Dynamic analysis apparatus, dynamic analysis system, program, and classification method

Abstract

To support diagnosis of a disease by using an analysis image obtained by analyzing a dynamic image.SOLUTION: A controller of an analysis apparatus generates an analysis image by analyzing a dynamic image of a chest, and classifies signal values in a lung field region in the analysis image into at least three or more clusters based on magnitudes of the signal values. Then, the control unit causes a display unit to display a cluster map in which a region of each cluster in the analysis image is color-coded and a feature amount for each region of the cluster.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a dynamic analysis device, a display device, a dynamic analysis system, a program, a classification method, and a display method. [Background technology]

[0002] Conventionally, chest CT imaging has been performed as a detailed examination for diagnosing chest diseases such as pulmonary hypertension. In imaging tests for pulmonary hypertension, for example, enlargement of the pulmonary artery trunk and attenuation of peripheral pulmonary blood vessels are confirmed. In other words, pulmonary hypertension is diagnosed by observing changes in each structure (e.g., large blood vessels, medium blood vessels, and small blood vessels) within the lung field region in chest CT images.

[0003] However, CT examinations have the problem of radiation exposure. Therefore, studies are being conducted to replace CT examinations with dynamic imaging examinations using low radiation exposure. For example, Patent Document 1 describes the generation of a blood flow analysis image in which blood flow feature quantities are used as signal values ​​and a ventilation analysis image in which ventilation feature quantities are used as signal values ​​from frame images of dynamic images obtained by dynamic imaging. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2021-132994 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the signal values ​​of the analysis image generated by the technology described in Patent Document 1 are relative signal values ​​based on the signal value of a certain frame image of a dynamic image, which makes it difficult to compare between patients or to specify a threshold value for the signal value to identify a structure region within the lung field region, making it difficult to use for diagnosing diseases.

[0006] An object of the present invention is to provide support for diagnosing diseases using an analysis image obtained by analyzing a dynamic image. [Means for solving the problem]

[0007] In order to solve the above problems, a dynamic analysis device according to the present invention includes: A dynamic analysis device that analyzes dynamic images obtained by performing dynamic radiography on a subject, a classification means for classifying signal values ​​within a target region in an analysis image generated by analyzing the dynamic image into at least three or more clusters based on the types of the signal values; Equipped with. [Effects of the Invention]

[0008] According to the present invention, it is possible to assist in diagnosing diseases using an analysis image obtained by analyzing a dynamic image. [Brief explanation of the drawings]

[0009] Advantages and features provided by one or more embodiments of the present invention will be more fully understood from the following detailed description and the accompanying drawings, which are for illustrative purposes only and are not intended to limit the scope of the invention. [Figure 1] 1 is a diagram illustrating an example of the overall configuration of a dynamic analysis system according to an embodiment of the present invention. [Figure 2] 2 is a flowchart showing an imaging control process executed by a control unit of the imaging console of FIG. 1. [Figure 3] 2 is a flowchart showing an analysis process executed by a control unit of the analysis device of FIG. [Figure 4] FIG. 1 is a diagram showing a schematic diagram of the branching of the pulmonary artery. [Figure 5A] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 5B] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 5C] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 5D] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 5E] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 5F] FIG. 10 is a diagram showing how signal values ​​on a histogram are classified into clusters. [Figure 6A] FIG. 10 is a diagram showing an example of a cluster map of a normal case and feature amounts of each cluster region. [Figure 6B] FIG. 10 is a diagram showing an example of a cluster map of a lung having an ischemic area in a peripheral blood vessel and the feature amount of each cluster region. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings, but the present invention is not limited to the illustrated examples.

[0011] Configuration of dynamic analysis system 100 FIG. 1 shows an example of the overall configuration of a dynamic analysis system 100 according to this embodiment. As shown in FIG. 1, the dynamic analysis system 100 includes an imaging device 1, an imaging console 2, and an analysis device 3 (dynamic analysis device). The imaging device 1 and the imaging console 2 are connected by a communication cable or the like. The imaging console 2 and the analysis device 3 are connected via a communication network NT such as a LAN (Local Area Network). RIS (Radiology Information Systems), HIS (Hospital Information Systems), an electronic medical record system, PACS (Picture Archiving and Communication System), etc. (not shown) may also be connected to the communication network NT. Each device constituting the dynamic analysis system 100 conforms to the DICOM (Digital Image and Communications in Medicine) standard. Communication between the devices is performed in accordance with DICOM.

