Myocardial energy calculation method, myocardial energy calculation system, myocardial energy calculation device, and myocardial energy calculation program

Abstract

To solve a problem of not being able to measure myocardial blood flow and myocardial dynamics simultaneously from tomographic images of the heart, while the conventional nuclear medicine imaging apparatus lacks sufficient sharpness and temporal resolution resulting in limitations in tissue morphology and dynamics analysis, the myocardial blood flow and the myocardial dynamics are measured by separate imaging tests although they are closely related, and each of measured values is used as an independent index as it does not strictly reflect the patient's state at the same time.SOLUTION: The present invention relates to a myocardial energy calculation method etc. in which not only myocardial flow reserve (MFR) is calculated based on tomographic images of the heart acquired from a nuclear medicine imaging system but also myocardial strain ratio (MSR) which represents a ratio of myocardial strain under vasodilator stress to that at rest is calculated by applying a feature tracking technology to the tomographic image, so as to make it possible to simultaneously measure myocardial blood flow and myocardial dynamics from the same tomographic image of the heart and facilitate the evaluation of myocardial condition with a single index, the myocardial energy calculated as the square root of the sum of the squares of these values.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to obtaining information on myocardial blood flow and myocardial dynamics (myocardial motion) from image data representing myocardial blood flow, etc., obtained by a nuclear medicine imaging technique such as a PET (Positron Emission Tomography) device or a SPECT (Single Photon Emission Computed Tomography) device, and calculating an index based on both pieces of information. Specifically, the present invention relates to a myocardial energy calculation method, system, device, and program (hereinafter referred to as "myocardial energy calculation method, etc.") for obtaining quantitative values ​​for myocardial blood flow and myocardial dynamics during the same examination, calculating a new index representing myocardial kinetic energy from each quantitative value, and displaying the myocardial energy based on a segment model in which the myocardium is divided into multiple regions. [Background technology]

[0002] Nuclear medicine imaging devices such as PET devices and SPECT devices can image the physiological functions of living organisms, such as blood flow and metabolism in organs. For example, PET devices and SPECT devices administer a drug containing radioactivity (a radiopharmaceutical) into the body, capture images of the distribution of radiation emitted from the administered radiopharmaceutical from various directions outside the body using a special camera, and reconstruct the images using a computer to produce a tomographic image (e.g., Non-Patent Document 1, Non-Patent Document 2). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Yasuhiro Wada, Fundamentals of Nuclear Medicine Technology "Principles of Imaging with PET Devices," Clinical Nuclear Medicine Vol. 48 No. 1 2015. URL http: / / www.rinshokaku.com / contents / pdf / sec7 / 6.pdf (Retrieved December 3, 2021) [Non-patent document 2] Masaya Suda, Fundamentals of Nuclear Medicine Technology "From SPECT Imaging to Image Processing," Clinical Nuclear Medicine Vol. 47 No. 3 2014. URL http: / / www.rinshokaku.com / contents / pdf / sec7 / 2.pdf (Retrieved December 3, 2021) Summary of the Invention [Problem to be solved by the invention]

[0004] Previous nuclear medicine imaging devices lacked sufficient sharpness and temporal resolution, limiting their ability to analyze tissue morphology and dynamics. This made it difficult to simultaneously measure myocardial blood flow and myocardial dynamics from the same cross-sectional image of the heart. Therefore, despite their close relationship, myocardial blood flow and myocardial dynamics were measured using separate imaging tests. The individual measurements obtained from separate imaging tests were used as independent indices, rather than strictly reflecting the patient's condition at the same time. Furthermore, the image data obtained from separate imaging tests differed in size and pixel count, requiring adjustments to match the size and pixel count, making it difficult to accurately measure the patient's condition at the same time.

[0005] While these issues have arisen with conventional imaging tests, recent advances in nuclear medicine imaging technology have improved the image quality of tomographic images, making it possible to clearly obtain the contours of organs such as the heart. Therefore, in this invention, not only do we calculate blood flow values ​​such as myocardial flow reserve (MFR) based on a cross-sectional image of the heart (e.g., a PET image) captured by a nuclear medicine imaging device (e.g., a PET device), but we also apply dynamic analysis techniques such as feature tracking to the cross-sectional image to calculate dynamic values ​​such as the ratio of myocardial strain under vasodilatory stress (referred to here as "stress strain") to strain at rest (referred to here as "resting strain") (referred to here as "myocardial strain ratio (MSR)"). This makes it possible to measure myocardial blood flow and myocardial dynamics at the same time from the same cross-sectional image of the heart (an image of the same size and number of pixels). Furthermore, by calculating myocardial energy, which is calculated as the square root of the sum of the squares of these values, we provide a myocardial energy calculation method, etc., which, rather than evaluating myocardial blood flow and myocardial dynamics using separate indices, expresses the kinetic energy of the myocardium using a single index called myocardial energy, making it easier to evaluate the state of the myocardium. [Means for solving the problem]

[0006] As one embodiment of the myocardial energy calculation method according to the present invention, the myocardial energy calculation method includes: acquiring cross-sectional images of the heart from a nuclear medicine imaging device; calculating myocardial flow reserve (MFR) representing the ratio of myocardial blood flow under stress to resting blood flow from the tomographic image; calculating a peak strain of the myocardium from the tomographic image; calculating a myocardial strain ratio (MSR) representing the ratio of stress strain to resting strain of the myocardium from the peak strain; calculating myocardial energy (ME) indicative of kinetic energy associated with the myocardium based on the MFR and the MSR; Including, The ME is characterized by being calculated as the square root of the sum of the squares of the MFR and the MSR.

