Dynamic image analysis device and program

Abstract

To enable the acquisition of information regarding pleural adhesions easily and accurately with low radiation exposure. [Solution] According to the control unit of the diagnostic console, a dynamic image of the chest obtained by dynamic radiography is acquired, and information regarding pleural adhesion is generated based on the amount of movement of the region in the lung area that includes at least the region adjacent to the rib cage in the acquired dynamic image, and the generated information regarding pleural adhesion is displayed and output by the display unit.

Description

Technical Field

[0001] The present invention relates to a moving image analysis apparatus and a program.

Background Art

[0002] As a technique for evaluating the presence or absence of adhesions between living tissues and the adhesion sites before surgery or the like, for example, Patent Document 1 describes a technique for collecting static images of two phases, inspiration and expiration, by a CT apparatus, and evaluating pleural adhesions using the collected static images of the two phases. Further, for example, Patent Document 2 describes calculating a slipperiness by obtaining a three-dimensional motion vector for each pair of voxels inside and outside the physically adjacent lung surfaces in three-dimensional CT image data (4D data) collected over time, and extracting a moving part and a non-moving part in a region on the contour line of the lung region based on the slipperiness. Further, for example, Patent Document 3 describes obtaining a motion vector of two structures that are physically and image-positionally close in an ultrasonic image and calculating the degree of adhesion between the two structures.

[0003] However, CT apparatuses and 4D-CTs are difficult to introduce into general medical facilities from the viewpoint of the cost of the apparatuses, and are also difficult to apply to general preoperative patients from the viewpoints of the complexity of the imaging procedure and the radiation dose. Further, an ultrasonic diagnostic apparatus cannot overview the whole subject due to local imaging, and the imaging time becomes extremely long if an attempt is made to image the whole. Also, the imaging technique is difficult. Therefore, similarly, there is a problem that it is difficult to apply to general preoperative patients.

[0004] As means for solving these problems, for example, as described in Patent Document 4, a technique for detecting adhesions based on a change in the shape of the diaphragm in a moving image, and as described in Patent Document 5, a technique for detecting adhesions from the inconsistency between the phase related to diaphragm displacement and the respiratory phase are known.

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] Japanese Patent Publication No. 2016-67832 [Patent Document 2] Japanese Patent Publication No. 2019-180899 [Patent Document 3] Japanese Patent Publication No. 2019-88565 [Patent Document 4] International Publication No. 2014 / 185197 [Patent Document 5] Japanese Patent Publication No. 2015-136566 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] However, the techniques described in Patent Documents 4 and 5 could not detect adhesions that did not manifest as changes in the shape or displacement of the diaphragm.

[0007] This invention has been made in view of the above-mentioned problems, and aims to enable the acquisition of information regarding pleural adhesions in a simple and accurate manner with a low dose of radiation. [Means for solving the problem]

[0008] To solve the above problems, the motion image analysis device according to the first aspect of the present invention is An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesions based on the amount of movement of a region within the lung area in the dynamic image that includes at least a region adjacent to the thoracic cage, An output unit that outputs information regarding the adhesion of the pleura that has been generated, It is equipped with.

[0009] Furthermore, the dynamic image analysis device according to the second aspect of the present invention is An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesion based on the difference or ratio between the amount of movement of a first region within the lung region in the dynamic image and the amount of movement of a second region different from the first region, An output unit that outputs information regarding the adhesion of the pleura that has been generated, It is equipped with.

[0010] Furthermore, the dynamic image analysis device according to the third aspect of the present invention is An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesions based on the amount of movement in the region of the lung area in the dynamic image that does not include the region adjacent to the thoracic cage, An output unit that outputs information regarding the adhesion of the pleura that has been generated, It is equipped with.

[0011] Furthermore, the program relating to the fourth aspect of the present invention is Computers Acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit generates information regarding pleural adhesions based on the amount of movement in the lung region, including at least the region adjacent to the thoracic cage, in the aforementioned dynamic image. An output unit that outputs information regarding the adhesion of the pleura that has been generated. To make it function as such.

[0012] Furthermore, the program relating to the fifth aspect of the present invention is Computers Acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generating unit that generates information regarding pleural adhesions based on the difference or ratio between the amount of movement of a first region within the lung area in the dynamic image and the amount of movement of a second region different from the first region. An output unit that outputs information regarding the adhesion of the pleura that has been generated. To make it function as such.

[0013] Also, the program according to the fifth aspect of the present invention causes a computer to function as an acquisition unit that acquires a dynamic image of the chest obtained by dynamic imaging using radiation, a generation unit that generates information regarding pleural adhesion based on the amount of movement of a region that does not include a region adjacent to the rib cage within the lung region in the dynamic image, and an output unit that outputs the generated information regarding pleural adhesion.

