JP7762989B2 - Program, image processing device and image processing method - Google Patents

Description

This disclosure relates to image processing techniques for nuclear medicine.

Nuclear cardiac medicine tests, which examine the heart using a technique called SPECT (Single Photon Emission Computed Tomography) or PET (Positron Emission Tomography), involve administering a radiopharmaceutical to a subject and then detecting the radiation emitted from the radiopharmaceutical to obtain a three-dimensional nuclear medicine image, are gaining attention.

In cardiac nuclear medicine examinations, nuclear medicine images are acquired in response to radiation from radiopharmaceuticals that have accumulated in the myocardium, and cardiac examinations are performed based on these nuclear medicine images. However, because radiopharmaceuticals also physiologically accumulate in the liver and are excreted via the digestive tract, false images (artifacts) caused by radiation from radiopharmaceuticals that have accumulated in the subdiaphragmatic organs, such as the liver and digestive tract, appear in the nuclear medicine images, affecting images of the myocardium, particularly the inferior myocardial wall adjacent to the subdiaphragmatic organs.

For example, radiation from organs below the diaphragm and radiation from the inferior myocardial wall may be affected by the partial volume effect, resulting in an increase in radiation count values in the inferior myocardial wall. In this case, defects in the inferior myocardial wall may be obscured, or radiation count values in areas other than the inferior myocardial wall may appear to be reduced. Furthermore, image reconstruction using filtered back-projection (FBP) may result in a decrease in radiation count values in the inferior myocardial wall (see Non-Patent Document 1).

Non-Patent Document 2 describes a method for eliminating the effects of radiopharmaceutical accumulation in subdiaphragmatic organs in myocardial nuclear medicine images. Specifically, it describes a method of repeating imaging until the accumulation of radiopharmaceuticals in the subdiaphragmatic organs decreases or moves, and a method of adding prone imaging to the usual supine imaging. Prone imaging increases the distance between the heart and the subdiaphragmatic organs, thereby reducing artifacts caused by accumulation in the subdiaphragmatic organs.

Steven Burrell, et al. “Artifacts and Pitfalls in Myocardial PerfusionImaging”, Journal of Nuclear Medicine Technology, 2006 Dec;34(4):193-211. Ryan A Dvorak, et al. “Interpretation of SPECT/CT Myocardial Perfusion Images: Common Artifacts and Quality Control Techniques”, Radiographics, Nov-Dec 2011;31(7):2041-57.

However, the methods described in Non-Patent Documents 1 and 2 do not necessarily reduce the influence of radiopharmaceutical accumulation in subdiaphragmatic organs on myocardial nuclear medicine images, and may actually reduce diagnostic accuracy. Furthermore, multiple imaging sessions are required, which lengthens the examination time and places a significant burden on the subject.

This disclosure has been made in consideration of the above-mentioned problems, and aims to provide a program, image processing device, and image processing method that can improve diagnostic accuracy while reducing the burden on the subject.

A program according to one aspect of the present disclosure causes a computer to implement: a setting unit that sets, as a region of interest in the tomographic image data, a region that depicts the myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, based on a reference pixel value that indicates the distribution of the radiopharmaceutical that has accumulated in the myocardium and subdiaphragmatic organs and is the maximum pixel value in a region above the center of the heart in the tomographic image data before smoothing processing; an extraction unit that extracts myocardial tomographic image data that indicates the myocardial distribution from the tomographic image data based on the region of interest; and an image processing unit that performs smoothing processing on the myocardial tomographic image data.

In a preferred embodiment, the setting unit measures pixel values of the tomographic image data along each of a plurality of radial lines extending three-dimensionally radially from a predetermined position within the cardiac cavity, and for each radial line, identifies the myocardial outer wall position corresponding to the myocardial outer wall in the tomographic image data based on high-density pixels, which are pixels whose pixel values are maximum and equal to or greater than a certain percentage of the reference pixel value, and sets the region of interest based on the myocardial outer wall position.

In a preferred embodiment, when there is one highly accumulating pixel, the setting unit identifies a position a certain distance outward from the highly accumulating pixel as the myocardial outer wall position; when there are two or more highly accumulating pixels, the setting unit identifies the position of the pixel having the smallest pixel value between the highly accumulating pixel closest to the specified position and the highly accumulating pixel second closest to the specified position as the myocardial outer wall position.

In a preferred embodiment, the setting unit sets, as the region of interest, an approximately heart-shaped region that connects the positions of the myocardial outer wall on each radial line, or an ellipsoid region that approximates the shape of the approximately heart-shaped region to an ellipsoid.

