interpolating data by the nearest neighbor method, and FIG. 15B is a flex M mode image created by interpolating data based only on the optical flow. FIG. 15C is a diagram showing a flex M-mode image created by interpolating data by the method according to the first embodiment. FIG. 2 is a diagram illustrating a functional configuration of the medical image processing apparatus according to the first embodiment. FIG. 3 is a diagram illustrating another functional configuration of the ultrasonic diagnostic apparatus according to the first embodiment. FIG. 9 is a diagram illustrating a functional configuration of an image generating unit according to a second embodiment. It is a figure showing the example of the flow chart which shows operation in flex M mode of the ultrasonic diagnostic equipment concerning a 2nd embodiment. It is a figure showing the flow chart which shows other examples of operation in the flex M mode of the ultrasonic diagnostic equipment concerning a 2nd embodiment. It is a figure showing the functional composition of the medical image processing device concerning a 2nd embodiment. It is a figure showing other functional composition of the ultrasonic diagnostic equipment concerning a 2nd embodiment. It is a block diagram showing composition of a medical image diagnostic device which performs line data interpolation processing concerning a 1st or 2nd embodiment.

  Hereinafter, embodiments will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a block diagram illustrating an example of a functional configuration of the ultrasonic diagnostic apparatus 10 according to the first embodiment. The ultrasonic diagnostic apparatus 10 shown in FIG. 1 includes an apparatus main body 11, an ultrasonic probe 12, an input unit 13, and a display unit 14. In addition, the ultrasonic diagnostic apparatus 10 is connected to a biological signal measuring unit (not shown) represented by an electrocardiograph, a phonograph, a pulse wave meter, and a respiratory sensor, and a network via an interface unit 116. Is also good.

  The ultrasonic probe 12 includes a plurality of piezoelectric vibrators as acoustic / electric reversible conversion elements such as piezoelectric ceramics. The plurality of piezoelectric vibrators are arranged in parallel and mounted on the tip of the ultrasonic probe 12. Each piezoelectric vibrator generates an ultrasonic wave in response to a drive signal supplied from a transmission / reception unit 111 described later. In addition, each piezoelectric vibrator receives a reflected wave from the scanned area of the subject and generates an echo signal. Note that the ultrasonic probe 12 is detachably connected to the apparatus main body 11.

  The input unit 13 includes, for example, a mouse, a keyboard, a button, a panel switch, a touch command screen, a foot switch, a trackball, a joystick, and the like. The input unit 13 is connected to the apparatus main body 11 and takes in various instructions, commands, and information from the operator into the apparatus main body 11.

  The display unit 14 displays a GUI (Graphical User Interface) for the operator of the ultrasonic diagnostic apparatus to input various setting requests using the input unit 13, and displays ultrasonic image data generated in the apparatus main body 11. Monitor and display.

  The apparatus main body 11 includes a transmission / reception unit 111, a B-mode data generation unit 112, a Doppler data generation unit 113, an image generation unit 114, a storage unit 115, an interface unit 116, and a control unit 117.

  The transmission / reception unit 111 controls transmission / reception of ultrasonic waves by the ultrasonic probe 12 based on an instruction from the control unit 117 described later. The transmission / reception unit 111 includes a pulse generator, a transmission delay circuit, a pulser, and the like, and supplies a driving signal to the ultrasonic probe 12. The pulse generator repeatedly generates a rate pulse for forming a transmission ultrasonic wave at a predetermined repetition frequency (PRF: Pulse Repetition Frequency). The transmission delay circuit focuses the ultrasonic waves generated from the ultrasonic probe 12 in a beam shape, and determines the delay time for each piezoelectric vibrator required to determine the transmission directivity by each rate generated by the pulse generator. Give to the pulse. The pulser applies a drive signal to the ultrasonic probe 12 at the timing when receiving the rate pulse from the transmission delay circuit. Thereby, the ultrasonic beam is transmitted to the subject.

  The transmission / reception unit 111 includes an amplifier circuit, an analog-digital converter, a reception delay circuit, an adder, and the like. The amplifier circuit amplifies the echo signal for each channel. The analog-to-digital converter converts the amplified echo signal into a digital signal. The reception delay circuit gives a reception delay time necessary for determining the reception directivity to the echo signal converted into the digital signal. The adder adds the echo signals given the reception delay time. By this addition processing, a reflected component from a direction corresponding to the reception directivity is emphasized, and a reception beam is formed (a so-called “ultrasonic scanning line” is determined by the reception directivity). The transmission / reception unit 111 generates a reception signal for each depth for each scanning line in the scanned area.

  The B-mode data generation unit 112 and the Doppler data generation unit 113 are signal processing units that perform various kinds of signal processing on the reception signal output from the transmission / reception unit 111.

  The B-mode data generation unit 112 is realized by, for example, a memory and a predetermined processor, and includes an envelope detector, a logarithmic converter, and the like (not shown). The envelope detector performs an envelope detection on the received signal output from the transmission / reception unit 111. The logarithmic converter performs logarithmic conversion of the amplitude of the detection signal to relatively emphasize a weak signal. Through these processes, B-mode data for each of a plurality of different scanning lines, in which the intensity of the reflected wave is represented by luminance, is generated. The B-mode data generator 112 outputs the generated B-mode data to the image generator 114.

  The Doppler data generation unit 113 is realized by, for example, a memory and a predetermined processor, and generates Doppler data from a reception signal output from the transmission / reception unit 111. For example, the Doppler data generation unit 113 calculates an average velocity value, a variance value, a power value, and the like of the blood flow based on the Doppler signals for multiple points. The Doppler data generation unit 113 generates color Doppler data and power Doppler data from the average velocity value, variance value, power value, and the like of the blood flow based on the Doppler signals at multiple points. Further, in the Doppler spectrum mode, the Doppler data generator 113 generates Doppler spectrum data by frequency-analyzing a reception signal from a certain observation point output from the transmission / reception unit 111. Here, the Doppler spectrum mode includes a continuous wave Doppler mode in which ultrasonic transmission / reception is continuously performed on one scanning line and the obtained received signal is subjected to frequency analysis, and an observation mode arranged in a blood vessel portion in an image. There is a pulse Doppler mode in which ultrasonic transmission / reception for a point is intermittently performed and the obtained received signal is subjected to frequency analysis. In the present embodiment, the color Doppler data, the power Doppler data, and the Doppler spectrum data (that is, the data that is the source of the color Doppler image, the power Doppler image, and the Doppler spectrum image) are collectively referred to as “Doppler data”. .

  The image generation unit 114 is realized by, for example, a memory and a predetermined processor, and generates two-dimensional B-mode image data from the B-mode data regarding a plurality of different scanning lines generated by the B-mode data generation unit 112.