Advantages of the Invention

[0014] According to the present invention, it becomes possible to simply and accurately acquire information regarding pleural adhesion with a small amount of exposure.

Brief Description of the Drawings

[0015] [Figure 1] It is a diagram showing the overall configuration of the dynamic analysis system in an embodiment of the present invention. [Figure 2] It is a flowchart showing the imaging control process executed by the control unit of the imaging console in FIG. 1. [Figure 3] It is a flowchart showing the dynamic analysis process A executed by the control unit of the diagnostic console in FIG. 1 in the first embodiment. [Figure 4] It is a diagram for explaining preprocessing. [Figure 5] It is a diagram for explaining the merging of motion vectors. [Figure 6] It is a diagram showing an example of a shadow continuous in a small region. [Figure 7] It is a flowchart showing an example of the adhesion information generation process A (high sensitivity) executed in step S16 of FIG. 3. [Figure 8] It is a flowchart showing an example of the adhesion information generation process A (high specificity) executed in step S16 of FIG. 3. [Figure 9] It is a diagram showing an example of the output of information regarding pleural adhesion in the first embodiment. [Figure 10]This figure shows an example of the output of information regarding pleural adhesions in the first embodiment. [Figure 11] This figure shows an example of the output of information regarding pleural adhesions in the first embodiment. [Figure 12] This figure shows an example of the output of information regarding pleural adhesions in the first embodiment. [Figure 13] This figure shows an example of the output of information regarding pleural adhesions in the first embodiment. [Figure 14] This figure shows an example of the output of information regarding pleural adhesions in the first embodiment. [Figure 15] In the second embodiment, this is a flowchart showing the dynamic analysis process B performed by the control unit of the diagnostic console shown in Figure 1. [Figure 16] (a) is a graph plotting the amount of vertical movement (typical value) of each block when there is no adhesion, and (b) is a graph plotting the amount of vertical movement (typical value) of each block when there is adhesion. [Figure 17] This figure shows an example of the output of information regarding pleural adhesions in the second embodiment. [Figure 18] This figure shows the amount of movement for each sub-region. [Figure 19] This figure shows an example of the output of information regarding pleural adhesions in the second embodiment. [Figure 20] This figure shows an example of the output of information regarding pleural adhesions in the second embodiment. [Figure 21] In the third embodiment, this is a flowchart showing the dynamic analysis process C performed by the control unit of the diagnostic console shown in Figure 1. [Figure 22] In the fourth embodiment, this is a flowchart showing the dynamic analysis process D performed by the control unit of the diagnostic console shown in Figure 1. [Modes for carrying out the invention]

[0016] Embodiments of the present invention will be described below with reference to the drawings. However, the scope of the invention is not limited to the illustrated examples.

[0017] <First Embodiment> [Configuration of Dynamic Analysis System 100] First, the configuration of this embodiment will be described. Figure 1 shows an example of the overall configuration of the dynamic analysis system 100 in this embodiment. As shown in Figure 1, the dynamic analysis system 100 is configured such that the imaging device 1 and the imaging console 2 are connected by a communication cable, and the imaging console 2 and the diagnostic console 3 are connected via a communication network NT such as a LAN (Local Area Network). Each device constituting the dynamic analysis system 100 conforms to the DICOM (Digital Image and Communications in Medicine) standard, and communication between each device is performed in accordance with DICOM.

[0018] [Configuration of imaging device 1] The imaging device 1 is an imaging means for capturing periodic (cycle-like) movements of the chest, such as the morphological changes of lung expansion and contraction associated with respiratory movement, and the beating of the heart. Dynamic imaging involves repeatedly irradiating the subject with pulsed radiation, such as X-rays, at predetermined time intervals (pulse By irradiating (or by irradiating continuously without interruption at a low dose rate), the subject can be identified. This refers to acquiring multiple images that show the movement of the body. A series of images obtained through motion photography is called a motion image. A motion image is, in other words, a three-dimensional image that includes the time axis. Motion images include moving images, but do not include images obtained by taking still images while displaying moving images. Furthermore, each of the multiple images that make up a motion image is called a frame image. The following embodiment describes a case in which dynamic imaging of the front of the chest is performed by pulsed irradiation.

[0019] The radiation source 11 is positioned opposite the radiation detection unit 13 with the subject M in between, and irradiates the subject M with radiation (X-rays) according to the control of the radiation irradiation control device 12. The radiation irradiation control device 12 is connected to the imaging console 2 and controls the radiation source 11 to perform radiography based on the radiation irradiation conditions input from the imaging console 2. The radiation irradiation conditions input from the imaging console 2 include, for example, the pulse rate, pulse width, pulse interval, number of imaging frames per scan, X-ray tube current value, X-ray tube voltage value, and additional filter type. The pulse rate is the number of radiation irradiations per second and is the same as 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 is the same as the frame interval, which will be described later.