In a preferred embodiment, for each perimeter pixel on the perimeter of the region of interest, if there is an outer accumulation pixel, which is a pixel whose pixel value is a maximum and is equal to or greater than a certain percentage of the reference pixel value, along an outward line extending outward from the perimeter pixel and within a specific distance from the perimeter pixel, the extraction unit extracts the myocardial tomographic image data by subtracting the pixel value of the accumulation below the diaphragm, which is a pixel value corresponding to the radiopharmaceutical that has accumulated in the organ below the diaphragm, from the pixel value of the pixel on the outward line based on the pixel values along the outward line.

In a preferred embodiment, the extraction unit approximates the change in pixel values from the outer peripheral pixels along the outward line to the outer accumulation pixels using a Gaussian function, and subtracts the pixel values represented by the Gaussian function from the pixel values of the pixels on the outward line as pixel values of the accumulation below the diaphragm.

In a preferred embodiment, the extraction unit approximates the change in pixel values along the outward line in the region of interest with a Gaussian function, and sets the pixel value represented by the Gaussian function to the pixel value of the pixel on the outward line minus the pixel value of the accumulation below the diaphragm.

In a preferred embodiment, for each peripheral pixel, if an outer accumulation pixel is present, the extraction unit sets all pixel values in the area outside the outer accumulation pixel along the outward line to zero; and if no outer accumulation pixel is present, the extraction unit sets all pixel values in the area outside a position a specific distance outward from the peripheral pixel in the tomographic image data to zero.

In a preferred embodiment, the image processing unit inversely converts the myocardial tomographic image data into projection data and performs smoothing processing on the myocardial tomographic image data based on the projection data.

An image processing device according to one aspect of the present disclosure includes a setting unit that sets, as a region of interest in the tomographic image data, a region that depicts the myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, based on a reference pixel value that is the maximum pixel value in a region above the center of the heart in the tomographic image data before smoothing processing is performed, and that represents the distribution of the radiopharmaceutical that has accumulated in the myocardium; an extraction unit that extracts myocardial tomographic image data that represents the myocardial distribution from the tomographic image data based on the region of interest; and an image processing unit that performs smoothing processing on the myocardial tomographic image data.

An image processing method according to one aspect of the present disclosure sets, as a region of interest in the tomographic image data, a region that shows the myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, based on a reference pixel value that is the maximum pixel value in a region above the center of the heart in the tomographic image data before smoothing processing is performed, and that represents the myocardial distribution; extracts myocardial tomographic image data that shows the myocardial distribution from the tomographic image data based on the region of interest; and performs smoothing processing on the myocardial tomographic image data.

This disclosure makes it possible to improve diagnostic accuracy while reducing the burden on subjects.

1 is a block diagram illustrating a configuration of an image processing device according to an embodiment of the present disclosure. 1 is a flowchart illustrating an operation of an image processing device according to an embodiment of the present disclosure. 10 is a flowchart illustrating an example of a region of interest extraction process. FIG. 10 is a diagram showing an example of radial lines in a slice image. 10A and 10B are diagrams for explaining a process for identifying the position of the myocardial outer wall. 10 is a flowchart illustrating an example of myocardial extraction processing. FIG. 10 is a diagram for explaining a process for identifying pixel values of accumulation in subdiaphragmatic organs. 1A and 1B are diagrams illustrating an image processing method of the present disclosure and an image processing method of a reference example. 1A and 1B are diagrams showing myocardial tomographic image data obtained by the image processing method of the present disclosure and myocardial tomographic image data obtained by the image processing method of a reference example.

Embodiments of the present disclosure will be described below with reference to the drawings.

Figure 1 is a block diagram showing the configuration of an image processing device according to one embodiment of the present disclosure. The image processing device 100 shown in Figure 1 is configured, for example, by a computer system including a processor (computer) and memory (neither of which are shown). In this case, the components and functions of the image processing device 100 described below are realized, for example, by the processor reading a program and executing the read program. The program can be recorded on a computer-readable recording medium such as memory.

The image processing device 100 is also connected to an input/output device 101 and an auxiliary storage device 102. The input/output device 101 has input devices such as a keyboard, touch panel, and pointing device that accept various information from a user using the image processing device 100, and output devices such as a display device and printer that output various information to the user. The input/output device 101 may also include a network interface device that sends and receives various information via a communication network such as the Internet. The auxiliary storage device 102 is a storage device that stores various information, such as a large-capacity storage device.

The image processing device 100 has an acquisition unit 1, a reconstruction unit 2, a setting unit 3, an extraction unit 4, and an image processing unit 5.

The acquisition unit 1 acquires projection data based on radiation from a subject administered with a radiopharmaceutical as the data to be processed. The projection data is generated by imaging the subject using a radiation detector a certain time after the subject is administered with the radiopharmaceutical. Specifically, the projection data is data including multiple pixels arranged two-dimensionally for each predetermined rotation angle of the detector that rotates around the subject, and the pixel value of each pixel. The pixel value is determined according to the count value obtained by counting radiation from the part of the subject that corresponds to the pixel. The pixel value may be, for example, the count value itself.