  The image generating unit 114 generates color Doppler image data from the color Doppler data generated by the Doppler data generating unit 113. The color Doppler image data is speed image data, dispersed image data, power image data, or image data obtained by combining these.

  In addition, the image generation unit 114 generates one linear spectrum image data from the Doppler spectrum data generated by the Doppler data generation unit 113 for a predetermined observation point. The image generating unit 114 generates Doppler spectrum image data by arranging the spectrum image data in time series.

  Further, the image generation section 114 generates M-mode image data by arranging the B-mode data on one predetermined scanning line generated by the B-mode data generation section 112 in a time series.

  In addition, the image generation unit 114 calculates an index value regarding the similarity of each object between temporally adjacent line data generated in the flex M mode. Further, the image generating unit 114 performs a first interpolation process of interpolating only data in the time direction between the line data temporally adjacent to each other in accordance with the calculated index value, and a first interpolation process in the depth direction and the time direction. A line data interpolation process for interpolating data between a plurality of temporally adjacent line data is executed by at least one of the second interpolation process for interpolating data, and a flex M-mode image is generated.

  The storage unit 115 is configured by a magnetic disk, a magneto-optical disk, a semiconductor memory, or the like, and is generated by a plurality of data groups such as a plurality of reception delay patterns having different focus depths, diagnostic protocols, transmission and reception conditions, and a B-mode data generation unit 112. B-mode data, color Doppler data and Doppler spectrum data generated by the Doppler data generator 113, and B-mode image data, color Doppler image data, Doppler spectrum image data, and M-mode image generated by the image generator 114. Data and flex M mode image data are stored.

  The interface unit 116 serves as an interface for the input unit 13, a network, an external storage device (not shown), and a biological signal measurement unit. Image data and analysis results obtained by the ultrasonic diagnostic apparatus 10 can be transferred to another apparatus via a network connected by the interface unit 116.

  The control unit 117 includes, for example, a processor, and a storage circuit of a program and data for a processor such as a ROM (Read Only Memory) and a RAM (Random Access Memory) to execute processing. The control unit 117 controls the overall operation of the ultrasonic diagnostic apparatus 10 by causing the processor to execute a control program of the ultrasonic diagnostic apparatus 10 stored in the ROM using the RAM. Specifically, the control unit 117 transmits and receives various requests input from the input unit 13 by the operator, such as a mode selection request, an ROI setting request, a reception delay pattern list selection request, and a transmission start / end request. The processing of the unit 111, the B-mode data generation unit 112, the Doppler data generation unit 113, and the image generation unit 114 is controlled. Further, when a request to generate a flex M-mode image is input from the input unit 13 by the operator, the control unit 117 causes the processor to execute the medical image processing program stored in the ROM using the RAM. Controls the image generation unit 114.

  The control unit 117 controls the display unit 14 to display the B mode image data, the color Doppler image data, the Doppler spectrum image data, the M mode image data, the flex M mode image data, and the like stored in the storage unit 115. I do. The control unit 117 controls the storage unit 115 to store the B mode image data, the color Doppler image data, the Doppler spectrum image data, the M mode image data, the flex M mode image data, and the like generated by the image generation unit 114. I do.

(Line data interpolation processing)
Next, the line data interpolation processing of the ultrasonic diagnostic apparatus according to the present embodiment will be described. The line data interpolation process includes a first interpolation process of interpolating only data in the time direction between line data that are temporally adjacent to each other in accordance with a calculated index value, and a data process in the depth direction and the time direction. An image is generated by executing an interpolation processing unit that interpolates data between a plurality of line data that are temporally adjacent to each other by at least one of a second interpolation process that interpolates the image data. This line data interpolation processing is executed in the image generation unit 114.

  In the following, for the sake of specific description, a case where the present line data interpolation processing is applied to the flex M mode will be described as an example. However, as will be described later, the present line data interpolation processing is not limited to application to the flex M mode.

  FIG. 2 is a diagram illustrating a configuration of the image generation unit 114. As shown in the figure, the image generator 114 includes an image data generator 1141, an index value calculator 1142, and an interpolation processor 1143.

  The image data generation unit 1141 generates two-dimensional B-mode image data from the B-mode data regarding the plurality of different scanning lines generated by the B-mode data generation unit 112. When an operator draws an arbitrary line (line: an arbitrary straight line or an arbitrary curve) on the B-mode image displayed on the display unit 14 via the input unit 13, the image data generation unit 1141 Image data (line data) on the drawn line is generated (read) and arranged in time series.

  In the following description, in order to make the description concrete, an example is shown in which the operator draws an arbitrary straight line on the B-mode image as shown on the right side of FIG. Therefore, in the present embodiment, the B-mode data on the line drawn by the operator is arranged in chronological order, and an interpolation process (described later) according to an index value for determining similarity is executed, thereby obtaining FIG. Will be generated as shown on the left side of FIG.

  The index value calculation unit 1142 calculates an index value for determining similarity for at least one target portion (target) on the line data using a plurality of line data that are temporally adjacent to each other. As the index value for determining the similarity, for example, a correlation coefficient, a covariance, an absolute value thereof, or a combination of a plurality of them can be adopted. In the present embodiment, using two line data that are temporally adjacent to each other, each position on the line data is set as a target part, and an index value for determining similarity is set as a covariance value, which will be described later. It is assumed that the calculation is performed for each target part by the technique.

  The interpolation processing unit 1143 performs an interpolation process (first interpolation process: for example, a linear interpolation process) based on only the time direction for each target on the line data according to the calculated index value, and a depth direction and a time An interpolation path between temporally adjacent line data is determined by at least one of direction-based interpolation processing (second interpolation processing: for example, interpolation processing based on a path framework, optical flow, or the like). I do. Further, the interpolation processing unit 1143 performs predetermined interpolation processing (for example, nearest neighbor interpolation, linear interpolation, polynomial interpolation, spline interpolation, other non-linear interpolation processing, etc.) on each pixel existing on the determined interpolation path. Determine the pixel value.

  Next, the line data interpolation processing executed by the image generation unit 114 will be described with reference to FIGS.

  FIGS. 4 and 5 are diagrams for explaining a case where a first interpolation process based only on the time direction is performed for each target on the line data in accordance with an index value for determining similarity. It is. When the line data is arranged in chronological order by the image data generation unit 1141, the index value calculation unit 1142, as shown in FIG. , The target window TWi is set to the older line data in time (referred to as “line data A” in this embodiment). The target window TWi is set to have a predetermined size in the depth direction so as to include the target pixel TPi as a target of similarity determination on the one line data and pixels in the vicinity thereof. In the present embodiment, as shown in FIGS. 4 to 9, a target window TWi including a total of three pixels in the depth direction is set around the target pixel TPi. However, the size of the target window TWi is not limited to the example, and may be, for example, an arbitrary size (typically, 2N + 1 pixels (N is a natural number) around the target pixel TPi) so as to include the target pixel TPi. Can be set. Further, the size of the target window can be set according to the characteristics of the data, that is, the tissue structure of the subject and the depth of the subject. Further, the size of the target window may be automatically changed according to the characteristics of the data and the depth of the subject, or may be changed according to the input from the input unit 13 by the operator.