[0020] 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, and multiple detection elements (pixels) are arranged in a matrix at predetermined positions on the substrate. These elements detect radiation irradiated from the radiation source 11 that has passed through at least the subject M according to its intensity, and convert the detected radiation into an electrical signal for storage. Each pixel is composed of a switching unit such as a TFT (Thin Film Transistor). FPDs can be of the indirect conversion type, which converts X-rays into electrical signals via a scintillator using a photoelectric conversion element, or the direct conversion type, which directly converts X-rays into electrical signals. Either type may be used. The radiation detection unit 13 is positioned to face the radiation source 11 with the subject M in between.

[0021] The reading control device 14 is connected to the imaging console 2. Based on the image reading conditions input from the imaging console 2, the reading control device 14 controls the switching unit of each pixel of the radiation detection unit 13, switching the reading of the electrical signals accumulated in each pixel, and acquires image data by reading the electrical signals accumulated in the radiation detection unit 13. 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 are, 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 the same as the pulse rate. The frame interval is the time from the start of the acquisition operation of one frame image to the start of the acquisition operation of the next frame image and is the same as the pulse interval.

[0022] Here, the radiation irradiation control device 12 and the reading control device 14 are connected to each other and exchange synchronization signals to synchronize the radiation irradiation operation and the image reading operation.

[0023] [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 radiography and radiographic image reading operations of the imaging device 1, and also displays the dynamic images acquired by the imaging device 1 for the imaging technician or other person performing the imaging to confirm positioning and whether the images are suitable for diagnosis. As shown in Figure 1, the shooting 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, and each unit is connected by a bus 26.

[0024] The control unit 21 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc. The CPU of the control unit 21 reads system programs and various processing programs stored in the memory unit 22 in response to operations on the operation unit 23, expands them into RAM, and executes various processes, including the imaging control process described later, according to the expanded programs, thereby centrally controlling the operation of each part of the imaging console 2, as well as the radiation irradiation and reading operations of the imaging device 1.

[0025] The storage unit 22 is composed of non-volatile semiconductor memory, a hard disk, or the like. The storage unit 22 stores data such as various programs executed by the control unit 21, parameters necessary for processing by the programs, or processing results. For example, the storage unit 22 stores a program for executing the imaging control processing shown in Figure 2. The storage unit 22 also stores radiation irradiation conditions and image reading conditions in association with the area to be inspected and the imaging direction. Various programs are stored in the form of readable program code, and the control unit 21 sequentially executes operations according to the program code.

[0026] The operation unit 23 is configured with a keyboard equipped with cursor keys, number input keys, and various function keys, and a pointing device such as a mouse, and outputs instruction signals input by key operations on the keyboard or mouse to the control unit 21. The operation unit 23 may also be equipped with a touch panel on the display screen of the display unit 24, in which case it outputs instruction signals input via the touch panel to the control unit 21.

[0027] The display unit 24 is composed of monitors such as LCDs (Liquid Crystal Displays) and CRTs (Cathode Ray Tubes), and displays input instructions and data from the operation unit 23 according to the instructions of the display signals input from the control unit 21.

[0028] The communication unit 25 is equipped with a LAN adapter, modem, TA (Terminal Adapter), etc., and controls the transmission and reception of data between it and each device connected to the communication network NT.

[0029] [Configuration of diagnostic console 3] The diagnostic console 3 is a dynamic image analysis device that acquires dynamic images of the chest from the imaging console 2, and generates and outputs information regarding pleural adhesions based on the acquired dynamic images. As shown in Figure 1, the diagnostic console 3 is configured to include a control unit 31, a storage unit 32, an operation unit 33, a display unit 34, and a communication unit 35, with each unit connected by a bus 36.

[0030] The control unit 31 is composed of a CPU, RAM, etc. The CPU of the control unit 31 reads system programs and various processing programs stored in the memory unit 32 in response to operations on the operation unit 33, expands them into RAM, and executes various processes, including dynamic analysis processing A described later, according to the expanded programs, thereby centrally controlling the operation of each part of the diagnostic console 3. By executing dynamic analysis processing A, the control unit 31 functions as an acquisition unit and a generation unit.

[0031] The storage unit 32 is composed of non-volatile semiconductor memory, a hard disk, or the like. The storage unit 32 stores various programs, including the program for executing the dynamic analysis process A in the control unit 31, as well as parameters necessary for executing the processing by the programs, and data such as processing results. These various programs are stored in the form of readable program code, and the control unit 31 sequentially executes operations according to the program code.