In this embodiment, the projection data is projection data for myocardial nuclear medicine examination. In this case, the count value typically reflects not only the radiation from the myocardium, but also the effects of radiation from radiopharmaceuticals that have accumulated in organs below the diaphragm, such as the liver. Furthermore, the radiopharmaceutical is not particularly limited, as long as it is a radiopharmaceutical used in myocardial SPECT examination or myocardial PET examination.

The reconstruction unit 2 generates tomographic image data based on the projection data acquired by the acquisition unit 1. Specifically, the reconstruction unit 2 performs image reconstruction processing on the projection data to generate horizontal tomographic image data representing horizontal tomographic images (body axis cross-sectional images) of the myocardium, and then performs cross-section transformation processing on the horizontal tomographic image data to generate multiple tomographic image data with different cross-sectional orientations. In this embodiment, the tomographic image data includes horizontal long-axis tomographic image data representing horizontal long-axis tomographic images of the myocardium, vertical long-axis tomographic image data representing vertical long-axis tomographic images of the myocardium, and short-axis tomographic image data representing short-axis images of the myocardium.

Each tomographic image data set reflects not only the distribution of radiopharmaceuticals accumulated in the myocardium, but also the distribution of radiopharmaceuticals accumulated in organs adjacent to the heart, such as the liver and digestive tract, below the diaphragm. Furthermore, each tomographic image data set is generated so that the center of the heart (more specifically, the center of the left ventricle of the heart) is positioned at the center of the image. Each tomographic image data set is a collection of multiple slice images.

In this embodiment, the image reconstruction process uses a statistical image reconstruction method that includes resolution correction, such as the Maximum Likelihood Expectation Maximization (MLEM) method or the Ordered Subset Expectation Maximization (OSEM) method. In this case, the impact of radiopharmaceutical accumulation in the subdiaphragmatic organs on the inferior wall of the myocardium can be reduced. It also facilitates the extraction of myocardial tomographic image data by the extraction unit 4, which will be described later. However, the image reconstruction process may be image processing that uses a statistical image reconstruction method that does not include resolution correction, or image processing that uses conventional filtered back-projection (FBP).

In particular, tomographic image data for myocardial SPECT examinations contains a relatively small amount of information and a relatively large amount of statistical noise. Therefore, to improve visibility, a smoothing process is usually performed before or after image reconstruction processing to smooth out changes in pixel values on the image. However, when smoothing processing is performed, the effect of radiopharmaceutical accumulation in organs below the diaphragm on the inferior wall of the myocardium is emphasized. For this reason, in this embodiment, the reconstruction unit 2 does not perform smoothing processing.

The acquisition unit 1 may also acquire tomographic image data instead of projection data as the data to be processed. In this case, there is no need for the reconstruction unit 2 to perform image reconstruction processing. The tomographic image data acquired by the acquisition unit 1 is also assumed to have not undergone smoothing processing.

The setting unit 3 sets a three-dimensional region of the tomographic image data that depicts the myocardial distribution, which is the distribution of radiopharmaceuticals accumulated in the myocardium, as a region of interest. Specifically, the setting unit 3 sets the region of the tomographic image data that depicts the myocardial distribution as a region of interest based on a reference pixel value that is the maximum pixel value in the upper region corresponding to the side above the center of the heart in the tomographic image data.

The extraction unit 4 extracts myocardial tomographic image data showing the myocardial distribution from the tomographic image data based on the region of interest set by the setting unit 3.

The image processing unit 5 performs various image processing on the myocardial tomographic image data extracted by the extraction unit 4 and outputs the result. Image processing includes, for example, smoothing and resection transformation. Alternatively, image processing may involve inversely transforming the myocardial tomographic image data into projection data, and then performing image reconstruction, resection transformation, and smoothing on the projection data to generate the desired tomographic image data. In this case, it becomes possible to generate tomographic image data with the cross-section oriented in the desired direction.

Figure 2 is a flowchart illustrating the operation of the image processing device 100.

First, the acquisition unit 1 acquires data to be processed (step S101). For example, the acquisition unit 1 may acquire data via the input/output device 101 from any external device, such as an imaging device that images a subject using a radiation detector, or may acquire data stored in the auxiliary storage device 102.

The acquisition unit 1 determines whether the acquired data is projection data (step S102).

If the acquired data is projection data, the reconstruction unit 2 generates tomographic image data based on the projection data (step S103). On the other hand, if the acquired data is not projection data, that is, if the acquired data is tomographic image data, the reconstruction unit 2 skips the processing of step S103. Note that it is assumed that no smoothing processing has been performed on the tomographic image data.