  Further, the index value calculation unit 1142 outputs the other line data of the temporally continuous line data (typically, the line data which is newer temporally. In this embodiment, this is referred to as “line data B”). In, the sample window SWi is set at the same position (depth) as the target window TWi. The sample window SWi is set to have the same size as the target window TWi on the other line data B.

  The index value calculation unit 1142 first uses each pixel value in the target window TWi on the line data A and each pixel value in the sample window SWi (having the same depth as the target window TWi) on the line data B. Calculate the index value (covariance value). The index value calculation unit 1142 determines whether or not the index value exceeds a preset threshold value (in the present embodiment, for example, the absolute value of the covariance value is set to 255). In the example of FIG. 4, the calculated covariance value is 3272, which is larger than the absolute value 255 which is a threshold value. Therefore, the index value calculation unit 1142 determines that the similarity between each pixel in the target window TWi and each pixel in the sample window SWi is high, and determines a pixel (corresponding pixel) corresponding to the target pixel TPi in the sample window SWi. ) Specify CPi.

  The interpolation processing unit 1143 determines an interpolation path based on the target pixel TPi and the corresponding pixel CPi. In this case, the target pixel TPi and the corresponding pixel CPi are located at the same depth. Therefore, as illustrated on the left side of FIG. 5, the interpolation processing unit 1143 determines the interpolation path based on only the time direction with the target pixel TPi and the corresponding pixel CPi as end points.

  Further, the interpolation processing unit 1143 uses the pixel value “255” of the target pixel TPi on the line data A and the pixel value “200” of the corresponding pixel CPi on the line data B to obtain the target pixel TPi and the corresponding pixel CPi. And interpolate the value of at least one pixel between.

  In the example of FIG. 5, for example, the following linear interpolation is performed on pixels A to C between the target pixel TPi and the corresponding pixel CPi.

Brightness value of pixel A = 255-1 / 4 * (255-200)
Brightness value of pixel B = 255-2 / 4 * (255-200)
Brightness value of pixel C = 255-3 / 4 * (255-200)
Here, 4 means the number of interpolation lines plus one.

  FIGS. 6 to 9 illustrate a case in which a second interpolation process is performed for each target on the line data, based on the index value for determining the similarity, based on the depth direction and the time direction. FIG. The index value calculation unit 1142 calculates each pixel value in the target window TWi set on the line data A shown in FIG. 6 and the sample window SWi set on the line data B (at the same depth as the target window TWi). It is determined whether or not each pixel value exceeds the value of 5). In the example of FIG. 6, the calculated covariance value is 103, and an index value is calculated. Since the index value calculation unit 1142 determines that the index value is smaller than a preset threshold value (the absolute value 255 which is a threshold value of 25), the index value calculation unit 1142 determines whether the target pixel TPi in the target window TWi and the sample window SWi It is determined that the similarity with the pixel corresponding to the target pixel TPi is low.

  When determining that the similarity is low, the index value calculation unit 1142 moves the position of the sample window SWi on the line data B by one pixel in the depth direction while keeping the target window TWi on the line data A fixed, and A sample window SWi + 1 is set, and the above-described index value is calculated between the target window TWi and the sample window SWi + 1. Thereafter, the index value calculation unit 1142 sequentially moves the sample windows SWi on the line data B to a predetermined depth while keeping the target window TWi on the line data A fixed, and sets each sample window SWi set for each depth. The index value for each depth is calculated using the target window TWi. FIG. 7 shows an example of index values calculated for each depth when the sample window SW is moved from SWi to SWi + 7. However, there is no limitation on the depth at which the sample window SW is moved without being limited to the example of FIG.

  When each index value up to the predetermined depth is obtained, the index value calculation unit 1142 specifies the position of the sample window SW where the value is maximum among the calculated index values for each depth, and specifies the specified sample. The pixel corresponding to the target pixel TPi in the window SW is specified. In FIG. 7, the pixel corresponding to the target pixel TPi in the sample window SWi + 4 corresponding to the covariance 3272 (that is, the pixel located at the center in the sample window SWi + 4) is specified as the corresponding pixel CPi + 4 of the target pixel TPi.

  The interpolation processing unit 1143 determines an interpolation path based on the target pixel TPi and the corresponding pixel CPi + 4. In this case, the corresponding pixel CPi + 4 has moved by 4 pixels in the depth direction with respect to the target pixel TPi. Therefore, as illustrated on the left side of FIG. 8, the interpolation processing unit 1143 determines the interpolation path based on the time direction and the depth direction with the target pixel TPi and the corresponding pixel CPi + 4 as end points. For determining the interpolation path based on the time direction and the depth direction, for example, a path framework, an optical flow, or the like can be adopted. Here, the path framework is a method of starting from a point to be interpolated, and using a corresponding pixel in preceding and following line data to determine a path including the point to be interpolated. The optical flow is a method of determining a line (two-dimensional vector) connecting corresponding pixels in the preceding and following line data as a path. Which interpolation process is used is set in advance according to the characteristics of the data. The interpolation path determined based on the optical flow is shown on the left side of FIG. 8, and the interpolation path determined based on the path framework is shown on the left side of FIG. 9, respectively.

  The interpolation processing unit 1143 uses the pixel value “255” of the target pixel TPi on the line data A and the pixel value “200” of the corresponding pixel CPi + 4 on the line data B to calculate the target pixel TPi and the corresponding pixel CPi + 4. Interpolate the value of at least one pixel between.

  In the example of FIG. 8, for example, the following linear interpolation is executed between the target pixel TPi and the corresponding pixel CPi + 4 for the pixels A to C on the interpolation path determined based on the optical flow.

Brightness value of pixel A = 255-1 / 4 * (255-200)
Brightness value of pixel B = 255-2 / 4 * (255-200)
Brightness value of pixel C = 255-3 / 4 * (255-200)
Here, 4 means the number of interpolation paths plus one.

  In the example of FIG. 9, for the pixels A to C on the interpolation path determined based on the path framework between the target pixel TPi and the corresponding pixel CPi + 4, for example, the luminance value of the pixel A: 100, the pixel The luminance value of B: 228 and the luminance value of pixel C: 90 are calculated.

(Operation in Flex M mode)
Next, an operation in the flex M mode using the above-described line data interpolation processing will be described. FIG. 10 is a diagram showing an example of a flowchart showing the operation of the present ultrasonic diagnostic apparatus in the flex M mode.