[0012] <Configuration of the imaging device 1> The imaging device 1 is an imaging unit that captures periodic (cyclical) dynamics of the chest, such as changes in the shape of lung expansion and contraction due to breathing, and heartbeats. Dynamic imaging refers to obtaining multiple images of a subject by irradiating the subject with pulsed radiation, such as X-rays. Pulse irradiation refers to repeatedly irradiating the subject with pulsed radiation at predetermined time intervals. Alternatively, dynamic imaging refers to obtaining multiple images of a subject by continuously irradiating the subject with radiation. Continuous irradiation refers to continuously irradiating the subject with radiation at a low dose rate without interruption. A series of images obtained by dynamic imaging is called a dynamic image. Furthermore, each of the multiple images that make up a dynamic image is called a frame image. Here, dynamic imaging includes video imaging but does not include capturing still images while displaying a video. Dynamic images include video images but do not include images obtained by capturing still images while displaying a video. In the following embodiment, a case where dynamic imaging is performed by pulse irradiation will be described as an example.

[0013] The radiation source 11 is disposed at a position facing the radiation detection unit 13 across the subject M, which is the region of the subject to be imaged. The radiation source 11 irradiates the subject M with radiation (X-rays) under the control of the radiation irradiation control device 12. The radiation irradiation control device 12 is connected to the imaging console 2. The radiation irradiation control device 12 controls the radiation source 11 based on the radiation irradiation conditions input from the imaging console 2 to perform radiation imaging. The radiation irradiation conditions input from the imaging console 2 include, for example, the pulse rate, pulse width, pulse interval, the number of imaging frames per imaging, the value of the X-ray tube current, the value of the X-ray tube voltage, and the type of additional filter. The pulse rate is the number of radiation irradiations per second and coincides with the frame rate, which will be described later. The pulse width is the radiation irradiation time per radiation irradiation. The pulse interval is the time from the start of one radiation irradiation to the start of the next radiation irradiation and coincides with the frame interval, which will be described later.

[0014] The radiation detection unit 13 is composed of a semiconductor image sensor such as an FPD (Flat Panel Detector). The FPD has, for example, a glass substrate. A plurality of detection elements (pixels) are arranged in a matrix at predetermined positions on the glass substrate. Each pixel detects radiation that has been irradiated from the radiation source 11 and transmitted through at least the subject M according to its intensity, and converts the detected radiation into an electrical signal and stores it. Each pixel is equipped with a switching unit such as a TFT (Thin Film Transistor). FPDs can be of an indirect conversion type, in which X-rays are converted into an electrical signal by a photoelectric conversion element via a scintillator, or a direct conversion type, in which X-rays are directly converted into an electrical signal, and either type may be used. The radiation detection unit 13 is disposed opposite the radiation source 11 with the subject M interposed therebetween.

[0015] The reading control device 14 is connected to the imaging console 2. The reading control device 14 controls the switching unit of each pixel of the radiation detection unit 13 based on the image reading conditions input from the imaging console 2, and reads the electrical signals accumulated in each pixel. In this way, the reading control device 14 acquires image data. This image data is a frame image. The reading control device 14 then outputs the acquired frame image to the imaging console 2. The image reading conditions include, for example, the frame rate, frame interval, pixel size, image size (matrix size), etc. The frame rate is the number of frame images acquired per second, and is equal to the pulse rate. The frame interval is the time from the start of acquisition of one frame image to the start of acquisition of the next frame image, and is equal to the pulse interval.

[0016] The radiation irradiation control device 12 and the reading control device 14 are connected to each other and exchange synchronization signals with each other to synchronize the radiation irradiation operation and the image reading operation.

[0017] <Configuration of shooting console 2> The imaging console 2 outputs radiation irradiation conditions and image reading conditions to the imaging device 1 to control the operations of radiography and reading of radiographic images by the imaging device 1. The imaging console 2 also displays dynamic images acquired by the imaging device 1 so that the imaging technician or other person performing the imaging can check the positioning and whether the images are suitable for diagnosis. 1, the radiography console 2 is configured to include a control unit 21, a storage unit 22, an operation unit 23, a display unit 24, and a communication unit 25. The respective units of the radiography console 2 are connected by a bus 26.

[0018] The control unit 21 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc. In response to operations performed by the operation unit 23, the CPU of the control unit 21 reads out a system program and various processing programs stored in the storage unit 22, loads them into the RAM, and executes various processes, including an imaging control process (to be described later), in accordance with the loaded programs. In this way, the control unit 21 centrally controls the operations of each unit of the imaging console 2 and the radiation irradiation and reading operations of the imaging device 1.