The setting unit 3 executes a region of interest extraction process (see Figures 3 to 5) to set a region of interest in the tomographic image data acquired by the acquisition unit 1 or generated by the reconstruction unit 2 (step S104).

The extraction unit 4 executes myocardial extraction processing (see Figures 6 and 7) to extract myocardial tomographic image data from the tomographic image data based on the region of interest set by the setting unit 3 (step S105).

The image processing unit 5 determines whether or not to generate projection data from the myocardial tomographic image data (step S106). For example, whether or not to generate projection data may be specified in advance, or the image processing unit 5 may inquire of the user at this stage as to whether or not to generate projection data.

If projection data is not to be generated, the image processing unit 5 performs predetermined image processing, including smoothing, on the myocardial tomographic image data (step S107). The image processing unit 5 then displays the processed myocardial tomographic image data on the input/output device 101 (step S108), and terminates the processing. Note that instead of displaying the myocardial accumulation data on the input/output device 101, the image processing unit 5 may transmit the data to an external device (not shown) via the input/output device 101, or may store the data in the auxiliary storage device 102.

On the other hand, when generating projection data, the image processing unit 5 generates the projection data from the myocardial tomographic image data (step S109). Then, the image processing unit 5 generates the desired tomographic image data as myocardial tomographic image data from the generated projection data, and performs predetermined image processing, including smoothing processing, on the myocardial tomographic image data (step S110). Then, the image processing unit 5 proceeds to processing in step S108.

Figure 3 is a flowchart illustrating an example of the region of interest extraction process in step S104.

In the region of interest extraction process, the setting unit 3 first determines whether or not to perform precision setting, which precisely sets the region of interest (step S201). For example, whether or not to perform precision setting may be specified in advance, or the setting unit 3 may inquire of the user at this stage as to whether or not to perform precision setting.

If precise setting is not required, the setting unit 3 performs simple setting to simply set the region of interest (step S202) and terminates the process. Methods for simply setting the region of interest include, for example, setting an ellipsoidal region of a predetermined size centered on the center of each tomographic image data as the region of interest, or having the user manually set the region of interest.

When performing precise setting, the setting unit 3 first determines the reference pixel value, which is the maximum pixel value in the upper region corresponding to the area above the center of the heart in the tomographic image data (step S203). Because the upper region is an area that is less susceptible to the influence of radiopharmaceutical accumulation in organs below the diaphragm, the reference pixel value can be considered to be the maximum pixel value that is not influenced by accumulation in organs below the diaphragm. In this embodiment, the upper region is assumed to be the upper half of the short-axis tomographic image data. In this case, the reference pixel value can be set more appropriately.

The upper region may be any region above the center of the heart, and may be limited, for example, to a portion of the upper half of the short-axis tomographic image data. Furthermore, the range of the upper region in the long axis direction is not limited to the range from the apex to the base of the heart (0% to 100%), when the total length along the long axis of the heart is 100% and the apex is the starting point, but may more preferably be limited to a range of 30% to 90%.

Next, the setting unit 3 measures pixel values of the tomographic image data along each of a plurality of radial lines extending three-dimensionally radially from the center of the heart (step S204). In this embodiment, the setting unit 3 measures pixel values from the center of the image outward along each of a plurality of radial lines extending radially from the image center at fixed angles in the central slice images of each of the tomographic image data (horizontal long axis tomographic image data, vertical long axis tomographic image data, and short axis tomographic image data). The fixed angle is, for example, 3 degrees. Note that the starting point of the radial lines is not limited to the center of the heart, and may be any position within the cardiac cavity. Furthermore, the starting point of the three-dimensional radial lines is not limited to the center of the heart, and may be any predetermined position within the cardiac cavity.

Figure 4 shows an example of radial lines in one slice image. In the example of Figure 4, 40 radial lines X are shown extending from the center O of the heart at a fixed angle of 9 degrees.

Returning to the explanation of Figure 3, after measuring the pixel values of each radial line, the setting unit 3 identifies, for each radial line, the pixel position where the pixel value is maximum and equal to or greater than a threshold value that is a certain percentage (e.g., 20%) of the reference pixel value as a high accumulation position where the radiopharmaceutical has accumulated (step S205). Note that a high accumulation position is considered to be a position corresponding to the myocardium.

The setting unit 3 identifies the myocardial outer wall position, which is the position corresponding to the outer wall of the myocardium, for each radial line based on the high accumulation positions (step S206). Specifically, the setting unit 3 checks the number of high accumulation positions for each radial line, and identifies the myocardial outer wall position based on the high accumulation positions and their number.

Figure 5 is a diagram for explaining the process of identifying the position of the myocardial outer wall, showing the change in pixel values along radial lines. In Figure 5, the horizontal axis represents the position along the radial lines with the center O of the heart set at 0, and the vertical axis represents the pixel value. The threshold value is also represented as T.