[0032] The operation unit 33 is configured with a keyboard equipped with cursor keys, number input keys, and various function keys, and a pointing device such as a mouse, and outputs instruction signals input by the user through key operations on the keyboard or mouse to the control unit 31. The operation unit 33 may also be equipped with a touch panel on the display screen of the display unit 34, in which case it outputs instruction signals input via the touch panel to the control unit 31.

[0033] The display unit 34 is composed of a monitor such as an LCD or CRT, and displays various information according to the instructions of the display signals input from the control unit 31. The display unit 34 also functions as an output unit.

[0034] The communication unit 35 is equipped with a LAN adapter, modem, TA, etc., and controls the transmission and reception of data between it and each device connected to the communication network NT.

[0035] [Operation of Dynamic Analysis System 100] Next, the operation of the dynamic analysis system 100 in this embodiment will be described.

[0036] (Operation of imaging device 1 and imaging console 2) First, we will explain the shooting operation using the shooting device 1 and the shooting console 2. Figure 2 shows the shooting control process executed in the control unit 21 of the shooting console 2. The shooting control process is executed through the cooperation of the control unit 21 and the program stored in the storage unit 22.

[0037] First, the control unit 21 receives patient information and examination information of the subject (subject M) through the operation of the operator's control unit 23 (step S1).

[0038] Next, the control unit 21 reads the radiation irradiation conditions from the storage unit 22 and sets them in the radiation irradiation control device 12, and reads the image reading conditions from the storage unit 22 and sets them in the reading control device 14 (step S2).

[0039] Next, the control unit 21 waits for an instruction to irradiate with radiation via the operation unit 23 (step S3). At this point, the person performing the imaging positions the subject M between the radiation source 11 and the radiation detection unit 13. When the imaging preparation is complete, the person inputs an instruction to irradiate with radiation by operating the operation unit 23.

[0040] When a radiation irradiation instruction is input via the operation unit 23 (step S3; YES), the control unit 21 outputs a start-up instruction to the radiation irradiation control device 12 and the reading control device 14, and starts dynamic imaging (step S4). That is, the radiation source 11 irradiates with radiation at pulse intervals set in the radiation irradiation control device 12, and the radiation detection unit 13 acquires frame images. During dynamic imaging, the person performing the imaging provides breathing guidance, such as "inhale" and "exhale," and the chest is imaged while breathing. The imaging device 1 may also be equipped with an audio output unit and a display unit, and when a start-up instruction is output, it may provide voice guidance such as "inhale" and "exhale" or display information.

[0041] When the operation unit 23 inputs a signal to end radiation irradiation, the control unit 21 outputs a signal to end imaging to the radiation irradiation control device 12 and the reading control device 14, and stops the imaging operation.

[0042] The frame images acquired through shooting are sequentially input to the shooting console 2, and the control unit 21 associates a number indicating the shooting order (frame number) with the input frame images and stores it in the storage unit 22 (step S5), and also displays it on the display unit 24 (step S6). The person performing the shooting checks the positioning etc. from the displayed dynamic image and determines whether an image suitable for diagnosis has been acquired through shooting (shooting OK) or whether reshooting is necessary (shooting NG). Then, they operate the operation unit 23 to input the result of the determination.

[0043] When a determination result indicating OK for shooting is input through a predetermined operation of the operation unit 23 (step S7; YES), the control unit 21 attaches information such as an identification ID for identifying the dynamic image, patient information, examination information, radiation irradiation conditions, image reading conditions, and a number indicating the shooting order (frame number) to each of the series of frame images acquired by dynamic imaging (for example, by writing it in the header area of ​​the image data in DICOM format), and transmits it to the diagnostic console 3 via the communication unit 25 (step S8). Then, it terminates the shooting control process. On the other hand, when a determination result indicating NG for shooting is input through 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), and terminates the shooting control process. In this case, reshooting is required.

[0044] (Operation of diagnostic console 3) Next, we will explain the operation of the diagnostic console 3. In the diagnostic console 3, when a series of frame images of the chest in motion are received from the imaging console 2 via the communication unit 35, the control unit 31 and the program stored in the storage unit 32 work together to execute the motion analysis process A shown in Figure 3.