[0019] The storage unit 22 is configured with a non-volatile semiconductor memory, a hard disk, etc. The storage unit 22 stores various programs executed by the control unit 21, parameters required for executing processing by the programs, data such as processing results, etc. For example, the storage unit 22 stores a program for executing the imaging control processing shown in FIG. 2. The storage unit 22 also stores radiation irradiation conditions and image reading conditions corresponding to the imaging region and imaging direction. The various programs are stored in the storage unit 22 in the form of readable program codes. The control unit 21 sequentially executes operations in accordance with the program codes.

[0020] The operation unit 23 includes a keyboard having cursor keys, numeric input keys, various function keys, etc., and a pointing device such as a mouse. The operation unit 23 outputs instruction signals input by operating the keys on the keyboard or the mouse to the control unit 21. The operation unit 23 may include a touch screen on the display screen of the display unit 24. In this case, the operation unit 23 outputs instruction signals input via the touch screen to the control unit 21.

[0021] The display unit 24 is configured with a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube), etc. The display unit 24 displays input instructions, data, etc. from the operation unit 23 in accordance with instructions of a display signal input from the control unit 21.

[0022] The communication unit 25 includes a LAN adapter, a modem, a TA (Terminal Adapter), etc. The communication unit 25 controls data transmission and reception between the radiography console 2 and each device connected to the communication network NT.

[0023] <Configuration of analysis device 3> The analysis device 3 acquires dynamic images from the imaging console 2, analyzes the acquired dynamic images, and displays the analysis results. The analysis device 3 corresponds to the dynamic analysis device and display device of the present invention. In this embodiment, the analysis device 3 generates a blood flow analysis image based on the dynamic images of the chest, and classifies the lung field region of the blood flow analysis image into a large blood vessel region, a medium blood vessel region, a small blood vessel region, and a no-blood-flow region for display. As shown in FIG. 1, the analysis device 3 comprises a control unit 31, a storage unit 32, an operation unit 33, a display unit , and a communication unit , and each unit is connected by a bus .

[0024] The control unit 31 is composed of a CPU, RAM, etc. In response to operations performed by the operation unit 33, the CPU of the control unit 31 reads out the system program and various processing programs stored in the storage unit 32, loads them into the RAM, and performs centralized control of the operations of the various units of the analysis device 3 according to the loaded programs. The CPU of the control unit 31 also executes various processes including the analysis process described below in cooperation with the programs stored in the storage unit 32. The control unit 31 functions as a classification means, a calculation means, a display control means, and an estimation means.

[0025] The storage unit 32 is configured with a non-volatile semiconductor memory, a hard disk, or the like. The storage unit 32 stores various programs, parameters required for executing processing by the programs, data such as processing results, etc. The programs stored in the storage unit 32 include programs for the control unit 31 to execute analysis processing. These various programs are stored in the storage unit 32 in the form of readable program code. The control unit 31 sequentially executes operations in accordance with the program code. The storage unit 32 also stores the dynamic image received from the imaging console 2 in association with the accompanying information.

[0026] The operation unit 33 is configured to include a keyboard equipped with cursor keys, numeric input keys, various function keys, etc., and a pointing device such as a mouse. The operation unit 33 outputs instruction signals input by operating the keyboard or the mouse to the control unit 31. The operation unit 33 may also include a touch screen on the display screen of the display unit 34. In this case, the operation unit 33 outputs instruction signals input via the touch screen to the control unit 31.

[0027] The display unit 34 is configured by a monitor such as an LCD or a CRT. The display unit 34 performs various displays according to instructions of a display signal input from the control unit 31. The display unit 34 functions as a presentation means.

[0028] The communication unit 35 includes a LAN adapter, a modem, a TA, etc. The communication unit 35 controls data transmission and reception between the analysis device 3 and each device connected to the communication network NT.

[0029] <Operation of Dynamic Analysis System 100> Next, the operation of the dynamic analysis system 100 will be described.

[0030] (Operation of imaging device 1 and imaging console 2) First, the imaging operation performed by the imaging device 1 and the imaging console 2 will be described. 2 shows an imaging control process executed in the control unit 21 of the imaging console 2. The imaging control process is executed by the control unit 21 in cooperation with a program stored in the storage unit 22.

[0031] First, the control unit 21 accepts input of patient information and examination information by the imaging implementer operating the operation unit 23 (step S1). The patient information is information about the subject. The patient information includes information such as the patient ID, name, age, sex, height, and weight. The examination information includes the examination ID, examination date, imaging region, imaging direction, etc. In this embodiment, the imaging region is the chest. The patient information and examination information may be acquired via the communication unit 25 from a RIS or HIS (not shown).