Figure 5(a) shows an example where there is one high accumulation position. In this case, the setting unit 3 determines that there is no accumulation in the subdiaphragmatic organs in the direction along the radial lines, and identifies a position a certain distance L away from the high accumulation position M toward the outside as the myocardial outer wall position W. The certain distance L is, for example, 2 cm.

Figure 5(b) shows an example where there are two or more high accumulation positions. In this case, the setting unit 3 determines that the high accumulation position M1 closest to the center O of the heart is the position of the myocardium, and the high accumulation position M2 second closest to the center O of the heart is the position of radiopharmaceutical accumulation in the organs below the diaphragm, and identifies the position with the smallest pixel value between high accumulation positions M1 and M2 as the myocardial outer wall position W. In this case, if the distance between high accumulation positions M1 and M2 is equal to or greater than a predetermined distance (e.g., 2 cm), the setting unit 3 may identify a position a predetermined distance outward from high accumulation position M1 as the myocardial outer wall position W. Note that the predetermined distance may be the same as the constant distance L shown in Figure 4(a).

Figure 5(c) shows an example where the high accumulation position is 0. In this case, the setting unit 3 determines that there is no myocardium in the direction along the radial line, and does not identify the myocardial outer wall position W.

Returning to the explanation of Figure 3, once the myocardial outer wall position has been identified for each radial line, the setting unit 3 sets a region of interest based on the myocardial outer wall position of each radial line (step S207), and ends the processing.

The region of interest is either an approximately heart-shaped region formed by connecting the positions of the myocardial outer wall of each radial line in each piece of tomographic image data, or an ellipsoidal region formed by approximating the shape of the approximately heart-shaped region to an ellipsoid. The method for approximating the approximately heart-shaped region to an ellipsoidal region is not particularly limited, but examples include the least squares method. Whether the region of interest is an approximately heart-shaped region or an ellipsoidal region may be set in advance, or may be set by the user.

The region of interest may also be adjusted manually. For example, if the region of interest is an ellipsoidal region, adjustments to the region of interest may include moving the entire region of interest, moving the three axes of the ellipsoid, deforming and rotating, etc. Furthermore, if the region of interest is an approximately cardiac region, adjustments to the region of interest may include mesh deformation, moving the entire region of interest, and pixel-by-pixel correction.

Figure 6 is a flowchart illustrating an example of the myocardial extraction process in step S105.

In the myocardial extraction process, the extraction unit 4 determines whether to perform estimated removal to remove the effect of radiopharmaceutical accumulation in subdiaphragmatic organs on the region of interest (step S301). For example, whether to perform estimated removal may be specified in advance, or the extraction unit 4 may inquire of the user at this stage as to whether to perform estimated removal.

If estimation removal is not performed, the extraction unit 4 extracts myocardial tomographic image data by setting all pixel values in regions other than the region of interest in each tomographic image data to zero (step S302), and then terminates the process.

When performing estimation and removal, the extraction unit 4 analyzes the tomographic image data and estimates pixel values due to the accumulation of radiopharmaceuticals in subdiaphragmatic organs (step S303).

Specifically, the extraction unit 4 first measures the pixel value for each perimeter pixel, which is a pixel on the perimeter of the region of interest in the tomographic image data, along an outward line extending outward from that perimeter pixel. Specifically, the outward line is a line passing through the perimeter pixel and the center of the region of interest, or a line passing through the perimeter pixel and perpendicular to the perimeter of the region of interest.

Next, for each peripheral pixel, the extraction unit 4 determines whether there is an outer cluster pixel, which is a pixel whose pixel value is a maximum and equal to or greater than a threshold value that is a certain percentage (e.g., 20%) of the reference pixel value, within a certain distance (e.g., 1 cm) along the outward line.

If there are no outer accumulation pixels, the extraction unit 4 determines that there are no pixel values due to accumulation of subdiaphragmatic organs on the outward line, and does not estimate pixel values of accumulation of subdiaphragmatic organs.

On the other hand, if outer accumulation pixels are present, the extraction unit 4 estimates the pixel values of the accumulation in the subdiaphragmatic organs based on the pixel values from the outer peripheral pixels on the outward line to the outer accumulation pixels.

Figure 7 is a diagram illustrating the process of estimating pixel values of accumulation in subdiaphragmatic organs based on pixel values from the peripheral pixels to the outer accumulation pixels. In Figure 7, the horizontal axis represents the position along the outward line, and the vertical axis represents the pixel value. Also, the threshold is T, the specific distance is R, the peripheral pixels are C, and the outer accumulation pixels are D.

The extraction unit 4 performs fitting to approximate the change F in pixel values from the outer peripheral pixel C to the outer accumulation pixel D on the outward line with a Gaussian function, and obtains a Gaussian function G that approximates the change F. The extraction unit 4 then identifies the pixel values represented by the Gaussian function G as pixel values of accumulation in the subdiaphragmatic organs. Note that a function other than a Gaussian function may be used to approximate the change F.