[0007] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the myocardial energy (ME) is calculated by:

number

[0008] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the nuclear medicine imaging device is a PET device or a SPECT device, The tomographic image is a PET image taken by the PET device or a SPECT image taken by the SPECT device.

[0009] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the tomographic image includes a plurality of cross-sectional images of the entire myocardium, Each of the plurality of cross-sectional images includes a predetermined number of consecutive frames corresponding to a cardiac cycle.

[0010] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the step of calculating the MFR and the step of calculating the MSR are performed before: receiving an input for determining a plurality of feature points along the endocardium recorded in a predetermined frame image among the predetermined number of consecutive frames of images; tracking the plurality of feature points across the predetermined number of consecutive frames of images using a feature tracking technique; generating a time strain curve with the cardiac cycle and the distance between each of the plurality of feature points, the distance between each of the two adjacent feature points being expressed by two-dimensional coordinates, as axes; determining the peak strain as a representative value of the peak value in the time strain curve; The present invention is characterized by comprising:

[0011] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the load strain and the rest strain are each a peak value of an endocardial length calculated from the sum of distances of the plurality of feature points and normalized to the endocardial length at end diastole in the cardiac cycle.

[0012] A preferred embodiment of the myocardial energy calculation method according to the present invention is characterized in that the ME includes values ​​corresponding to multiple regions of the myocardium and is displayed by fitting it to a segment model in which the myocardium is divided into the multiple regions.

[0013] In a preferred embodiment of the myocardial energy calculation method according to the present invention, the segment model includes segments corresponding to 16 regions of the myocardium.

[0014] As one embodiment of the myocardial energy calculation system according to the present invention, the myocardial energy calculation system includes: a nuclear medicine imaging device; Information processing device Including, the nuclear medicine imaging device captures and stores tomographic images of the heart; The information processing device includes: acquiring the tomographic image from the nuclear medicine imaging device; Calculating myocardial flow reserve (MFR), which represents the ratio of myocardial blood flow under stress to resting blood flow, from the tomographic image; Calculating a peak strain of the myocardium from the tomographic image; Calculating a myocardial strain ratio (MSR) representing the ratio of myocardial stress strain to resting strain from the peak strain; calculating myocardial energy (ME) indicative of kinetic energy related to the myocardium based on the MFR and the MSR; The ME is characterized by being calculated as the square root of the sum of the squares of the MFR and the MSR.

[0015] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the myocardial energy (ME) is calculated by:

number

[0016] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the nuclear medicine imaging device is a PET device or a SPECT device, The tomographic image is a PET image taken by the PET device or a SPECT image taken by the SPECT device.

[0017] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the tomographic image includes a plurality of cross-sectional images of the entire myocardium, Each of the plurality of cross-sectional images includes a predetermined number of consecutive frames corresponding to a cardiac cycle.

[0018] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the information processing device calculates the MFR and the MSR by: accepting an input for determining a plurality of feature points along the endocardium recorded in a predetermined frame image among the predetermined number of consecutive frames of images; tracking the plurality of feature points across the predetermined number of consecutive frames of images using a feature tracking technique; For each of the normalized distances between two adjacent points represented by the peak values, a time strain curve is generated with the cardiac cycle and the distance between the two points as axes; The method is characterized in that it includes determining the peak strain by using a peak value as a representative value in the time strain curve.

[0019] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the load strain and the rest strain are each calculated by summing the distances of the plurality of characteristic points to calculate the endocardial length, and normalizing the endocardial length at the end of the diastole in the cardiac cycle, and the peak value of this normalized length is used.

[0020] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the ME includes values ​​corresponding to a plurality of regions of the myocardium, The information processing device displays the ME by fitting it to a segment model in which the myocardium is divided into the plurality of regions.

[0021] In a preferred embodiment of the myocardial energy calculation system according to the present invention, the segment model includes segments corresponding to 16 regions of the myocardium.

[0022] As one embodiment of the myocardial energy calculation device according to the present invention, the myocardial energy calculation device comprises: The method is characterized by performing each step of the myocardial energy calculation method according to any one of the embodiments of the myocardial energy calculation method.