[0032] Next, based on the input patient information and examination information, the control unit 21 reads out radiation irradiation conditions from the storage unit 22 and sets them in the radiation irradiation control device 12. The control unit 21 also reads out image reading conditions from the storage unit 22 and sets them in the reading control device 14 (step S2).

[0033] Next, the control unit 21 waits for an instruction to irradiate radiation (step S3). Here, the person performing the imaging performs positioning by placing the subject M between the radiation source 11 and the radiation detection unit 13, and when the imaging preparations are complete, operates the operation unit 23 to input an instruction to irradiate radiation.

[0034] When a radiation irradiation instruction is input via the operation unit 23 (step S3; YES), the control unit 21 outputs an imaging start instruction to the radiation irradiation control device 12 and the reading control device 14, and starts dynamic imaging (step S4). That is, the control unit 21 causes the radiation source 11 to irradiate radiation at pulse intervals set in the radiation irradiation control device 12, and causes the radiation detection unit 13 to acquire frame images. Before or during dynamic imaging, the imaging implementer instructs the subject on their breathing state. In this embodiment, the subject is instructed to hold their breath in order to suppress movement due to respiratory movement. The imaging device 1 may output a voice and / or a display instructing the subject on their breathing state.

[0035] When an instruction to end radiation irradiation is input by the operation unit 23, the control unit 21 outputs an instruction to end imaging to the radiation irradiation control device 12 and the reading control device 14, and stops the imaging operation.

[0036] The frame images of dynamic images acquired by imaging are sequentially input to the imaging console 2. The control unit 21 associates the input frame images with numbers (frame numbers) indicating the imaging order and stores them in the storage unit 22 (step S5). The control unit 21 also displays the input frame images on the display unit 24 (step S6). The radiographer checks the positioning and the like using the displayed dynamic image and determines whether an image suitable for diagnosis has been acquired through radiography (radiography OK) or whether re-radiography is necessary (radiography NG).The radiographer then operates the operation unit 23 to input the result of the determination.

[0037] When a determination result indicating that imaging is OK is input by a predetermined operation of operation unit 23 (step S7; YES), control unit 21 attaches, to each of the series of frame images acquired by dynamic imaging, an identification ID for identifying the dynamic image, patient information, examination information, radiation irradiation conditions, image reading conditions, a number indicating the imaging order (frame number), etc. as incidental information. Control unit 21 transmits the series of frame images with the incidental information attached to them to analysis device 3 via communication unit 25 (step S8). Then, control unit 21 ends the imaging control process. On the other hand, when a determination result indicating that photography is NG is input by a predetermined operation of the operation unit 23 (step S7; NO), the control unit 21 deletes the series of frame images stored in the storage unit 22 (step S9). Then, the control unit 21 ends the photography control process. In this case, photography needs to be retaken.

[0038] (Operation of analysis device 3) Next, the operation of the analysis device 3 will be described. In the analysis device 3, for example, when a dynamic image is received from the radiography console 2 via the communication unit 35, the control unit 31 associates the received dynamic image with additional information and stores the associated information in the storage unit 32. When an instruction is given by operating the operation unit 33 to perform an analysis process on the dynamic image of the chest stored in the storage unit 32, the control unit 31 executes the analysis process shown in Fig. 3. The analysis process is executed by cooperation between the control unit 31 and a program stored in the storage unit 32. The analysis process will be described below with reference to Fig. 3.

[0039] First, the control unit 31 analyzes the dynamic image to generate a blood flow analysis image (step S11). The blood flow analysis image can be generated, for example, by the following processes (1) to (4).

[0040] (1) First, the control unit 31 extracts a lung field region from each frame image of the dynamic image. Any known method may be used to extract the lung field region. For example, the control unit 31 determines a threshold value by discriminant analysis from a histogram of the pixel values ​​(signal values) of each pixel in the frame image, and initially extracts regions with signals higher than this threshold as lung field region candidates. Next, edge detection is performed near the boundary of the initially extracted lung field region candidate, and the boundary of the lung field region can be extracted by extracting points along the boundary where the edge is maximum in small blocks near the boundary.