Returning to the explanation of Figure 6, once the pixel values of the accumulation in the subdiaphragmatic organs have been estimated, the extraction unit 4 subtracts the pixel values of the accumulation in the subdiaphragmatic organs corresponding to each peripheral pixel from the pixel values of the tomographic image data to extract myocardial image data (step S304), and ends the process. In the example of Figure 7, the pixel values represented by curve H, obtained by subtracting the pixel values of the accumulation in the subdiaphragmatic organs represented by Gaussian function G from the pixel values represented by pixel value change F, become the pixel values of the myocardial image data.

In step S304, if there is a pixel whose pixel value in the tomographic image data minus the pixel value of the accumulation in the subdiaphragmatic organs is negative, the extraction unit 4 sets the pixel value of that pixel to zero. Furthermore, the extraction unit 4 sets the pixel value of any pixel outside the outer accumulation pixel or outside a position a specific distance away from the outer perimeter pixel along the outward line to zero.

In addition, in step S303, the change in pixel value F from the outer peripheral pixel C to the outer accumulation pixel D was approximated using a Gaussian function G. However, for example, the change in pixel value from the high accumulation position M within the region of interest to the outer peripheral pixel C may also be approximated using a Gaussian function. In this case, the myocardial image data is extracted by replacing the pixels outside the high accumulation position M with pixels represented by a Gaussian function.

As described above, according to this embodiment, the setting unit 3 sets a region of the tomographic image data that depicts the myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, as a region of interest based on a reference pixel value, which is the maximum pixel value of the region above the center of the heart in the tomographic image data before smoothing processing. The extraction unit 4 extracts myocardial tomographic image data that shows the myocardial distribution from the tomographic image data based on the region of interest. The image processing unit 5 performs smoothing processing on the myocardial tomographic image data. Therefore, the smoothing processing is performed after the myocardial tomographic image data that shows the myocardial distribution is extracted from the tomographic image data. Therefore, it is possible to generate highly visible image data while removing the influence of radiopharmaceutical accumulation in organs below the diaphragm in the tomographic image data, without having to perform multiple imaging of the subject, thereby reducing the burden on the subject and improving diagnostic accuracy.

In addition, in this embodiment, the setting unit 3 measures pixel values of the tomographic image data along each of a plurality of radial lines extending three-dimensionally radially from the center of the heart, and for each radial line, identifies the myocardial outer wall position corresponding to the outer wall of the myocardium in the tomographic image data based on highly concentrated pixels, which are pixels whose pixel values are maximum and equal to or greater than a certain percentage of the reference pixel value, and sets the region of interest based on the myocardial outer wall position. This makes it possible to appropriately set the region of interest.

In addition, in this embodiment, when there is one highly accumulating pixel, the setting unit 3 identifies a position a certain distance outward from the highly accumulating pixel as the myocardial outer wall position, and when there are two or more highly accumulating pixels, it identifies the position of the pixel with the smallest pixel value between the highly accumulating pixel closest to the center and the second-closest to the center as the myocardial outer wall position. This makes it possible to appropriately identify the myocardial outer wall position, thereby enabling more appropriate setting of the region of interest.

In addition, in this embodiment, the region of interest is an approximately heart-shaped region, which is a region connecting the positions of the myocardial outer wall on radial lines, or an ellipsoid region, the shape of which approximates an ellipsoid. This makes it possible to set the region of interest to an appropriate region that captures the heart.

In addition, in this embodiment, for each perimeter pixel on the perimeter of the region of interest, if there is an outer accumulation pixel, which is a pixel whose pixel value is a maximum and is equal to or greater than a certain percentage of the reference pixel value, along an outward line extending outward from the perimeter pixel and within a specific distance from the perimeter pixel, the extraction unit 4 extracts myocardial tomographic image data by subtracting the pixel value of the accumulation in the subdiaphragmatic organs, which is a pixel value corresponding to the radiopharmaceutical that has accumulated in the subdiaphragmatic organs, from the pixel values of the pixels on the outward line based on the pixel values along the outward line. This allows myocardial tomographic image data to be extracted appropriately.

In addition, in this embodiment, the extraction unit 4 approximates the change in pixel values from the outer peripheral pixels along the outward line to the outer accumulation pixels using a Gaussian function, and subtracts the pixel values represented by this Gaussian function from the pixel values of the pixels on the outward line as pixel values of accumulation in the subdiaphragmatic organs. This makes it possible to appropriately determine the pixel values of accumulation in the subdiaphragmatic organs, thereby enabling more appropriate extraction of myocardial tomographic image data.