[0023] As one embodiment of the myocardial energy calculation program according to the present invention, the myocardial energy calculation program is characterized in that, when executed by a computer, the computer functions as the myocardial energy calculation device. [Effects of the Invention]

[0024] The myocardial energy calculation method according to the present invention calculates the MFR based on cross-sectional images such as PET images of the heart taken by a nuclear medicine imaging device such as a PET device, and also calculates the MSR by using dynamic analysis techniques such as feature tracking on the cross-sectional images, thereby making it possible to measure myocardial blood flow and myocardial dynamics simultaneously from the cross-sectional images of the heart, and to perform an examination that accurately reflects the patient's condition at a certain time. This makes it possible to measure myocardial blood flow and myocardial dynamics from image data of the same size and the same number of pixels, eliminating the need for adjustments such as standardizing the size and number of pixels of each image data acquired in the conventional separate imaging examinations.

[0025] Furthermore, information on myocardial blood flow and myocardial dynamics (MFR, MSR) contains numerical information for the entire myocardial region, and can be generalized by applying it to a segment model that divides the myocardium into multiple regions, such as the conventional myocardial segment model used in nuclear cardiology (16 segments excluding the apical segment of the 17-segment model).

[0026] By calculating myocardial energy, which is calculated as the square root of the sum of the squares of MFR and MSR, it is possible to express the kinetic energy of the myocardium with a single index, myocardial energy, rather than evaluating myocardial blood flow and myocardial dynamics with separate indexes, making it easier to evaluate the condition of the myocardium. [Brief explanation of the drawings]

[0027] [Figure 1] 1 is a diagram showing an overview of a myocardial energy calculation system according to one embodiment of the present invention. [Figure 2] 1 is a flowchart showing the flow of a myocardial energy calculation method according to one embodiment of the present invention. [Figure 3] FIG. 1 is a diagram showing an example of a myocardial blood flow image generated by a nuclear medicine imaging device. [Figure 4] FIG. 1 is a diagram showing an example of a model of myocardial segmentation (myocardial segment model) in nuclear cardiology. [Figure 5]FIG. 5 is a diagram showing an example of myocardial flow reserve (MFR) calculated from a cross-sectional image of the heart (PET image) acquired by a PET device, applied to the myocardial segment model shown in FIG. 4 and displayed. [Figure 6] FIG. 10 is a diagram showing feature points determined from the contour of the endocardium designated by a pointer in a PET image of the heart at a certain time (any one frame). [Figure 7] FIG. 1 is a diagram illustrating an overview of a mechanism for tracking feature points using feature-tracking technology. [Figure 8] FIG. 10 is a diagram showing an example of tracking multiple feature points in a PET image. [Figure 9] 9 is a graph showing an example of the results (time strain curve) of tracking feature points in the PET image shown in FIG. 8. [Figure 10] FIG. 10 is a diagram illustrating an example of peak values ​​of a time strain curve. [Figure 11] FIG. 1 is a diagram showing an example of displaying a myocardial segment model based on MFR calculated from a PET image of a patient and a myocardial segment model based on myocardial energy (ME). [Figure 12] FIG. 1 is a diagram showing the results of evaluation of the usefulness of myocardial energy using a receiver operating characteristic (ROC) curve. [Figure 13] FIG. 1 shows the results of evaluation of the usefulness of myocardial energy using Kaplan-Meier curves. DETAILED DESCRIPTION OF THE INVENTION

[0028] An embodiment of the present invention will be described below with reference to the drawings. In all drawings used to explain the embodiment, the same components are generally designated by the same reference numerals, and repeated explanations will be omitted. The individual embodiments of the present invention are not independent, and can be appropriately implemented in combination with each other.

[0029] 1 shows an overview of a functional ischemia detection system according to one embodiment of the present invention. The functional ischemia detection system according to the present invention includes an image analysis device 100, a nuclear medicine imaging device 200 such as a PET device or a SPECT device, and a data processing device 210. The nuclear medicine imaging device 200 may be included in the same housing as the data processing device 210 and configured as an integrated unit. The image analysis device 100 and the data processing device 210 are connected via a network N.

[0030] The image analysis device 100 and the data processing device 210 have the hardware configuration of a general computer (information processing device), and include, for example, hardware resources such as a CPU (Central Processing Unit), memory consisting of ROM (Read Only Memory) and RAM (Random Access Memory), a bus, an input / output interface, an input unit, an output unit, a storage unit, and a communication unit.

[0031] The CPU executes various processes according to programs stored in the memory or programs loaded from the storage unit into the memory. The CPU can execute, for example, a program that causes a computer to function as the image analysis device of the present invention. It is also possible to implement at least some of the functions of the image analysis device in hardware using an application-specific integrated circuit (ASIC) or the like. The same applies to the other data processing devices 210 of the present invention.