[0041] (2) Next, the control unit 31 determines a reference frame image. The reference frame image can be, for example, a frame image corresponding to the end of ventricular diastole of the heart. Here, when blood flows into an organ, the blood flow prevents radiation from passing through. As a result, the blood flow reduces the amount of radiation passing through in the radiographic image, reducing pixel values ​​(signal values), and the radiographic image appears whitish. When this is viewed in relation to the cardiac blood flow flowing in and out of the heart and the pulmonary blood flow flowing in and out of the lung fields, when the heart is in ventricular diastole and a large amount of blood is flowing into the heart, radiation is prevented from passing through the heart, resulting in a small signal value and a relatively whitish appearance on the radiographic image. In contrast, at this timing, there is little blood flowing into the lung fields, and a large amount of radiation passes through the lung fields, resulting in a large signal value and a relatively dark appearance on the radiographic image. In other words, the frame image corresponding to the end of ventricular diastole of the heart is a frame image that shows the state in which pulmonary blood flow is at its lowest. Therefore, for example, the control unit 31 sets an ROI in the ventricular region of the heart for each frame image of the dynamic image, and determines the frame image in which the signal value (e.g., average value) of the ROI is the smallest as the frame image (reference frame image) corresponding to the end of ventricular diastole of the heart.

[0042] (3) Next, the control unit 31 calculates, for each pixel in the lung field region of each frame image, the ratio (D' / D) between the difference value D' in the signal value from the corresponding pixel in the reference frame image and the signal value D of the corresponding pixel in the reference frame image. Corresponding pixels in the reference frame image are pixels that are located at the same position in the reference frame image. The ratio (D' / D) is a value that represents the rate of change in the signal value of the pixel in each frame image relative to the signal value of the corresponding pixel in the reference frame image. This D' / D is a feature that indicates the blood flow rate in the lungs.

[0043] (4) Next, the control unit 31 generates a blood flow analysis image by integrating representative values ​​(maximum absolute values) of a plurality of ratios (D' / D) calculated for pixels at the same position in each frame image into one image.

[0044] The control unit 31 may perform binning, which divides the lung field region of each frame image into small blocks consisting of multiple pixels and replaces each signal value in the small blocks with a representative value (such as an average value).The control unit 31 may then treat each small block as one pixel and perform the processes (3) and (4) above.For example, when dynamic imaging is performed under quiet breathing, the control unit 31 may acquire a waveform of the time change in signal value for each pixel in the lung field region of the dynamic image, apply a high-pass filter or band-pass filter in the time direction to extract only the signal component of the cardiac cycle, and then perform the processes (3) and (4) above.

[0045] Next, the control unit 31 generates a histogram (probability distribution) of the signal values ​​of the blood flow analysis image (step S12). Specifically, the control unit 31 generates a histogram in which the horizontal axis represents the signal value in the lung field region of the blood flow analysis image and the vertical axis represents the number of pixels having that signal value.

[0046] Next, the control unit 31 normalizes the generated histogram (step S13). For example, the control unit 31 normalizes the range of the horizontal axis (range from minimum value to maximum value) which differs depending on the patient.

[0047] Next, the control unit 31 clusters the signal values ​​from the generated histogram using a statistical method (step S14). For example, the control unit 31 classifies the signal values ​​in the lung field region into three or more clusters based on their type (here, size) using the k-means method.

[0048] Here, the blood flow in the pulmonary artery is the flow of blood based on the pulsation of the heart, and the blood vessels of the pulmonary artery are deformed by the blood flow caused by the pulsation. D in the signal value (D' / D) of each pixel in the blood flow analysis image corresponds to the blood vessel diameter before deformation due to blood flow. D' in the signal value (D' / D) of each pixel in the blood flow analysis image corresponds to the maximum blood vessel diameter deformed by blood flow. If K is the elastic modulus of the blood vessel and P is the internal pressure of the pulmonary artery, then D' / D = K × P. As shown in Figure 4, the pulmonary artery branches in a geometric progression and narrows, and the magnitude of the value of D' / D is thought to depend on the number of branches. For example, if the signal value (D' / D) of the main pulmonary artery in the blood flow analysis image is S, in a blood vessel with normal blood flow, the signal value of a blood vessel that branches once from the main pulmonary artery into two branches is S / 2, and the signal value of a blood vessel that branches twice into four branches is S / 4, etc. Therefore, in this embodiment, the lung field region of the blood flow analysis image is classified into four clusters based on the magnitude of the signal value. As a result, the lung field region is classified into a region of signal values ​​corresponding to large blood vessels, a region of signal values ​​corresponding to medium blood vessels, a region of signal values ​​corresponding to small blood vessels, and a region of signal values ​​corresponding to no blood flow. Large blood vessels include, for example, the pulmonary artery trunk and the left and right pulmonary arteries branching from it. Small blood vessels include, for example, capillaries and peripheral blood vessels. Medium blood vessels include, for example, blood vessels between large and small blood vessels.