[0045] Here, the rib cage and diaphragm are soft containers for the lungs, and when we want to breathe, we move the rib cage and diaphragm, causing changes in internal pressure that expand and contract the lungs, allowing air to enter and exit. During breathing, the lungs expand and contract due to the up-and-down movement of the diaphragm and the expansion and contraction of the rib cage. Normally, the rib cage and lungs are separate, but if inflammation or other factors cause adhesions between the parietal pleura (the membrane lining the rib cage) and the visceral pleura (the membrane surrounding the lungs), the rib cage and lungs become tightly attached at that point. As a result, the range of motion of the visceral pleura in the area of ​​adhesion is reduced compared to the surrounding areas. Therefore, in dynamic analysis process A, dynamic images of the chest are analyzed, and information regarding pleural adhesion is generated and output based on the amount of movement of the region within the lung area in the dynamic image that includes at least the region adjacent to the thoracic cage (the thoracic cage on the side of the body).

[0046] The dynamic analysis process A will be explained below with reference to Figure 3. First, the control unit 31 acquires the motion image received by the communication unit 35 (step S11).

[0047] Next, the control unit 31 performs preprocessing on the acquired motion image (step S12). In the preprocessing stage, the control unit 31 acquires frame images from the acquired dynamic images of the section to be analyzed (used to generate information about pleural adhesions).

[0048] For example, the control unit 31 acquires frame images of the exhalation period of the dynamic image (e.g., from maximum inspiration to maximum expiration) as frame images of the section to be analyzed. Frame images of the exhalation period can be acquired, for example, by recognizing the lung region from each frame image of the dynamic image and extracting frame images from the maximum (maximum) to the minimum (minimum) area of ​​the recognized lung region. Alternatively, the distance between the lung apex and the diaphragm may be measured from each frame image of the dynamic image, and frame images of the section from the maximum (maximum) to the minimum (minimum) distance between the lung apex and the diaphragm may be acquired as frame images of the exhalation period. Alternatively, frame images of the section from the maximum (maximum) to the minimum (minimum) density of the lung region in the dynamic image may be acquired as frame images of the inspiratory period. The user may also be asked to specify the section to be analyzed.

[0049] Next, as shown in Figure 4, the control unit 31 applies a bone suppression process (BS process) to each acquired frame image (original image) to recognize the bone region and reduce the bone signal component to generate a bone suppression image (BS image), and then applies a frequency enhancement process to the generated BS image to obtain a frequency enhancement image. In dynamic images of the chest, various structures such as ribs, as well as the lungs, are represented on a single image. Therefore, simply calculating the corresponding points of the patterns on the image to extract the lung motion vector will result in them being mixed with the corresponding points of bones, which move differently from the lungs. By applying bone attenuation to the original image, it is possible to calculate the corresponding points in the lung region accurately in subsequent processing. Furthermore, the patterns of the lung region visible in dynamic images are mainly pulmonary blood vessels and are composed of high-frequency components, while extrapulmonary organs, fat, and muscle exhibit characteristics in low-frequency components. Therefore, it is desirable to perform frequency enhancement processing in advance to emphasize the specific high-frequency components corresponding to the pulmonary blood vessels.

[0050] Next, the control unit 31 performs optical flow between adjacent frame images in the time direction (hereinafter referred to as "between adjacent frame images") for the pre-processed frame images of the section to be analyzed, and calculates motion vectors by finding corresponding points between adjacent frame images for each small region (step S13). For example, the first frame of the analysis interval (e.g., the maximum inspiratory position frame, referred to as frame 1) is divided into multiple sub-regions, and for each sub-region, corresponding points are sequentially found between adjacent frame images using dense optical flow, and motion vectors are calculated. A sub-region may be a pixel, or it may be a pixel block consisting of multiple pixels (e.g., 5mm x 5mm). In the case of a pixel block, for example, the motion vector of the center of the sub-region is calculated. In this embodiment, the case where the sub-region is a 5mm x 5mm pixel block is described. Here, the motion vector is calculated between adjacent frame images, but it may also be calculated between frames n frames ahead (where n is a positive integer). Furthermore, to reduce measurement errors in heartbeat-induced motion, n may be the number of frames in one heartbeat cycle. Note that the calculation of motion vectors only needs to be performed for each sub-region of the lung region. The amount of motion in each sub-region of the lung region in the dynamic image is based on the respiratory volume.

[0051] Next, the control unit 31 merges (integrates) the multiple motion vectors obtained in step S13 for each small region (step S14).

[0052] Figure 5 is a diagram illustrating the merging process in step S14. Here, the motion vector from the start frame to the end frame is calculated. As shown in Figure 5, in step S14, first, the sum of the motion vector obtained in step S13 from the start frame image (frame 1) and the adjacent start frame image (frame 1+n) and the motion vector obtained from the adjacent start frame image (frame 1+n) and the next adjacent start frame image (frame 2+n) (shown by a thick arrow in Figure 5) is calculated. Next, the sum of the calculated motion vectors is added to the sum of the next motion vector. This is repeated until all the calculated motion vectors have been added together. This allows us to calculate a motion vector representing the movement from the start frame image to the end frame image of the subject being analyzed. This motion vector is saved at the vector start coordinates or vector end coordinates. For example, if the reference frame image described later is the frame image at the maximum intake position, the motion vector should be saved at the vector start coordinates.