  First, the image data generation unit 1141 included in the image generation unit 114 generates time-series line data for a line drawn on the B-mode image by the operator via the input unit 13. The image generation unit 114 performs the following processing on the updated line data.

  First, the index value calculation unit 1142 included in the image generation unit 114 sets the pixel at the shallowest position in the predetermined area set in the line data A as the target pixel TPi as a processing target point, and A target window TWi having a predetermined size including TPi is set on the line data A (step S101).

  In addition, the index value calculation unit 1142 sets a sample window SWi of the same size at a position corresponding to the target window TWi on the line data A in the line data B. The index value calculation unit 1142 calculates an index value for determining the similarity between a plurality of pixels included in the target window and a plurality of pixels (a plurality of samples) included in the sample window by the method described above. (Step S102).

  The index value calculation unit 1142 determines whether the calculated index value exceeds a preset threshold value (255) (Step S103). If it exceeds (Yes in step S103), the index value calculation unit 1142 determines that the similarity between the plurality of pixels included in the target window TWi on the line data A and the plurality of pixels included in the sample window SWi on the line data B is determined. It is determined to be high, and the fact that the similarity is high is notified to the interpolation processing unit 1143. Upon receiving the notification that the similarity is high, the interpolation processing unit 1143 specifies a pixel at the same position in the sample window SWi on the line data B as the target pixel TPi as a corresponding pixel CPi. Further, the interpolation processing unit 1143 determines an interpolation path based only on the time direction with the target pixel TPi on the line data A and the corresponding pixel CPi on the line data B as end points, and determines the pixel value of the target pixel TPi and Using the pixel value of the corresponding pixel CPi, the value of each pixel between the target pixel TPi and the corresponding pixel CPi is interpolated by, for example, linear interpolation (step S104).

  On the other hand, when the calculated index value does not exceed the preset threshold value (No in step S103), the index value calculation unit 1142 calculates a plurality of pixels included in the target window TWi on the line data A and a sample on the line data B. It is determined that the similarity with a plurality of pixels included in the window SWi is low, the sample window SWi is sequentially moved while the target window TWi is fixed, and an index value at each depth up to a predetermined depth is calculated (step S105). ). The index value calculation unit 1142 specifies, as the corresponding pixel CP, a pixel corresponding to the target pixel TPi in the sample window SW where the index value is the maximum among the calculated index values (step S106).

  The interpolation processing unit 1143 determines an interpolation path based on the depth direction and the time direction using the target pixel TPi and the corresponding pixel CP in the second interpolation processing by an optical flow, a path framework, or the like. Then, the value of the determined route is calculated by, for example, linear interpolation (step S108).

  After the processing of steps S104 and S108, the index value calculation unit 1142 determines whether or not the target pixel TPi is the deepest in the set search range (step S109). When the target pixel TPi is not the deepest pixel (No in step S109), the index value calculation unit 1142 moves the target pixel TPi by one pixel to the deep part (step S1010), shifts the processing to step S102, and performs the same processing. Repeat the process. On the other hand, when the target pixel TPi is the deepest pixel (Yes in step S109), the index value calculation unit 1142 ends the processing.

  Through each of the above-described steps S101 to S109, one flex M-mode image is generated. The generated flex M-mode image is displayed on the display unit 14 in a predetermined form.

  FIGS. 11 to 13 are diagrams showing examples of a flex M-mode image of a valve that moves up and down at a high speed, for example, a heart valve and surrounding tissue. FIG. 11 shows an example of a flex M-mode image in a case where values between generated line data are interpolated using the above-described processing according to the present embodiment. FIG. 12 illustrates an example of a flex M-mode image when the line data generated by the image data generation unit 1141 is maintained until the next update timing. Note that line data is generated at triangular timings scattered in the horizontal axis direction in FIG. FIG. 13 shows an example of a flex M-mode image when values between generated line data are linearly interpolated.

  As shown in FIG. 13, it can be seen that it is difficult to smoothly connect the valve movements that move rapidly up and down only with linear interpolation (however, in general, in a tissue with little movement, only linear interpolation is used. Even if it is connected relatively smoothly). On the other hand, in FIG. 11, the connection of the valve is improved, and the good connection in the tissues other than the valve is also maintained.

  FIGS. 14A and 15A show examples of a flex M-mode image when the line data generated by the image data generation unit 1141 is maintained until the next update timing. FIG. 14B and FIG. 15B show examples of a flex M-mode image when values between generated line data are interpolated by an optical flow. FIG. 15C shows an example of a flex M-mode image when values between generated line data are interpolated by the interpolation processing according to the present embodiment. In each of FIGS. 14A, 14B, 15A, 15B, and 15C, line data is generated at triangular timings scattered in the horizontal axis direction.

  As can be seen by comparing FIG. 14A and FIG. 14B, when interpolating between line data having low similarity in the time direction, it is possible to preferably perform interpolation using an optical flow. However, as can be understood from FIG. 15B, when the interpolation path is determined based on the optical flow between the line data having high similarity, the similar data is connected more than necessary, and the data is appropriately connected. Cannot interpolate.

  On the other hand, according to the interpolation processing according to the present embodiment, as shown in FIG. 15C, even when data having a high similarity is close to each other, unnecessary connections between similar data are eliminated. be able to.

  As described above, according to the ultrasound diagnostic apparatus according to the present embodiment, first, at the same depth, the similarity in the time direction is determined based on a predetermined index value using the line data that is temporally adjacent. If it is determined that the similarity is high, a first interpolation process of interpolating data based on only the time direction is executed. On the other hand, when it is determined that the similarity in the time direction is low at the same depth, a second interpolation process of interpolating data in consideration of the depth direction and the time direction is executed.

  In a mode in which tissues are displayed in a time series such as the M mode and the flex M mode, the amount of data acquired by the ultrasonic diagnostic apparatus may be smaller than the amount of data required to update an image. In such a case, the conventional ultrasonic diagnostic apparatus employs a one-dimensional linear interpolation method for line data acquired on a predetermined scanning line, and generates interpolation data in the time direction of the line data.

  However, in the flex M mode, it is necessary to first create a B-mode image in order to update data in one scanning line, so that it is not necessary to transmit and receive ultrasonic waves as many times as necessary to create a B-mode image. Not be. Therefore, the flex M-mode image has a lower time resolution than a normal M-mode image. Since the flex M mode has a low time resolution, even if interpolation data is generated in the time direction of the line data by adopting the one-dimensional linear interpolation method as in the related art, the display of a fast-moving object is displayed in the flex M mode image. It becomes discontinuous in the time direction.

  According to the first embodiment, an appropriate interpolation process is appropriately selected according to the similarity of line data in the time direction. As a result, even when observing an object moving at high speed in the flex M mode, it is possible to suppress the display of the object in the flex M mode image from being discontinuous in the time direction. Also, for example, even when a subject having a large movement such as a heart valve and a subject having a small movement such as a tissue near a body surface having a layered structure are mixed, there is a natural connection in a time-series direction. A flex M-mode image can be generated.