[0049] In step S14, the control unit 31 clusters the signal values ​​of the blood flow analysis image by performing the following processes (I) to (IV). Figures 5A to 5F are diagrams showing how signal values ​​on a histogram are classified into clusters. Figures 5A to 5F show an example in which signal values ​​on a histogram are classified into three clusters, clusters A to C. (I) First, the control unit 31 randomly assigns a cluster to each point on the histogram (see FIG. 5A). (II) Next, the control unit 31 calculates, for each cluster, the average position (cluster center) of the points assigned to that cluster (see the star in FIG. 5A). (III) Next, the control unit 31 calculates the distance from the center of each cluster for each point, and reallocates the point to the cluster that is closest in distance (see FIG. 5B). (IV) The above steps (II) and (III) are repeated until the assigned clusters do not change (see Figures 5C to 5F).

[0050] In the above methods (I) to (IV), clusters are randomly assigned in (I), so the results may depend on the initial values. Therefore, the cluster centers may be found by the following processes (a) to (d) (k-means++ method). This reduces the dependency on the initial values. (a) First, the control unit 31 calculates the value of each point x on the histogram. i Randomly select one point from the cluster and use it as the center of the cluster. (b) Next, the control unit 31 calculates the value of each point x on the histogram. i Calculate the distance D(x) from the cluster center. (c) Weighted probability distribution (D(x)) for each point 2 / ΣD(x) 2 ) to select new cluster centers. (d) The above steps (b) and (c) are repeated until k cluster centers (here, k=4) are selected.

[0051] As described above, the signal value of each pixel in the blood flow analysis image is a relative signal value based on the signal value of the reference frame image. Therefore, it is not possible to compare the magnitude of this signal value between patients. Furthermore, even if a predetermined percentile value of the signal values ​​within the target region is set as a threshold to divide the target region (lung region) of the blood flow analysis image into three or more structure regions based on signal values, the threshold is not an absolute standard, and therefore it is not possible to identify each structure using a unified standard. On the other hand, in this embodiment, a probability distribution (histogram) of the signal values ​​within the target region is calculated based on the type (magnitude) of the signal values ​​within the target region, and the target region is classified into four clusters based on the probability distribution using cluster analysis, a statistical method. Therefore, the target region of the blood flow analysis image, which is composed of relative signal values, can be classified into large blood vessel regions, medium blood vessel regions, small blood vessel regions, and no blood flow regions without varying standards between patients.

[0052] Next, the control unit 31 labels each pixel in the lung field region of the blood flow analysis image based on the clustering result (step S15). Next, the control unit 31 calculates the area of ​​each cluster region and the ratio of each cluster region to the total area of ​​the lung field (step S16). In this embodiment, the control unit 31 calculates the number of pixels classified into each cluster as the area of ​​each cluster. Next, the control unit 31 generates a cluster map by color-coding the lung field region of the blood flow analysis image for each cluster (step S17). In step S17, the lung field regions of the blood flow analysis image may be displayed in a different display mode for each cluster so that they can be distinguished, and the display mode is not limited to color coding.

[0053] Next, the control unit 31 causes the display unit 34 to display the cluster map and the feature amounts of each cluster region (step S18). Then, the control unit 31 ends the analysis process. Examples of the feature amounts of each cluster region include the signal value at the center of the cluster, its area, and its proportion to the entire lung field region.

[0054] 6A and 6B are diagrams showing an example of a cluster map 341 and feature values ​​342 of each cluster region displayed on the display unit 34 in step S18. Cluster 0 is a region of signal values ​​corresponding to no blood flow (no blood flow region). Cluster 1 is a region of signal values ​​corresponding to small blood vessels (small blood vessel region). Cluster 2 is a region of signal values ​​corresponding to medium blood vessels (medium blood vessel region). Cluster 3 is a region of signal values ​​corresponding to large blood vessels (large blood vessel region). FIG. 6A shows the cluster map 341 and feature values ​​342 of each cluster region of a normal subject. FIG. 6B shows the cluster map 341 and feature values ​​342 of each cluster region of a subject with an ischemic site in a peripheral blood vessel. In FIGS. 6A and 6B, an example is shown in which regions outside the lung region in the cluster map 341 are displayed in black. The cluster map 341 may be displayed superimposed on one frame image of the dynamic image.