[0053] The above method for calculating motion vectors is just one example, and the method is not particularly limited as long as it can ultimately calculate motion vectors for each small region from the start frame image to the end frame image of the exhalation period. However, during the deep breath period (approximately 5 seconds), the movement and deformation of the lungs due to respiration are large, causing rapid changes in the appearance of the image, making it very difficult to calculate corresponding points on the image. Therefore, as described above, by calculating corresponding points in short time units, such as between adjacent frame images in the time direction, and merging them, the motion vector for the exhalation period can be calculated with high accuracy. In addition, various filtering processes such as Gaussian filters may be performed as post-processing of the motion vector calculation results to remove noise.

[0054] Next, the control unit 31 calculates the amount of motion (length of the motion vector) for each sub-region based on the calculated motion vector and creates a motion amount MAP showing the amount of motion for each sub-region (step S15). When creating the motion amount MAP, motion vectors outside the lung region may be excluded from the sub-region motion vectors. For extracting the lung region (lung field region), known methods such as the edge detection method described in Japanese Patent Application Publication No. 2018-148964 can be used.

[0055] Next, the control unit 31 executes adhesion information generation process A, refers to the motion amount MAP created in step S15, and generates information regarding pleural adhesion (adhesion information) based on the motion amount of the region within the lung area that includes at least the region adjacent to the rib cage (step S16). In this application, the term "thoracic cage" refers to the thoracic cage on the lateral side of the body. Furthermore, the region adjacent to the thoracic cage within the lung region refers to the region representing the visceral pleura, and is a small region located on the contour of the lung region on the thoracic side (located at the boundary with the thoracic cage) in dynamic images.

[0056] In the adhesion information generation process A of step S13, the control unit 31 generates information regarding pleural adhesions by combining one or more of the following first to fourth methods.

[0057] (1st method) When pleural adhesions are present, the movement of the adhesion-affected area (small region) in the visceral pleura becomes reduced. Therefore, in the first method, the control unit 31 determines whether the amount of movement of each small region located on the contour of the thoracic side of the lung region in the dynamic image is below a predetermined threshold. If it determines that the amount of movement is below the predetermined threshold, it stores information indicating that the amount of movement of that small region is below the predetermined threshold, or information indicating that the amount of movement of that small region is decreasing, in RAM or the like, associated with that small region, as information regarding pleural adhesion. If it determines that the amount of movement exceeds the predetermined threshold, it stores information indicating that the amount of movement of that small region is not below the predetermined threshold (exceeds the predetermined threshold), or information indicating that the amount of movement of that small region is not decreasing, in RAM or the like, associated with that small region, as information regarding pleural adhesion. The predetermined threshold used for comparison with the amount of motion in a small area is a value verified in clinical trials.

[0058] (Second method) When pleural adhesions are present, the movement of the adhered area (small region) in the visceral pleura becomes smaller, and the difference in movement between this area and the surrounding area becomes larger compared to areas without adhesions. Therefore, in the second and third methods, the control unit 31 compares the amount of movement of the region representing the visceral pleura in the dynamic image with the amount of movement of a region in the lung region that is different from the visceral pleura to determine whether or not the amount of movement of the region representing the visceral pleura has decreased, and generates the determination result as information regarding pleural adhesion.

[0059] In the second method, the control unit 31 calculates the difference (or ratio; the same applies hereinafter in this embodiment) between the amount of movement of each small region representing the visceral pleura in the dynamic image, i.e., each small region located on the thoracic contour of the lung region in the dynamic image, and the amount of movement of surrounding small regions within the lung region (for example, small regions within the lung region whose distance from that small region is within a predetermined threshold (for example, small regions with a radius of 10 mm from the center of the small region)), and determines whether the calculated difference (absolute value of the difference; the same applies hereinafter) is greater than or equal to a predetermined threshold. If it is determined that the calculated difference is greater than or equal to a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is greater than or equal to a predetermined threshold, or information indicating that the amount of movement of that small region has decreased, in RAM or the like as information relating to pleural adhesion of that small region, associated with that small region. If it is determined that the calculated difference is less than a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not greater than or equal to a predetermined threshold, or information indicating that the amount of movement of that small region has not decreased, in RAM or the like as information relating to pleural adhesion of that small region, associated with that small region. The predetermined threshold used for comparison with the above difference is a value verified in clinical trials. Furthermore, the movement amount of the surrounding subregions mentioned above is, for example, a representative value (mean, median, maximum, etc.; the same applies hereinafter) of the movement amount of the surrounding subregions.