(Modification)
As a modification of the first embodiment, when the present line data interpolation processing is realized by a medical image processing apparatus, the medical image processing apparatus has a configuration shown in FIG.

  Further, the image generating unit 114 included in the ultrasound diagnostic apparatus 10 according to the first embodiment may have a function realized by executing an application program by a processor, as shown in FIG. I do not care.

(Second embodiment)
In the first embodiment, the image generation unit 114 included in the ultrasonic diagnostic apparatus 10 includes a first interpolation process that interpolates data only in the time direction and a second interpolation process that interpolates data in the depth direction and the time direction. The case has been described as an example where data between a plurality of temporally adjacent line data is interpolated by adopting one of the two interpolation processes. However, the line data interpolation processing executed by the image generation unit is not limited to this.

  The image generating unit executes the first interpolation process and the second interpolation process, and is obtained in the first interpolation process according to the index values calculated between the line data that are temporally adjacent to each other. Adopting an interpolation path and a pixel value for the path, an interpolation path and a pixel value for the path obtained in the second interpolation processing, or a combination of these interpolation paths and a pixel value for these paths. Thus, data between a plurality of line data that are temporally adjacent may be interpolated.

  Hereinafter, the line data interpolation processing of the ultrasonic diagnostic apparatus according to the second embodiment will be described. This line data interpolation processing is executed in the image generator 118 provided in the ultrasonic diagnostic apparatus. FIG. 18 is a diagram illustrating an example of a functional configuration of the image generating unit 118. As shown in the figure, the image generation unit 118 includes an image data generation unit 1181, an index value calculation unit 1182, and an interpolation processing unit 1183.

  When an operator draws an arbitrary line (line: an arbitrary straight line or an arbitrary curve) on the B-mode image displayed on the display unit 14 via the input unit 13, the image data generation unit 1181 Image data (line data) on the drawn line is generated (read) and arranged in time series.

  In the following description, for the sake of clarity, as in the description of the first embodiment, an arbitrary straight line is drawn by the operator on the B-mode image as shown on the right side of FIG. Let's take an example.

  The index value calculation unit 1182 calculates an index value for determining similarity for at least one target portion (target) on the line data, using a plurality of temporally adjacent line data. As the index value for determining the similarity, for example, a correlation coefficient, a covariance, an absolute value thereof, or a combination of a plurality of them can be adopted. In the present embodiment, using two line data that are temporally adjacent to each other, each position on the line data is set as a target part, and an index value for determining similarity is set as a covariance value, which will be described later. It is assumed that the calculation is performed for each target part by the technique.

  Specifically, when the line data is arranged in chronological order by the image data generation unit 1181, the index value calculation unit 1182 outputs one of the line data (typically, The target window TWi is set to the older line data (referred to as “line data A” in this embodiment). The target window TWi is set to have a predetermined size in the depth direction so as to include the target pixel TPi as a target of similarity determination on the one line data and pixels in the vicinity thereof.

  Also, the index value calculation unit 1182 outputs the other line data of the temporally continuous line data (typically, the line data which is newer in time. In this embodiment, this is referred to as “line data B”). In, the sample window SWi is set at the same position (depth) as the target window TWi. The sample window SW is set to have the same size as the target window TW on the other line data B.

  The index value calculation unit 1182 first uses each pixel value in the target window TWi on the line data A and each pixel value in the sample window SWi (of the same depth as the target window TWi) on the line data B. Calculate the index value (covariance value). Subsequently, the index value calculation unit 1182 moves the position of the sample window SWi on the line data B by one pixel in the depth direction while fixing the target window TWi on the line data A, and sets a new sample window SWi + 1. Then, the above-described index value is calculated between the target window TWi and the sample window SWi + 1. Thereafter, the index value calculation unit 1182 sequentially moves the sample windows SWi on the line data B to a predetermined depth while keeping the target window TWi on the line data A fixed, and sets each sample window SWi set for each depth. The index value for each depth is calculated using the target window TWi.

  When each index value up to the predetermined depth is obtained, the index value calculation unit 1182 specifies the position of the sample window SW where the value is the maximum among the calculated index values for each depth, and specifies the specified sample. The pixel corresponding to the target pixel TPi in the window SW is specified.

  The interpolation processing unit 1183 performs, for each target on the line data, an interpolation process based on only the time direction (first interpolation process: for example, a linear interpolation process) and an interpolation process based on the depth direction and the time direction ( A second interpolation process: for example, an interpolation process based on a path framework, an optical flow, or the like) determines an interpolation path between temporally adjacent line data. Further, the interpolation processing unit 1183 performs predetermined interpolation processing (for example, nearest neighbor interpolation, linear interpolation, polynomial interpolation, spline interpolation, other nonlinear interpolation processing, etc.) for each pixel existing on the determined interpolation path. Determine the pixel value.

  Specifically, the interpolation processing unit 1183 sets the target pixel TPi in the sample window SWi of the line data A and the corresponding pixel CPi located at the same depth as the target pixel TPi in the line data B as end points, and An interpolation path is determined based only on the reference. Further, the interpolation processing unit 1183 uses the pixel value of the target pixel TPi on the line data A and the pixel value of the corresponding pixel CPi on the line data B to determine at least one between the target pixel TPi and the corresponding pixel CPi. Interpolate the values of two pixels.

  Further, the interpolation processing unit 1183 uses the target pixel TPi in the sample window SWi of the line data A and the corresponding pixel CPi + n specified by the index value calculation unit 1182 as end points, and performs interpolation based on the time direction and the depth direction. Determine the route. Further, the interpolation processing unit 1183 uses the pixel value of the target pixel TPi on the line data A and the pixel value of the corresponding pixel CPi + n on the line data B to determine at least one between the target pixel TPi and the corresponding pixel CPi + n. Interpolate the values of two pixels.

  Further, the interpolation processing unit 1183 determines, based on the index value calculated by the index value calculation unit 1182, a pixel included in the interpolation path determined in the first interpolation processing and a pixel included in the second interpolation processing. A weight coefficient is calculated for the pixels included in the interpolated interpolation path.

  Specifically, the interpolation processing unit 1183 calculates a weight coefficient for a pixel included in the interpolation path determined in the first interpolation processing by using each pixel value in the target window TWi on the line data A and the line data. It is calculated based on an index value calculated using each pixel value in the sample window SWi (with the same depth as the target window TWi) on B.

  Further, the interpolation processing unit 1183 sets the weighting coefficient for the pixels included in the interpolation path determined in the second interpolation processing to be the maximum of the index values calculated for each depth by the index value calculation unit 1182. It is calculated based on an index value.