[0055] From the cluster map 341 and the feature values ​​342 of each cluster region displayed on the display unit 34, a physician can obtain information on the shape, area, and overall proportion of each of the large blood vessel regions, medium blood vessel regions, small blood vessel regions, and no-blood-flow regions in the lung field region. For example, in FIG. 6B, a no-blood-flow region is observed in an area where small blood vessels would normally be present, indicating an ischemic area in the peripheral blood vessels. From the state of such a lung field region, a physician can diagnose whether or not a disease such as pulmonary hypertension or pulmonary embolism is present. The control unit 31 may calculate feature values ​​for each blood vessel region (each cluster region), such as by multiplying the signal value in the lung field region of the blood flow analysis image by the area of ​​the cluster region to calculate the flow velocity, and display the calculated feature values ​​on the display unit 34 together with the cluster map 341, etc.

[0056] Furthermore, the control unit 31 may estimate the presence or absence of a disease and / or the type of disease based on the region pattern of each classified cluster. The type of disease is the name of the disease or the name of the disease and its classification (for example, groups 1 to 5 of pulmonary hypertension). For example, a machine learning model for estimating the type of disease from the clustering results is created in advance based on a large amount of training data that associates the clustering results of signal values ​​in the lung field region of the blood flow analysis image with the type of disease diagnosed by a doctor, and the model is stored in the storage unit 32. The control unit 31 uses this machine learning model to estimate the presence or absence of a disease and / or the type of disease from the clustering results of step S14. Then, the estimation result is displayed on the display unit 34. This can help make the doctor's diagnosis even easier. Note that the presence or absence of a disease and / or the type of disease may be estimated from the clustering results of step S14 using AI (artificial intelligence) without training data.

[0057] (Variation) In the above embodiment, the case where clustering is performed using a statistical method (k-means method) in step S14 has been described. Instead, clustering may be performed using AI such as a machine learning model. For example, based on image data of a large number of blood flow analysis images in which lung fields are classified by a doctor into large blood vessel regions, medium blood vessel regions, small blood vessel regions, and no blood flow regions, a machine learning model that clusters signal values ​​of the lung field regions of the blood flow analysis images is created and stored in the storage unit 32. Alternatively, based on image data of other modalities (CT, MRI, RI) in which lung fields are classified into large blood vessel regions, medium blood vessel regions, small blood vessel regions, and no blood flow regions, a machine learning model that clusters signal values ​​of the lung field regions of the blood flow analysis images may be created and stored in the storage unit 32. Then, in step S14, the control unit 31 may cluster the signal values ​​of the lung field regions of the blood flow analysis images into signal values ​​corresponding to large blood vessels, signal values ​​corresponding to medium blood vessels, signal values ​​corresponding to small blood vessels, and signal values ​​corresponding to no blood flow using the machine learning model stored in the storage unit 32.

[0058] As described above, the control unit 31 of the analysis device 3 classifies the signal values ​​within the lung field region in the blood flow analysis image generated by analyzing the dynamic image of the chest into at least three or more clusters based on the magnitude of the signal values. Therefore, the signal values ​​in the lung field of the blood flow analysis image can be classified into three or more clusters based on their size, and the lung field can be classified into three or more structures. As a result, doctors can be supported in diagnosing diseases using blood flow analysis images.

[0059] The control unit 31 also calculates at least one of the area or ratio of the region for each classified cluster and presents the calculation results, thereby providing a feature value for each region of each cluster that is useful for doctors to diagnose diseases.

[0060] Furthermore, the control unit 31 colors the lung field regions of the blood flow analysis image according to the classified clusters and displays them on the display unit 34. This allows the doctor to intuitively recognize the distribution and size of the regions for each cluster and make a diagnosis.

[0061] Furthermore, the control unit 31 classifies the signal values ​​in the lung field region of the blood flow analysis image based on a probability distribution, so that even relative signal values ​​can be classified with high accuracy.

[0062] Furthermore, the control unit 31 classifies the signal values ​​in the lung field region of the blood flow analysis image into signal values ​​corresponding to large blood vessels, signal values ​​corresponding to medium blood vessels, signal values ​​corresponding to small blood vessels, and signal values ​​corresponding to no blood flow, thereby supporting doctors in diagnosing diseases using the blood flow analysis image.

[0063] Furthermore, the control unit 31 classifies the signal values ​​in the lung field region of the blood flow analysis image into four or more clusters, thereby assisting doctors in diagnosing diseases using the blood flow analysis image.

[0064] Furthermore, the control unit 31 estimates the presence or absence of a disease or the type of disease based on the pattern of the classified cluster, thereby making it easier for doctors to diagnose diseases.

[0065] The contents of the above embodiment are preferred examples of the dynamic analysis device, display device, dynamic analysis system, program, classification method and display method according to the present invention, and are not limited to these. For example, in the above embodiment and its modified examples, the signal values ​​of the lung field region of the blood flow analysis image are clustered into four groups, but the analysis image to be processed may be another analysis image, such as a ventilation analysis image. As the ventilation analysis image, for example, an image obtained by subtracting the signal value of the corresponding pixel in a frame image at the maximum expiration position from the signal value of a frame image at the maximum inspiration position in a dynamic image captured of the chest during deep breathing or quiet breathing can be applied.