[0032] The memory also stores data necessary for the CPU to execute various processes as appropriate. The CPU and memory are interconnected via a bus. An input / output interface is also connected to this bus. An input unit, an output unit, a storage unit, and a communication unit are connected to the input / output interface. The input unit is composed of various buttons, a touch panel, a microphone, etc., and inputs various information in response to instructions from users of the image analysis device 100 and the data processing device 210. The output unit is composed of a display, a speaker, etc., and outputs image data and audio data. The storage unit is composed of semiconductor memory such as DRAM (Dynamic Random Access Memory) or a hard disk, and stores various data. The communication unit realizes communication with other devices.

[0033] The image analysis device 100 can acquire and store cardiac tomographic image (PET image or SPECT image) data from, for example, a nuclear medicine imaging device 200 or a data processing device 210. In the embodiment shown in Fig. 1, the image analysis device 100 can acquire tomographic image data transferred from the data processing device 210 connected to the nuclear medicine imaging device 200 via a network N such as a dedicated line or a public line.

[0034] Fig. 2 is a flowchart showing the flow of a myocardial energy calculation method according to one embodiment of the present invention. Each step of the myocardial energy calculation method shown in Fig. 2 is performed, for example, by an image analysis device 100, which is an information processing device in the myocardial energy calculation system shown in Fig. 1. First, the image analysis device 100 acquires tomographic images of the heart from the nuclear medicine imaging device 200 (step S1). For example, the nuclear medicine imaging device 200 may store tomographic images, which are obtained by capturing time-series images of the chest (heart) of a subject such as a patient, in the data processing device 210, and transmit the tomographic images from the data processing device 210 to the image analysis device 100.

[0035] The nuclear medicine imaging device 200 may be, for example, a PET device, a SPECT device, or the like, and the tomographic images may be PET images or SPECT images acquired by such devices. The tomographic images include multiple cross-sectional images of the entire myocardium, i.e., images of cross sections corresponding to all regions of the myocardium. Each of the multiple cross-sectional images includes a predetermined number of consecutive images corresponding to the cardiac cycle. For example, a PET device includes blood flow images (cross-sectional images) of approximately 15 slices of the entire myocardium, and the blood flow images of these cross sections include a predetermined number of consecutive images (e.g., generally 16 frames) corresponding to the cardiac cycle (all cardiac phases). In this embodiment, if the size of the cross-sectional images included in the tomographic images is 128 pixels x 128 pixels, blood flow image information of 128 pixels x 128 pixels x 15 slices x 16 frames can be obtained at rest and under stress (see FIG. 3).

[0036] Figure 3 shows an example of a myocardial blood flow image generated by a nuclear medicine imaging device. For comparison, Figure 3 shows stress and rest images of myocardial blood flow, which are PET images obtained in an ammonia PET test, divided into normal, angina pectoris (positive ischemia), and infarction states. Figure 3 also shows normal stress and rest images of myocardial blood flow, which are SPECT images obtained in a SPECT test.

[0037] In the PET images obtained in an ammonia PET test, myocardial blood flow is visualized as a circle in the stress and rest images of a normal state. In contrast, in the stress and rest images of angina pectoris (positive ischemia) and infarction, myocardial blood flow is not visualized as a circle, but as a partially missing circle (semicircle), and the missing area (indicated by the arrow in Figure 3) can be identified as the area where myocardial blood flow is reduced. In a SPECT test, the state of myocardial blood flow can be identified in a similar manner.

[0038] 3, the image analysis device 100 can collect blood flow image data twice, once at rest and once under vasodilator stress, as one set. That is, the image analysis device 100 can acquire blood flow image data of the entire myocardial region at rest (stress blood flow image) and blood flow image data of the entire myocardial region under stress (resting blood flow image) from the PET device, and can calculate the myocardial flow reserve (MFR) by dividing the blood flow obtained from the stress blood flow image by the blood flow obtained from the resting blood flow image (calculating the blood flow obtained from the stress blood flow image / blood flow obtained from the resting blood flow image).

[0039] Furthermore, if necessary, the image analysis device 100 can extract MFR data of only the myocardial region from the MFR data (e.g., 128 pixels x 128 pixels x 15 slices x 16 frames) obtained from the image data, fit it to a conventional myocardial segment model (see Figure 4), and display it in color (black and white shading in Figure 5).

[0040] Figure 4 shows an example of a myocardial segmentation model (myocardial segment model) used in nuclear cardiology. The example shown in Figure 4 is a typical 17-segment model (segment #17 corresponds to the apex, and segments #1 to #16 correspond to the myocardial regions (base, base-lateral wall, etc.)). The 17-segment model divides the base, central, and apex of the short-axis myocardial tomography image into 6, 6, and 4 segments, respectively, plus one segment from the center slice of the long-axis vertical tomography to the apex, for a total of 17 segments. Figure 4 also shows the relative positions of the anterior wall, lateral wall, posterior wall, and septum of the myocardium in the 17-segment model, as well as the relative positions of the left anterior descending artery (LAD), left circumflex artery (LCX), and right coronary artery (RCA).