  Accordingly, when the target portion having a high index value exists near the same depth in the adjacent line data, the interpolation processing unit 1183 determines that the weight coefficient such that the interpolation result by the first interpolation process becomes dominant. Is calculated. Further, when the target portion having a high index value exists at a different depth in the adjacent line data, the interpolation processing unit 1183 calculates a weight coefficient such that the interpolation result obtained by the second interpolation process becomes dominant. I do. Note that the interpolation processing unit 1183 may calculate the weight coefficient using the index value. Further, the interpolation processing unit 1183 may select a weighting coefficient from any of the stepwise numerical values prepared in advance according to the index value.

  Further, the interpolation processing unit 1183 multiplies the pixel value calculated for the interpolation path determined in the first interpolation processing by the weight coefficient calculated for the pixels included in this path. Further, the interpolation processing unit 1183 multiplies the pixel value calculated for the interpolation path determined in the second interpolation processing by the weight coefficient calculated for the pixels included in this path. The interpolation processing unit 1183 adds the data multiplied by the weight coefficient, and interpolates a value between line data.

  Next, the operation of the ultrasonic diagnostic apparatus in the flex M mode using the line data interpolation processing will be described. FIG. 19 is a diagram showing an example of a flowchart showing the operation of the present ultrasonic diagnostic apparatus in the flex M mode.

  First, when a line is drawn on a B-mode image by the operator via the input unit 13, the image data generating unit 1181 included in the image generating unit 118 generates time-series line data. The image generating unit 118 performs the following processing on the updated line data.

  First, the index value calculation unit 1182 included in the image generation unit 118 sets the pixel at the shallowest position in the predetermined area set in the line data A as the target pixel TPi as the processing target point, and A target window TWi having a predetermined size including TPi is set on the line data A (step S171).

  In addition, the index value calculation unit 1182 sets a sample window SWi of the same size in the line data B at a position corresponding to the target window TWi on the line data A. The index value calculation unit 1182 calculates an index value for determining the similarity between a plurality of pixels included in the target window and a plurality of pixels (a plurality of samples) included in the sample window by the method described above. . The index value calculation unit 1182 sequentially moves the sample window SWi while keeping the target window TWi fixed, and calculates an index value at each depth up to a predetermined depth. Also, when each index value up to a predetermined depth is obtained, the index value calculation unit 1182 specifies the position of the sample window SW where the value is maximum among the calculated index values for each depth, and specifies the index value. The pixel corresponding to the target pixel TPi in the sample window SW is specified (step S172).

  After step S171, the interpolation processing unit 1183 determines and determines the interpolation path using the first interpolation processing, using the target pixel TPi and the corresponding pixel CPi located at the same depth as the target pixel TPi as end points. The value for the route is calculated by linear interpolation (step S173). The interpolation processing unit 1183 causes the storage unit 115 to store the interpolation path determined in the first interpolation processing and the pixel value in this path (step S174).

  After step S171, the interpolation processing unit 1183 determines the interpolation path using the second interpolation processing using the target pixel TPi and the corresponding pixel CPi + n specified by the index value calculation unit 1182 as end points, and determines the interpolation path. The value of the route thus calculated is calculated by linear interpolation (step S175). The interpolation processing unit 1183 causes the storage unit 115 to store the interpolation path determined in the second interpolation processing and the pixel value in this path (step S176).

  After step S172, the interpolation processing unit 1183 determines, based on the index value calculated by the index value calculation unit 1182, the pixels included in the interpolation path determined in the first interpolation processing and the pixels included in the second interpolation processing. A weight coefficient is calculated for the pixels included in the interpolation path determined in the calculation, and the calculated weight coefficient is stored in the storage unit 115 (step S177).

  After the processing of steps S174, S176 and S177, it is determined whether or not the target pixel TPi is the deepest of the set search range (step S178). If the target pixel TPi is not the deepest pixel (No in step S178), the index value calculation unit 1182 moves the target pixel TPi by one pixel to the deep part (step S179), and performs the processing in steps S172, S173, and S175. Then, the same processing is repeated.

  On the other hand, if the target pixel TPi is the deepest pixel (Yes in step S178), the interpolation processing unit 1183 determines a plurality of weighting factors calculated for the same pixel among the weighting factors stored in the storage unit 115. Are combined to calculate a combined weight coefficient. The interpolation processing unit 1183 multiplies the pixel value for the interpolation path determined in the first and second interpolation processing stored in the storage unit 115 by the synthesis weight coefficient. The interpolation processing unit 1183 adds the weighted data, interpolates a value between the line data A and the line data B (step S1710), and ends the processing.

  As described above, in the second embodiment, the index value calculation unit 1182 included in the image generation unit 118 calculates an index value for determining the similarity between target portions of time-series continuous line data. The interpolation processing unit 1183 performs a first interpolation process of interpolating data between line data on the basis of only a time direction and a second interpolation process of interpolating data in consideration of a depth direction and a time direction. Is obtained by using The interpolation processing unit 1183 calculates a weight coefficient for a pixel included in the interpolation path determined in the first and second interpolation processes, based on the index value calculated by the index value calculation unit 1182. Then, the interpolation processing unit 1183 weights the pixel values calculated by the first and second interpolation processes using the weighting coefficients, and adds the weighted data to interpolate the data between the line data. Thus, it is possible to appropriately change the influence of the first and second interpolation processes on the interpolated value according to the index value to be calculated.

  Therefore, according to the ultrasonic diagnostic apparatus according to the second embodiment, even when a fast-moving object is observed in the flex M mode, the display of the subject in the flex M mode image is discontinuous in the time direction. Can be suppressed. Also, for example, even when a subject having a large movement such as a heart valve and a subject having a small movement such as a tissue near a body surface having a layered structure are mixed, there is a natural connection in a time-series direction. A flex M-mode image can be generated.

  In the second embodiment, an example in which the ultrasonic diagnostic apparatus operates according to the flowchart shown in FIG. 19 has been described. However, it is not limited to this. The ultrasonic diagnostic apparatus may operate according to the flowchart shown in FIG.

  First, when a line is drawn on a B-mode image by the operator via the input unit 13, the image data generating unit 1181 included in the image generating unit 118 generates time-series line data. The image generating unit 118 performs the following processing on the updated line data.

  First, the index value calculation unit 1182 included in the image generation unit 118 sets the pixel at the shallowest position in the predetermined area set in the line data A as the target pixel TPi as the processing target point, and A target window TWi having a predetermined size including TPi is set on the line data A (step S181).