[0060] (Third method) In the third method, the control unit 31 first performs a process to detect shadows continuous with each small region located on the thoracic contour of the lung region in the dynamic image. Here, shadows continuous with small regions located on the thoracic contour of the lung region refer to, for example, cord-like shadows as indicated by arrows in the enlarged view of the lung region in Figure 6. Figure 6 shows shadows continuous with small region R on the lung contour. Shadows continuous with small regions can be detected, for example, by using a small region located on the thoracic contour of the lung region as a starting point, determining the region where the difference from the signal value of that small region is within a predetermined threshold using a region expansion method, and then extracting the region that is continuous with the small region and has a thickness of 2 mm to 3 mm from the determined region. When a shadow continuous with that small region is detected within the lung region, the control unit 31 calculates the difference between the amount of movement of that small region and the amount of movement of other small regions on the shadow continuous with that small region within the lung region, and determines whether the calculated difference is greater than or equal to a predetermined threshold. If the control unit 31 determines that the calculated difference is greater than or equal to a predetermined threshold, it stores information indicating that the calculated difference is greater than or equal to a predetermined threshold, or information indicating that the amount of movement of the small region has decreased, in RAM or the like, as information relating to pleural adhesion in that small region. If the control unit 31 determines that the calculated difference is less than or equal to a predetermined threshold, it stores information indicating that the calculated difference is not greater than or equal to a predetermined threshold, or information indicating that the amount of movement of the small region has not decreased, in RAM or the like, as information relating to pleural adhesion in that small region. The predetermined threshold used for comparison with the above difference is a value verified in clinical trials. Furthermore, the amount of movement of other subregions located on consecutive shadows within a subregion is, for example, a representative value of the amount of movement of other subregions located on consecutive shadows within a subregion.

[0061] (4th method) When pleural adhesions are present, the movement of a small area in the visceral pleura where the adhesions are located decreases, resulting in a difference in the amount of movement between that small area and other small areas on the adjacent shadow. Consequently, the variability in the amount of movement within the region consisting of that small area and the other small areas on the adjacent shadow becomes large. Therefore, the fourth method generates information about pleural adhesions based on this variability. For example, the control unit 31 processes each small region located on the thoracic contour of the lung region in the dynamic image to detect continuous shadows within the lung region. If detected, it calculates, for example, the standard deviation or variance as the variation in the amount of movement within the region consisting of that small region and other small regions on the continuous shadows. It then determines whether the calculated variation is above a predetermined threshold. If it determines that the calculated variation is above a predetermined threshold, the control unit 31 stores information indicating that the calculated variation is above a predetermined threshold or information indicating that the amount of movement of that small region has decreased, as information relating to pleural adhesion of that small region, in RAM or the like. If it determines that the calculated variation is below a predetermined threshold, the control unit 31 stores information indicating that the calculated variation is not above a predetermined threshold or information indicating that the amount of movement of that small region has not decreased, as information relating to pleural adhesion of that small region, in RAM or the like. The predetermined threshold used for comparison with the above-mentioned variability is a value verified in clinical experiments. Furthermore, for detecting continuous shadows in small areas, for example, the method described in the third method can be used.

[0062] (A combination of two or more of the methods 1 through 4) As described above, information regarding pleural adhesions may be generated using any of the first to fourth methods, or by combining multiple methods. The method or combination of methods used to generate information regarding pleural adhesions may be predetermined, or it may be set by the user via the control unit 33. Alternatively, information regarding pleural adhesions may be generated using a method that meets the user's needs, such as increasing detection sensitivity (not wanting to miss people with adhesions) or increasing specificity (carefully detecting people with adhesions), as set by the control unit 33. Below, an example of generating information regarding pleural adhesions by combining multiple methods according to the user's needs will be described.

[0063] Figure 7 is a flowchart showing the flow of adhesion information generation process A (high sensitivity) when the user's need is set to "increase detection sensitivity". Adhesion information generation process A (high sensitivity) is executed in cooperation with the control unit 31 and the program stored in the storage unit 32.

[0064] First, the control unit 31 selects a small region from the dynamic image (step S161). Next, the control unit 31 determines whether the selected small region is located on the thoracic contour of the lung region (step S162). If the control unit 31 determines that the selected subregion is not located on the thoracic contour of the lung region (step S162; NO), it proceeds to step S170.