[0041] Figure 5 shows an example of myocardial blood flow reserve (MFR) calculated from cross-sectional images of the heart (PET images) acquired by a PET device, fitted to the myocardial segment model shown in Figure 4. In this way, by fitting the MFR calculated from stress blood flow images / rest blood flow images to the myocardial segment model and displaying it in color (shown in black and white shading in Figure 5), the state of myocardial blood flow can be easily grasped.

[0042] 2, the process of calculating the MFR described above corresponds to the processes (steps S2 to S4) after acquiring the tomographic image data of the heart (after step S1). To calculate the MFR and the MSR (described later), the image analyzing device 100 first accepts an input for specifying a plurality of feature points in a cross-sectional image of the heart at a certain time included in the tomographic image data, and determines an object to be analyzed (step S2).

[0043] FIG. 6 shows feature points determined from the endocardial contour designated by a pointer in a PET image of the heart at a certain time (any frame). For example, a user of image analysis device 100 may use a pointing device such as a mouse to designate the endocardial contour from a cross-sectional image (PET image) at a certain time with a pointer. Image analysis device 100 then determines multiple feature points (11 or 12 feature points in the example shown in FIG. 6 ) from the contour using a dynamic analysis technique such as existing feature tracking technology. In this manner, the analysis target is determined. Feature points can also be determined by setting multiple points (e.g., 5 to 7 points) including a start point, an end point, and a curved portion. The analysis target can be set arbitrarily, and may be a region, or may be automatically determined using image processing or image recognition using artificial intelligence. Alternatively, a user of image analysis device 100 may manually designate multiple points on the endocardial surface in a PET image using a pointing device such as a mouse, and automatically connect the multiple points into a line using a density gradient of approximately 10% to extract the endocardial contour.

[0044] After determining the analysis target (step S2), the image analysis device 100 tracks the analysis target using a feature-tracking technique (step S3). FIG. 7 shows an overview of the mechanism for tracking feature points using the feature-tracking technique. The image analysis device 100 can track feature points (points) using a template matching technique by applying the feature-tracking technique. As shown in FIG. 7, the image analysis device 100 tracks the changes (movements) of feature points during the cardiac cycle from end-diastole through end-systole to mid-diastole. In template matching, feature points in a cross-sectional image (PET image) at end-diastole are used as a template image, and feature points that substantially match the template image are searched for and tracked in each of a predetermined number of consecutive images corresponding to the cardiac cycle. This allows tracking of feature points during the cardiac cycle.

[0045] The size of the template image and the search area in template matching can be set arbitrarily. For example, if the size of a PET image is 128 pixels x 128 pixels, the size of the template image can be set to 24 pixels x 24 pixels, and the search area can be set to 32 pixels x 32 pixels. Note that, for simplicity of explanation, Fig. 7 shows template matching, search, and tracking for one feature point, but the same mechanism applies to tracking multiple feature points.

[0046] For example, Fig. 8 shows an example of tracking multiple feature points in PET images. Using template matching, multiple feature points can be tracked in each of the successive images from myocardial expansion to contraction, as shown in Figs. 8(a) to (d). Referring to Fig. 2, after tracking the analysis target (feature points, etc.) in this way (after step S3), image analysis device 100 can calculate, based on the tracking results, myocardial flow reserve (MFR), which is an index representing myocardial blood flow, and myocardial strain ratio (MSR), which represents the ratio of myocardial stress strain to resting strain, as an index representing the state of myocardial dynamics (step S4).

[0047] MFR is a well-known index that represents the ratio of myocardial blood flow under stress to that under rest, and as mentioned above, it is calculated by dividing the blood flow obtained from the stress blood flow image by the blood flow obtained from the rest blood flow image (calculating the blood flow obtained from the stress blood flow image / blood flow obtained from the rest blood flow image). Figure 5 shows an example of the MFR for the entire myocardial region, fitted to the myocardial segment model (see Figure 4).

[0048] Myocardial strain is measured from blood flow image (cross-sectional image) data (e.g., 128 pixels x 128 pixels x 1 slice x 16 frames), and this measurement is performed for all slices (e.g., 16 slices) to calculate the strain of the entire myocardial region. Using the example of tracking feature points in a PET image shown in Figure 8, to measure myocardial strain, a time-strain curve can be plotted for each of the distances between two adjacent feature points, expressed in two-dimensional coordinates, with the cardiac cycle (time or number of frames) as the axis and the distance between the two points as the axis.

[0049] Figure 8 shows the tracking of 12 feature points. For example, if points 1 to 12 are defined counterclockwise from a certain feature point, the distances of 12 line segments (distances 1, 2, 3, ... 12) can be calculated from the two-dimensional coordinates of adjacent feature points. Figure 9 shows curves plotted with the sum of the distances between two adjacent points on the vertical axis and time (number of frames) for six regions on the horizontal axis. This quantifies the movement of each myocardial region from diastole to systole and back to diastole during the cardiac cycle. The upper part of Figure 9 reflects the movement of the myocardium in the circumferential and longitudinal directions. Similarly, the lower part of Figure 9 reflects the movement of the myocardium in the radial direction, which is perpendicular to the circumferential and longitudinal directions.