  In addition, the index value calculation unit 1182 sets a sample window SWi of the same size in the line data B at a position corresponding to the target window TWi on the line data A. The index value calculation unit 1182 calculates an index value for determining the similarity between a plurality of pixels included in the target window and a plurality of pixels (a plurality of samples) included in the sample window by the method described above. . The index value calculation unit 1182 sequentially moves the sample window SWi while keeping the target window TWi fixed, and calculates an index value at each depth up to a predetermined depth. Also, when each index value up to a predetermined depth is obtained, the index value calculation unit 1182 specifies the position of the sample window SW where the value is maximum among the calculated index values for each depth, and specifies the index value. The pixel corresponding to the target pixel TPi in the sample window SW is specified (Step S182).

  After step S181, in the first interpolation processing, the interpolation processing unit 1183 determines and determines an interpolation path using the target pixel TPi and the corresponding pixel CPi located at the same depth as the target pixel TPi as end points. The value for the route is calculated by linear interpolation (step S183). After step S181, the interpolation processing unit 1183 determines an interpolation path in the second interpolation processing, using the target pixel TPi and the corresponding pixel CPi + n specified by the index value calculation unit 1182 as end points. The value of the route thus calculated is calculated by linear interpolation (step S184).

  After step S182, the interpolation processing unit 1183 determines, based on the index value calculated by the index value calculation unit 1182, the pixels included in the interpolation path determined in the first interpolation processing and the pixels included in the second interpolation processing. A weight coefficient is calculated for the pixels included in the interpolation path determined in Step S185 (Step S185).

  After the processing of step S183, step S184, and step S185, the interpolation processing unit 1183 multiplies the luminance value obtained using the first and second interpolation processing by a weighting coefficient. The interpolation processing unit 1183 adds the weighted data and interpolates a value between the target pixel TPi in the line data A and the pixel of the line data B (step S186).

  After the process in step S186, the index value calculation unit 1182 determines whether the target pixel TPi is the deepest in the set search range (step S187). When the target pixel TPi is not the deepest pixel (No in step S187), the index value calculation unit 1182 moves the target pixel TPi by one pixel to the deep part (step S188), and performs the processing in steps S182, S183, and S184. Then, the same processing is repeated.

  On the other hand, when the target pixel TPi is the deepest pixel (Yes in step S187), the image generation unit 118 ends the processing.

  By performing the processing shown in FIG. 20, the image generating unit 118 can reduce the storage capacity as compared with the processing shown in FIG.

(Modification)
As a modified example of the second embodiment, when the technical idea of the ultrasonic diagnostic apparatus 10 is realized by a medical image processing apparatus, the medical image processing apparatus has a configuration illustrated in FIG.

  Further, the image generator 118 included in the ultrasound diagnostic apparatus 10 according to the second embodiment may have a function realized by executing an application program by a processor, as shown in FIG. I do not care.

(Third embodiment)
The line data interpolation processing according to the first or second embodiment is not limited to an ultrasonic diagnostic apparatus, but is applied to a medical image diagnostic apparatus such as an X-ray CT apparatus, an X-ray diagnostic apparatus, a magnetic resonance imaging apparatus, and a nuclear medicine diagnostic apparatus. FIG. 23 is a block diagram showing the configuration of the medical image diagnostic apparatus 2 that executes the line data interpolation processing according to the first or second embodiment. As shown in FIG. 1, the medical image diagnostic apparatus 2 includes a scanning unit 21 as a data collection system and a data processing system 22.

  When the medical image diagnostic apparatus 2 is an X-ray CT apparatus, the scanning unit 21 mounts the medical image diagnostic apparatus 2 on a mount having an X-ray tube, an X-ray detector, a data acquisition system (DAS), a rotating ring, and the like. When the medical image diagnostic apparatus 2 is a magnetic resonance imaging apparatus, a static magnetic field is generated in an imaging mechanism including an X-ray tube, a plane detector, a C arm (and an Ω arm). A gamma ray detecting unit, a detecting unit holding mechanism, a rotation driving device, and the like, when the medical image diagnostic apparatus 2 is a nuclear medicine diagnostic apparatus, in a gantry having a unit and a gradient magnetic field generating unit, a transmitting coil, a receiving coil, and the like. , Respectively.

  The data processing system 22 includes a control unit 24, a storage unit 25, an interface unit 26, a display unit 27, and an input unit 28. These components are substantially the same as, for example, the control unit 117, the storage unit 115, the interface unit 116, the input unit 13, and the display unit 14 illustrated in FIG.

  The image data generating unit 231 of the image generating unit 23 generates at least two time-series line data using the scanning result of the scanning unit 21. For example, when the medical image diagnostic apparatus 2 is an X-ray diagnostic apparatus, the image data generating unit 231 performs image reconstruction (or sequential reconstruction) using the projection data collected by the scanning unit 21; At least two time-series line data are generated from the obtained reconstructed image. The index value calculation unit 232 calculates an index value for determining similarity using at least two pieces of time-series line data. The interpolation processing unit 233 generates interpolation data between line data based on the similarity between line data determined based on the index values calculated by the index value calculation unit 232.

  According to the configuration described above, the line data interpolation processing according to the first or second embodiment is performed in a medical image diagnostic apparatus such as an X-ray CT apparatus, an X-ray diagnostic apparatus, a magnetic resonance imaging apparatus, and a nuclear medicine diagnostic apparatus. Can also be realized.

(Other modifications)
In the first and second embodiments, an example has been described in which the ultrasound diagnostic apparatus determines a method of generating interpolation data according to the similarity between line data in the flex M mode. However, it is not limited to this. The ultrasonic diagnostic apparatus can acquire two line data by a method other than the flex M mode, and determine a method of generating interpolation data according to the similarity between the two line data.

  For example, the ultrasonic diagnostic apparatus may determine a method of generating the interpolation data according to the similarity between the line data in the normal M mode. At this time, the image data generator 1141 of the ultrasonic diagnostic apparatus sets B-mode image data generated from B-mode data in one scanning line as line data.

  Further, the ultrasonic diagnostic apparatus may determine a method of generating the interpolation data according to the similarity between the line data obtained from the Doppler data. For example, the ultrasound diagnostic apparatus generates, as line data, image data on a line drawn for a color Doppler image or a combination of a B-mode image and a color Doppler image, and performs similarity between temporally adjacent line data. The generation method of the interpolation data may be determined according to the characteristics. At this time, the image data generating unit 1141 of the ultrasonic diagnostic apparatus generates two-dimensional color Doppler image data from the color Doppler data generated by the Doppler data generating unit 113, and converts the two-dimensional color Doppler image data into a combination of a B-mode image and a color Doppler image. The image data on the line drawn is defined as line data. Further, in the Doppler spectrum mode, the ultrasonic diagnostic apparatus may determine a method of generating the interpolation data according to the similarity between the line data. At this time, the image data generating unit 1141 of the ultrasonic diagnostic apparatus sets spectrum image data on one line generated from Doppler spectrum data at a predetermined observation point as line data. Further, the ultrasonic diagnostic apparatus may determine a method of generating the interpolation data according to the similarity between the line data of the edge portions of two adjacent two-dimensional images.