In addition, in this embodiment, for each peripheral pixel, if an outer accumulation pixel is present, the extraction unit 4 sets all pixel values in the area outside the outer accumulation pixel along the outward line to zero; if no outer accumulation pixel is present, the extraction unit 4 sets all pixel values in the area outside a position a specific distance outward from the peripheral pixel in the tomographic image data to zero. This makes it possible to appropriately extract the myocardial distribution, which is the distribution of radiopharmaceuticals that have accumulated in the myocardium.

In addition, in this embodiment, the image processing unit 5 inversely converts the myocardial tomographic image data into projection data and performs smoothing processing on the myocardial tomographic image data based on the projection data. This makes it possible to generate the desired myocardial tomographic image.

Here, to evaluate the effect of radiopharmaceutical accumulation in subdiaphragmatic organs on myocardial tomographic image data, we compared myocardial tomographic image data obtained using the image processing method of the present disclosure with myocardial tomographic image data obtained using the image processing method of the reference example.

Figure 8 shows the image processing method of the present disclosure and the image processing method of the reference example. Specifically, Figure 8(a) shows the image processing method of the reference example, and Figure 8(b) shows the image processing method of the present disclosure.

In the image processing method of the reference example shown in Figure 8(a), a smoothing process is performed on the tomographic image data generated by the reconstruction process, followed by a resection transformation process and a myocardial extraction process. On the other hand, in the image processing method of the present disclosure shown in Figure 8(b), as described in the embodiment, a smoothing process is performed on the tomographic image data generated by the reconstruction process, followed by a resection transformation process and a myocardial extraction process. Note that in the myocardial extraction process, estimation removal to remove the influence of radiopharmaceutical accumulation in subdiaphragmatic organs on the region of interest is not performed, and all pixel values in regions other than the region of interest in each tomographic image data are set to zero.

Figure 9 shows myocardial tomographic image data obtained by the image processing method of the reference example and myocardial tomographic image data obtained by the image processing method of the present disclosure.

The projection data was acquired using a Siemens Symbia T6 equipped with a low-energy, high-resolution collimator, and data processing was performed using a Siemens nuclear medicine image processing workstation, SYNGO Mi Apps (version VA60C), and Fujifilm Toyama Chemical's DRIP and cardioBULL. The dose calibrator used was an Aloka Curiemeter IGC-7.

The subject used was a Kyoto Chemical Industry Co., Ltd. heart-liver HL-type phantom. A polypropylene cylindrical container (inner diameter 3.5 cm, length 10.0 cm, volume 100.0 mL) simulating a subdiaphragmatic organ collection was attached in close contact with the outside of the inferior wall of the left ventricular myocardium. Two types of subjects were prepared: one with no left ventricular myocardial defect and one with a 2.0 cm diameter circular defect chip attached to the center of the inferior wall of the left ventricular myocardium. Hereinafter, tomographic image data acquired when there is no left ventricular myocardial defect will be referred to as defect-free data, and tomographic image data acquired when a circular defect chip is attached will be referred to as defect-containing data.

The radiopharmaceutical nuclide was ^99mTc , and the amount of ^99mTc injected into the left ventricular myocardium (volume 120 mL) was set to 14.8 MBq (123 kBq/mL), assuming that approximately 2% of the administered dose accumulates in the myocardium in clinical settings. ^99mTc was injected into the cylindrical container so that the radioconcentration ratios of the cylindrical container to the left ventricular myocardium were 0 (water only), 0.5, 0.75, 1.0, 1.25, 1.5, 2.0, and 3.0. Figure 9 shows the myocardial tomographic image data (specifically, the central slice of the short-axis tomographic image data). Only water was injected into the left and right ventricular cavities, mediastinum, liver, and stomach. In addition, the image reconstruction methods used in the image reconstruction process are FBP, OSEM, OSEM-PR, which is OSEM with resolution recovery (RR), and OSEM-ACSCRR, which is OSEM with attenuation correction (AC), scatter correction (SC), and resolution recovery (PR), and Figure 9 shows myocardial tomographic image data obtained by each image reconstruction method.

Each myocardial tomographic image data was evaluated as follows:

Specifically, circumferential profile curves (CPCs) were created at 1-degree intervals within the region of interest in the central slice of the short-axis tomographic image data, and the maximum pixel value in the upper half of the region was normalized to 100%. In clinical practice, a %Uptake (the ratio of pixel values to the maximum pixel value) of 70% or less is often considered to indicate a significant blood flow abnormality. Therefore, for data with missing data, the influence of subdiaphragmatic organ accumulation was deemed significant when the minimum value in the 90-degree region on the inferior wall was 70% or greater. For data without missing data, the influence of subdiaphragmatic organ accumulation was deemed significant when the maximum value in the 90-degree region on the inferior wall was 143 (100/0.7) or greater, as this could indicate that walls other than the inferior wall may appear to have blood flow abnormalities.