[0050] Based on the time strain curve generated in this way, the image analysis device 100 calculates the peak value of myocardial strain (herein referred to as "peak strain") from cross-sectional images such as PET images of the heart during vasodilatory stress and at rest, and calculates the myocardial strain ratio (MSR), which represents the ratio of stress strain to rest strain, from the peak strain. In the example of the time strain curve shown in Figure 9, the peak value is used as a representative value to determine the peak strain. The stress strain and rest strain of the myocardium can be calculated from the peak strain.

[0051] Fig. 9 is a graph showing an example of the results (time strain curve) of tracking feature points in the PET image shown in Fig. 8. The horizontal axis of the graph represents the frame number (corresponding to cardiac phase, time), and the vertical axis represents the endocardial length calculated from the sum of the distances between feature points, which is a value (%) obtained by normalizing the length at each cardiac phase with the length at end diastole. As shown in Fig. 9, it is possible to measure changes in myocardial strain for each of multiple feature points determined along the endocardium of the myocardium.

[0052] FIG. 10 shows an example of the peak value of a time strain curve. The myocardial strain value can be, for example, the peak value in the time strain curve. That is, the image analyzing device 100 draws a time strain curve based on the change in endocardial length throughout the cardiac cycle, and uses the peak value as a representative value. In the example of the time strain curve shown in FIG. 10, the horizontal axis represents the cardiac cycle (RR duration) (corresponding to the time and number of frames), and the vertical axis represents longitudinal strain (longitudinal strain).

[0053] In this way, the MSR can be calculated from the calculated myocardial strain of the entire myocardial region by calculating the load strain / resting strain, similar to the calculation of the MFR.

[0054] After calculating the MFR and MSR (after step S4), the image analysis device 100 can calculate the myocardial energy (ME), which indicates the kinetic energy of the myocardium, based on the MFR and MSR (step S5), as shown in FIG. 2. The ME is expressed as the square root of the sum of the squares of the MFR and MSR. For example, the myocardial energy (ME) can be calculated by:

number

[0055] After calculating the myocardial energy (ME) (after step S5), the image analysis device 100 can display a myocardial energy (ME) map (step S6). As the myocardial energy (ME) map, the image analysis device 100 can display the ME values ​​in color (shade) using, for example, a conventional myocardial segment model in nuclear cardiology (16 segments excluding the apical segment of the 17-segment model) (see FIG. 11(b)).

[0056] Figure 11 shows an example of a myocardial segment model based on MFR and ME calculated from PET images of a patient who required interventional treatment by percutaneous coronary intervention (PCI) approximately one year after the PET scan.

[0057] Figure 11(a) shows a myocardial segment model based on MFR, and Figure 11(b) shows a myocardial segment model (ME map) based on ME. The MFR shading (color display) shows little unevenness in the shading, and no abnormal myocardial regions are detected in the conventional PET index MFR < 2.0 (Figure 11(a)). However, the ME shading (color display) shows areas of decreased ME in segments #3, #9, #13, and #14 (see Figure 4), which correspond to the light areas in the shading. Thus, myocardial abnormalities that cannot be detected using the conventional index MFR can be detected by using the new index ME. If the ME map had been available at the time of this PET scan, it is possible that the highly invasive PCI treatment one year later would not have been necessary.

[0058] The results of evaluating the usefulness of myocardial energy (ME) are shown in Figures 12 and 13. First, Figure 12 shows the results of evaluating the usefulness of myocardial energy using an ROC curve. Ninety-five patients with coronary artery disease (22 with a future cardiac event) were included in the study. ME was calculated from each patient's PET images, and the entire myocardial region was fitted to a 16-segment model (see Figure 4; segment #17 was excluded from the calculation). The average ME of the 16 segments was defined as G-ME, and the average ME of the segments corresponding to the right coronary artery, left coronary artery, and anterior descending artery was defined as RCA-ME, LAD-ME, and LCX-ME, respectively. For comparison, MFR was calculated from each patient's PET images, and G-MFR, RCA-MFR, LAD-MFR, and LCX-MFR were calculated in the same way as ME.

[0059] As an evaluation method, ROC analysis was used to compare the cardiac event prediction ability of MFR < 2.0 and ME. The higher the ROC graph, the higher the prediction accuracy. The area to the lower right of the curve is defined as the AUC (Area Under the Curve), and the higher the AUC, the higher the prediction accuracy. The solid line in the graph indicates ME, and the dotted line indicates the prediction accuracy of MFR.

[0060] Figure 12 (a) is a graph comparing G-ME and G-MER, (b) is a graph comparing RCA-ME and RCA-MFR, (c) is a graph comparing LAD-ME and LAD-MFR, and (d) is a graph comparing LCX-ME and LCX-MFR. The vertical axis of the graph is sensitivity, which corresponds to the positive rate, and the horizontal axis is 1-specificity, which corresponds to the false positive rate. As is clear from graphs (a) to (d), the AUC of ME is broadly superior to the AUC of MFR in terms of diagnostic ability. Myocardial energy shows superior results in predicting cardiac events in the entire left ventricle and the three major regions when MFR is < 2.0.