  Further, in the first and second embodiments, when performing the ultrasonic scanning in the B mode or the M mode, the line data is not limited to the image data by the fundamental component, May be generated.

  Further, in the first to third embodiments, when calculating the index value and determining the similarity, it is not always necessary to use the data at all positions on the line data. For example, the similarity (partial similarity) between line data may be determined by calculating an index value using data within a range that requires interpolation processing on the line data.

  Further, each processor in the first to third embodiments is, for example, a CPU (central processing unit), a GPU (Graphics Processing Unit), or an application specific integrated circuit (ASIC)), a programmable logic It means a circuit such as a device (for example, a Simple Programmable Logic Device (SPLD), a Complex Programmable Logic Device (CPLD), and a Field Programmable Gate Array (FPGA)). .

  Although several embodiments of the present invention have been described, these embodiments are provided by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in other various forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and equivalents thereof.

  DESCRIPTION OF SYMBOLS 10 ... Ultrasonic diagnostic apparatus, 11 ... Device main body, 111 ... Transmission / reception part, 112 ... B-mode data generation part, 113 ... Doppler data generation part, 114 ... Image generation part, 1141 ... Image data generation part, 1142 ... Index value calculation Unit, 1143 interpolation processing unit, 115 storage unit, 116 interface unit, 117 control unit, 118 image generation unit, 1181 image data generation unit, 1182 index value calculation unit, 1183 interpolation processing unit, 12 ... Ultrasonic probe, 13 ... Input unit, 14 ... Display unit, 2 ... Medical image diagnostic apparatus, 21 ... Scanning unit, 22 ... Data processing system, 23 ... Image generation unit, 231 ... Image data generation unit, 232 ... Index value Calculation unit, 233 interpolation processing unit, 24 control unit, 25 storage unit, 26 interface unit, 27 display unit, 28 input unit

Claims (14)

A scanning unit that scans the inside of the subject;
An image data generating unit that generates first-line data and second-line data that are linear image data based on a scanning result of the scanning unit, and are temporally adjacent to each other;
A similarity determination unit that calculates an index value indicating a similarity between the first target on the first line data and the second target on the second line data at the same depth position as the first target ;
When the index value calculated by the similarity determination unit exceeds a threshold , interpolation data between the first target and the second target is generated by a first interpolation process based only on a time direction. Processing unit
With
The similarity determination unit, when the index value calculated by the similarity determination unit does not exceed the threshold, by changing the depth position of the second object, the second after the change between the first object Calculate an index value indicating the similarity with the two objects ,
The interpolation processing unit is configured such that the first object determined based on the index value indicating the similarity between the first object calculated by the similarity determination unit and the second object after the change is a depth position. A medical image diagnostic apparatus that generates interpolation data between the second object after the change and the second object by a second interpolation process based on a time direction and a depth direction. The scanning unit includes an ultrasonic probe that transmits and receives ultrasonic waves,
The image data generating unit, the claim 1 Symbol placement of the medical image diagnostic apparatus for generating said first line data and the second line data based on the results of transmission and reception of ultrasonic waves by the ultrasonic probe. The image data generating unit sequentially generates two-dimensional image data based on a result of transmission and reception of ultrasonic waves for a plurality of different scanning lines by the ultrasonic probe, and is set on the two-dimensional image data. The medical image diagnostic apparatus according to claim 2 , wherein the first line data and the second line data are generated based on a pixel value at each position of a straight line or a curve. The medical image diagnostic apparatus according to claim 3 , wherein the two-dimensional image data is B-mode image data. The medical image diagnostic apparatus according to claim 3 , wherein the two-dimensional image data is a color Doppler image or a combination of B-mode image data and the color Doppler image. The image data generating unit generates the first line data and the second line data corresponding to the predetermined one scanning line based on a result of transmission and reception of an ultrasonic wave related to a predetermined one scanning line by the ultrasonic probe. The medical image diagnostic apparatus according to claim 2 , wherein 3. The image data generating unit generates spectrum image data at the predetermined observation point as the first and second line data based on a result of transmission and reception of ultrasonic waves at a predetermined observation point by the ultrasonic probe. 4. 4. The medical image diagnostic apparatus according to claim 1. The similarity determination unit sets a target window including a predetermined number of pixels for the first line data, and sets a sample window including a predetermined number of pixels for the second line data. 2. The medical image diagnostic apparatus according to claim 1 , wherein the index value is calculated based on a pixel value of each pixel forming the target window and the sample window. The medical image diagnostic apparatus according to claim 8 , wherein the similarity determination unit calculates the index value by calculating a correlation coefficient or a covariance using the pixel value. The similarity determination unit, the target window and the number of pixels constituting the sample window, a medical image according to claim 8 or 9 to change depending on the position on the first line data and the second line data Diagnostic device. The similarity determination unit, the number of pixels constituting the target window and the sample window in claim 8 or 9 to change depending on the organizational structure represented in the first line data and the second on line data The medical image diagnostic apparatus according to the above. A scanning unit that scans the inside of the subject;
An image data generating unit that generates first-line data and second-line data that are linear image data based on a scanning result of the scanning unit, and are temporally adjacent to each other;
Using a plurality of interpolation methods, generating a plurality of first interpolation data related to a predetermined position between the first line data and the second line data, and generating a plurality of first interpolation data based on the plurality of first interpolation data. A medical image diagnostic apparatus comprising: an interpolation processing unit that generates second interpolation data. An index value calculation unit that calculates an index value indicating a similarity between the first target on the first line data and the second target on the second line data;
The interpolation processing unit weights the plurality of first interpolation data with a weight coefficient determined according to the magnitude of the index value, and adds the plurality of weighted first interpolation data to each other, thereby obtaining the second interpolation data. 13. The medical image diagnostic apparatus according to claim 12 , wherein the apparatus generates interpolation data. An image data generating unit that generates one-dimensional image data based on a scanning result inside the subject and generates first line data and second line data that are temporally adjacent to each other;
A similarity determination unit that calculates an index value indicating a similarity between the first target on the first line data and the second target on the second line data at the same depth position as the first target ;
When the index value calculated by the similarity determination unit exceeds a threshold , interpolation data between the first target and the second target is generated by a first interpolation process based only on a time direction. A processing unit;
With
The similarity determination unit, when the index value calculated by the similarity determination unit does not exceed the threshold, by changing the depth position of the second object, the second after the change between the first object Calculate an index value indicating the similarity with the two objects ,
The interpolation processing unit is configured such that the first object determined based on the index value indicating the similarity between the first object calculated by the similarity determination unit and the second object after the change is a depth position. A medical image processing apparatus comprising: an interpolation processing unit that generates interpolation data between the second object after the change and the second object by a second interpolation process based on a time direction and a depth direction.