For missing data, in the image processing method of the reference example, when the image reconstruction methods were FBP and OSEM, the %Uptake was 70% or more when the radioactivity concentration ratio of the cylindrical container was 0.75 or more, and when the image reconstruction methods were OSEM-PR and OSEM-ACSCPR, which used PR, the %Uptake was 70% or more when the radioactivity concentration ratio of the cylindrical container was 1.0 or more.

On the other hand, with the image processing method disclosed herein, when the image reconstruction method was FBP, the %Uptake was 70% or more when the radiation concentration ratio of the cylindrical container was 1.75 or more; when the image reconstruction method was OSEM, the %Uptake was 70% or more when the radiation concentration ratio of the cylindrical container was 1.25 or more; and when the image reconstruction method was OSEM-PR, the %Uptake was 70% or more when the radiation concentration ratio of the cylindrical container was 3.0 or more. Furthermore, when the image reconstruction method was OSEM-ACSCPR, the %Uptake was less than 70% at all radiation concentration ratios.

Furthermore, for data without any missing parts, in the image processing method of the reference example, when the image reconstruction method was FBP, the %Uptake was 143% or greater when the radioactivity concentration ratio of the cylindrical container was 2.0 or greater, and for other methods, the %Uptake was 143% or greater when the radioactivity concentration ratio of the cylindrical container was 1.5 or greater. On the other hand, in the image processing method of the present disclosure, the Uptake was less than 143% for all image reconstruction methods.

Therefore, the image processing method disclosed herein has been shown to reduce the effects of accumulation of subdiaphragmatic organs and improve diagnostic accuracy.

The above-described embodiments and examples of the present disclosure are illustrative examples of the present disclosure and are not intended to limit the scope of the present disclosure to only those embodiments. Those skilled in the art may implement the present disclosure in various other forms without departing from the scope of the present disclosure.

1: Acquisition unit, 2: Reconstruction unit, 3: Setting unit, 4: Extraction unit, 5: Image processing unit, 100: Image processing device, 101: Input/output device, 102: Auxiliary storage device

Claims (8)

An image processing method performed by an image processing device,
the image processing device sets a region that shows a myocardial distribution, which is the distribution of the radiopharmaceutical accumulated in the myocardium, on a tomographic image that shows the distribution of the radiopharmaceutical used in the myocardial SPECT examination or the myocardial PET examination;
the image processing device removes the influence of accumulation of organs under the diaphragm in a region other than the region imaging the myocardial distribution from the tomographic image, thereby extracting a myocardial tomographic image consisting of the region imaging the myocardial distribution and the region other than the region imaging the myocardial distribution from which the influence has been removed ;
The image processing method includes performing a smoothing process on the myocardial tomographic image by the image processing device . The image processing method according to claim 1 , wherein in extracting the myocardial tomographic image, the image processing device removes the influence by subtracting pixel values indicating accumulation of the organs below the diaphragm from the tomographic image. The image processing method according to claim 1 , wherein, in extracting the myocardial tomographic image, the image processing device removes the influence by setting pixel values of regions other than the region showing the myocardial distribution to zero. The image processing method according to claim 1 , wherein in setting the region showing the myocardial distribution, the image processing device sets a region specified by a user as the region showing the myocardial distribution. The image processing method described in any one of claims 1 to 4, wherein the tomographic image is a tomographic image before smoothing processing is performed. The image processing method described in any one of claims 1 to 5, wherein the smoothing process is a process for smoothing changes in pixel values on the image. a setting unit that sets a region that shows a myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, in a tomographic image that shows the distribution of the radiopharmaceutical used in the myocardial SPECT examination or the myocardial PET examination;
an extracting unit that removes the influence of accumulation of organs under the diaphragm in a region other than the region where the myocardial distribution is imaged from the tomographic image, thereby extracting a myocardial tomographic image consisting of the region where the myocardial distribution is imaged and the region other than the region where the myocardial distribution is imaged from which the influence has been removed ;
a smoothing unit that performs smoothing processing on the myocardial tomographic image; and a program for causing a computer to realize the above-mentioned smoothing unit. a setting unit that sets a region that shows a myocardial distribution, which is the distribution of the radiopharmaceutical that has accumulated in the myocardium, in a tomographic image that shows the distribution of the radiopharmaceutical used in the myocardial SPECT examination or the myocardial PET examination;
an extracting unit that removes the influence of accumulation of organs under the diaphragm in a region other than the region where the myocardial distribution is imaged from the tomographic image, thereby extracting a myocardial tomographic image consisting of the region where the myocardial distribution is imaged and the region other than the region where the myocardial distribution is imaged from which the influence has been removed ;
a smoothing unit that performs a smoothing process on the myocardial tomographic image.