[0061] Next, Figure 13 shows the results of an evaluation of the usefulness of myocardial energy using a Kaplan-Meier curve. As with the evaluation using the ROC curve, 95 patients with coronary artery disease (22 with a future cardiac event) were included in the study. ME was calculated from each patient's PET image, and the entire myocardial region was fitted to a 16-segment model (see Figure 4; segment #17 was excluded from the calculation). The average ME of the 16 segments was taken as G-ME. For comparison, MFR was also calculated from each patient's PET image, and G-MFR was calculated in the same way as ME.

[0062] As an evaluation method, Kaplan-Meier analysis was used. The subjects were divided into two groups using ME=62.4 as a cutoff, and the number of months on the horizontal axis was plotted against the event-free rate in each group on the vertical axis (see Figure 13(a)). Similarly, the subjects were divided into two groups using MFR=2.0 as a cutoff, and the number of months on the horizontal axis was plotted against the event-free rate in each group on the vertical axis (see Figure 13(b)).

[0063] The P value calculated from the Kaplan-Meier curve based on ME was 0.0034, and the P value calculated from the Kaplan-Meier curve based on MFR was 0.017. In statistical hypothesis testing, the P value is the probability that a test statistic will take that value under the null hypothesis; a smaller P value indicates a lower probability of the test statistic taking that value. The greater the difference between the two Kaplan-Meier curves, the better the prognostic indicator. Comparing Figures 13(a) and (b) reveals that, in long-term follow-up observations of 60 months or more, using G-ME ≥ 62.4, G-ME < 62.4 as the criteria for diagnosing cardiac risk is more accurate than using G-MFR ≥ 2.0, G-MFR < 2.0 as the criteria (note that n in parentheses indicates the number of patients).

[0064] The evaluation results shown in Figures 12 and 13 confirm that myocardial energy (ME) can be a more useful index than the conventional index (MFR). Note that, in the examples of one embodiment of the present invention, ammonia PET data is shown for a myocardial perfusion PET preparation; however, similar analysis is also possible for myocardial glucose metabolism (FluoroDeoxyGlucose; FDG) in addition to ammonia myocardial perfusion PET preparations. In other words, instead of calculating myocardial energy based on myocardial blood flow reserve (MFR) and myocardial strain (MSR), an energy map calculated based on glucose metabolism and strain can also be created.

[0065] In this way, the myocardial energy calculation method according to the present invention calculates the MFR based on cross-sectional images such as PET images of the heart taken by a nuclear medicine imaging device such as a PET device, and also calculates the MSR by using a dynamic analysis technique such as feature tracking on the cross-sectional images, thereby making it possible to measure myocardial blood flow and myocardial dynamics simultaneously from the cross-sectional images of the heart, and to perform examinations that accurately reflect the patient's condition at a certain time. This makes it possible to measure myocardial blood flow and myocardial dynamics from image data of the same size and the same number of pixels, eliminating the need for adjustments such as standardizing the size and number of pixels of image data acquired in conventional separate imaging examinations.

[0066] In addition, by calculating myocardial energy, which is calculated as the square root of the sum of the squares of MFR and MSR, it is possible to express the kinetic energy of the myocardium with a single index, myocardial energy, rather than evaluating myocardial blood flow and myocardial dynamics with separate indexes, making it easier to evaluate the condition of the myocardium.

[0067] Furthermore, display using a myocardial segment model (for example, the map display of the myocardial segment model shown in Figure 11) is a display method common to all imaging modalities, such as MRI, CT, ultrasound, and nuclear medicine (PET), so it is possible to calculate a new clinical value called ME by calculating information from 16 segments even when using different modalities. [Industrial Applicability]

[0068] The myocardial energy calculation method and the like according to the present invention can be used to assist in the diagnosis of heart disease and the like. [Explanation of symbols]

[0069] 100 Image analysis device 200 Nuclear Medicine Imaging Device 210 Data processing device N Network

Claims

1. 1. A method for calculating myocardial energy, comprising: acquiring cross-sectional images of the heart from a nuclear medicine imaging device; calculating myocardial flow reserve (MFR) representing the ratio of myocardial blood flow under stress to resting blood flow from the tomographic image; calculating a peak strain of the myocardium from the tomographic image; calculating a myocardial strain ratio (MSR) representing the ratio of stress strain to resting strain of the myocardium from the peak strain; calculating myocardial energy (ME) indicative of kinetic energy associated with the myocardium based on the MFR and the MSR; Including, A method for calculating myocardial energy, characterized in that the ME is calculated as the square root of the sum of the squares of the MFR and the MSR.