[0066] In the above embodiment, the lung field area in the analysis image of the chest is divided into four areas, but the imaging site, the area to be divided, and the number of divisions are merely examples and can be changed as appropriate.

[0067] In addition, in the above embodiment, the control unit 31 calculates and displays both the area of ​​each classified cluster and its ratio to the entire target area, but it may also calculate only one of them and display it on the display unit 34.

[0068] Furthermore, for example, in the above description, examples have been disclosed in which a hard disk or a semiconductor nonvolatile memory is used as a computer-readable medium for the program according to the present invention, but the present invention is not limited to this example. Portable recording media such as CD-ROMs can also be used as other computer-readable media. Furthermore, carrier waves can also be used as a medium for providing data for the program according to the present invention via a communication line.

[0069] While embodiments of the present invention have been described and illustrated in detail, the disclosed embodiments are made for purposes of illustration and example only and are not intended to limit the invention, the scope of which is to be construed by the appended claims. [Explanation of symbols]

[0070] 100 Dynamic Analysis System 1. Imaging device 11 Radiation source 12 Radiation exposure control device 13 Radiation detection unit 14 Reading control device 2. Filming console 21 Control Unit 22 Memory section 23 Control section 24 Display section 25 Communications Department 26 Bus 3 Analysis device 31 Control Unit 32 Storage section 33 Operation section 34 Display section 35 Communications Department 36 Bus

Claims

1. A dynamic analysis device that analyzes dynamic images obtained by performing dynamic radiography on a subject, a classification means for classifying signal values ​​within a target region in an analysis image generated by analyzing the dynamic image into at least three or more clusters based on the types of the signal values; A dynamic analysis device comprising:

2. The dynamic image is a dynamic image of the chest. The dynamic analysis device according to claim 1 .

3. a calculation means for calculating at least one of the area or ratio of each cluster classified by the classification means; a presentation means for presenting a calculation result by the calculation means; The dynamic analysis device according to claim 1 , comprising:

4. a display control means for displaying the target region of the analysis image on a display unit in a color-coded manner for each of the classified clusters; The dynamic analysis device according to claim 1 , comprising:

5. the classification means classifies signal values ​​within the region of interest based on a probability distribution; The dynamic analysis device according to claim 1 .

6. the classification means classifies the signal values ​​in the target region into signal values ​​corresponding to large blood vessels, signal values ​​corresponding to medium blood vessels, signal values ​​corresponding to small blood vessels, and signal values ​​corresponding to no blood flow; The dynamic analysis device according to claim 1 .

7. the classification means classifies signal values ​​within the region of interest into four or more clusters; The dynamic analysis device according to claim 1 .

8. a prediction means for predicting the presence or absence or type of a disease based on the pattern of the classified cluster; The dynamic analysis device according to claim 1 , comprising:

9. A display unit; a control unit that classifies signal values ​​within a target region in an analysis image generated by analyzing a dynamic image obtained by performing dynamic radiography on a subject, based on the types of the signal values, into at least three or more clusters, and displays the target region of the analysis image on the display unit in a display mode that differs for each of the classified clusters; A display device comprising:

10. an imaging device for performing dynamic radiography on a subject; A dynamic analysis device according to any one of claims 1 to 8, A dynamic analysis system comprising:

11. Computer, a classification means for classifying signal values ​​within a target region in an analysis image generated by analyzing a dynamic image obtained by performing dynamic radiography on a subject using radiation into at least three or more clusters based on the types of the signal values; A program to function as a

12. Computer, a control unit that classifies signal values ​​within a target region in an analysis image generated by analyzing a dynamic image obtained by performing dynamic radiography on a subject using radiation, into at least three or more clusters based on the types of the signal values, and displays the target region of the analysis image on a display unit in a display mode that differs for each of the classified clusters; A program to function as a

13. The computer classifying signal values ​​within a target region in an analysis image generated by analyzing a dynamic image obtained by performing dynamic radiography on a subject, into at least three or more clusters based on the types of the signal values; Classification method.

14. The computer classifying signal values ​​within a target region in an analysis image generated by analyzing a dynamic image obtained by performing dynamic radiography on a subject into at least three or more clusters based on the types of the signal values, and displaying the target region of the analysis image on a display unit in a display mode that differs for each of the classified clusters; Display method.

Images