2. The myocardial energy (ME) is expressed as follows: A and B are arbitrary coefficients.

2. The myocardial energy calculation method according to claim 1, wherein the myocardial energy calculation method is expressed by the following formula:

3. the nuclear medicine imaging device is a PET device or a SPECT device, 3. The myocardial energy calculation method according to claim 1, wherein the tomographic image is a PET image taken by the PET device or a SPECT image taken by the SPECT device.

4. the tomographic image includes a plurality of cross-sectional images of the entire myocardium; 4. The myocardial energy calculation method according to claim 1, wherein each of the plurality of cross-sectional images includes a predetermined number of consecutive frames corresponding to a cardiac cycle.

5. Prior to the steps of calculating the MFR and calculating the MSR, receiving an input for determining a plurality of feature points along the endocardium recorded in a predetermined frame image among the predetermined number of consecutive frames of images; tracking the plurality of feature points across the predetermined number of consecutive frames of images using a feature tracking technique; generating a time strain curve with the cardiac cycle and the distance between the two points as axes for each distance between two adjacent points expressed by two-dimensional coordinates among the plurality of feature points; determining the peak strain as a representative value of the peak value in the time strain curve; 5. The myocardial energy calculation method according to claim 4, further comprising:

6. 6. The myocardial energy calculation method according to claim 5, wherein the load strain and the rest strain are the peak values ​​of the endocardial length calculated from the sum of the distances of the plurality of feature points and normalized by the endocardial length at the end of the diastole in the cardiac cycle.

7. A myocardial energy calculation method according to any one of claims 1 to 6, characterized in that the ME includes values ​​corresponding to each of multiple regions of the myocardium and is displayed by fitting it to a segment model in which the myocardium is divided into the multiple regions.

8. The myocardial energy calculation method according to claim 7, characterized in that the segment model is a 16-segment model including segments #1 to #16 corresponding to 16 regions of the myocardium, excluding segment #17 corresponding to the apex of the heart in a 17-segment model corresponding to 17 regions of the myocardium.

9. 1. A myocardial energy calculation system, comprising: a nuclear medicine imaging device; Information processing device Including, the nuclear medicine imaging device captures and stores tomographic images of the heart; The information processing device includes: acquiring the tomographic image from the nuclear medicine imaging device; Calculating myocardial flow reserve (MFR), which represents the ratio of myocardial blood flow under stress to resting blood flow, from the tomographic image; Calculating a peak strain of the myocardium from the tomographic image; Calculating a myocardial strain ratio (MSR) representing the ratio of myocardial stress strain to resting strain from the peak strain; calculating myocardial energy (ME) indicative of kinetic energy related to the myocardium based on the MFR and the MSR; A myocardial energy calculation system characterized in that the ME is calculated as the square root of the sum of the squares of the MFR and the MSR.

10. The myocardial energy (ME) is expressed as follows: A and B are arbitrary coefficients.

10. The myocardial energy calculation system according to claim 9, wherein the myocardial energy calculation system is expressed by the following formula:

11. the nuclear medicine imaging device is a PET device or a SPECT device, 11. The myocardial energy calculation system according to claim 9, wherein the tomographic image is a PET image taken by the PET device or a SPECT image taken by the SPECT device.

12. the tomographic image includes a plurality of cross-sectional images of the entire myocardium; 12. The myocardial energy calculation system according to claim 9, wherein each of the plurality of cross-sectional images includes a predetermined number of consecutive frames corresponding to a cardiac cycle.

13. In order to calculate the MFR and the MSR, the information processing device accepting an input for determining a plurality of feature points along the endocardium recorded in a predetermined frame image among the predetermined number of consecutive frames of images; tracking the plurality of feature points across the predetermined number of consecutive frames of images using a feature tracking technique; generating a time strain curve with the cardiac cycle and the distance between the two points as axes for each distance between two adjacent points expressed by two-dimensional coordinates among the plurality of feature points; The myocardial energy calculation system according to claim 12, further comprising determining the peak strain as a representative value of a peak value in the time strain curve.

14. The myocardial energy calculation system of claim 13, characterized in that the load strain and the rest strain are the peak values ​​of the endocardial length calculated from the sum of the distances of the multiple feature points, normalized by the endocardial length at the end of the diastole in the cardiac cycle.

15. The ME includes values ​​corresponding to each of a plurality of regions of the myocardium; 15. The myocardial energy calculation system according to claim 9, wherein the information processing device displays the ME by applying it to a segment model in which the myocardium is divided into the plurality of regions.

16. The myocardial energy calculation system of claim 15, characterized in that the segment model is a 16-segment model including segments #1 to #16 corresponding to 16 regions of the myocardium, excluding segment #17 corresponding to the apex of the heart in a 17-segment model corresponding to 17 regions of the myocardium.

17. A myocardial energy calculation device that executes each step of the myocardial energy calculation method according to any one of claims 1 to 8.

18. A myocardial energy calculation program, which, when executed by a computer, causes the computer to function as the myocardial energy calculation device according to claim 17.

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