[0065] If the control unit 31 determines that the selected subregion is located on the thoracic contour of the lung region (step S162; YES), it determines whether the amount of movement of the subregion is below a predetermined threshold (step S163). If the control unit 31 determines that the amount of movement of the selected subregion is below a predetermined threshold (step S163; YES), it determines that the movement of the selected subregion has decreased, stores information indicating that the movement of the subregion has decreased in RAM or the like, as information relating to the subregion's pleural adhesion (step S168), and proceeds to step S170.

[0066] If the control unit 31 determines that the amount of movement of the selected small region exceeds a predetermined threshold (step S163; NO), it performs a process to detect shadows continuous with the selected small region within the lung region of the dynamic image (step S164). Next, the control unit 31 determines whether or not a continuous shadow is detected within the lung region of the dynamic image with respect to the selected subregion (step S165). If the control unit 31 determines that no continuous shadow was detected in the lung region of the dynamic image with respect to the selected subregion (step S165; NO), it determines that the amount of motion of the selected subregion has not decreased, and stores information indicating that the amount of motion of the subregion has not decreased in RAM or the like, as information regarding pleural adhesion in that subregion, in association with that subregion (step S169), and proceeds to step S170.

[0067] If the control unit 31 determines that a continuous shadow is detected within the lung region of the dynamic image (step S165; YES), it calculates the difference between the amount of movement of the selected small region and the amount of movement (representative value) of other small regions on the continuous shadow with the selected small region (step S166). Then, the control unit 31 determines whether the calculated difference is greater than or equal to a predetermined threshold. If it determines that the calculated difference is greater than or equal to a predetermined threshold (step S167; YES), the control unit 31 determines that the amount of movement of the selected small region has decreased, and stores information indicating that the amount of movement of that small region has decreased in RAM or the like, as information relating to pleural adhesion in that small region (step S168), and proceeds to step S170. If the calculated difference is determined to be below a predetermined threshold (step S167; NO), the control unit 31 determines that the amount of movement of the selected subregion has not decreased, stores information indicating that the amount of movement of the subregion has not decreased in RAM or the like, as information relating to the subregion's pleural adhesion (step S169), and proceeds to step S170.

[0068] In step S170, the control unit 31 determines whether the processing in steps S161 to S169 for all sub-regions has been completed (step S170). If the control unit 31 determines that the processing in steps S161 to S169 has not been completed for all subregions (step S170; NO), it returns to step S161, selects a subregion that has not yet been processed, and executes the processing in steps S161 to S169. If the control unit 31 determines that the processing in steps S161 to S169 has been completed for all subregions (step S170; YES), it terminates the adhesion information generation process A (high sensitivity).

[0069] In adhesion information generation process A (high sensitivity) shown in Figure 7, in step S163, a judgment is made using the first method described above. If the judgment result is YES, it is determined that the amount of movement of the small area has decreased, and information regarding pleural adhesions indicating this is generated. If the judgment result is NO, a judgment is made using the third method described above. If the judgment result is YES, it is determined that the amount of movement of the selected small area has decreased, and information regarding pleural adhesions indicating this is generated. If the result is NO, it is determined that the amount of movement of the selected small area has not decreased, and information regarding pleural adhesions indicating this is generated. Therefore, the sensitivity of adhesions can be increased compared to generating information regarding pleural adhesions using only the first method or only the third method.

[0070] Furthermore, in the processing shown in Figure 7, the second method may be used instead of the first method. Also, the fourth method may be used instead of the third method. Alternatively, all of the first to fourth methods may be used, and if the result of all methods is NO, it may be determined that the amount of movement of the selected subregion has not decreased. If there is even one YES result, it may be determined that the amount of movement of the selected subregion has decreased, and the results may be generated as information regarding pleural adhesions.

[0071] Figure 8 is a flowchart showing the flow of adhesion information generation process A (high specificity) when the user's need is set to "increase specificity". Adhesion information generation process A (high specificity) is executed through the cooperation of the control unit 31 and the program stored in the storage unit 32.

[0072] First, the control unit 31 selects a small region from the dynamic image (step S181). Next, the control unit 31 determines whether the selected small region is located on the thoracic contour of the lung region (step S182). If the control unit 31 determines that the selected subregion is not located on the thoracic contour of the lung region (step S182; NO), it proceeds to step S190.

[0073] If the control unit 31 determines that the selected subregion is located on the thoracic contour of the lung region (step S182; YES), it determines whether the amount of movement of the subregion is below a predetermined threshold (step S183). If the control unit 31 determines that the amount of movement of the selected subregion exceeds a predetermined threshold (step S183; NO), it determines that the movement of the selected subregion has not decreased, stores information indicating that the movement of the subregion has not decreased in RAM or the like, as information relating to the subregion's pleural adhesion (step S189), and proceeds to step S190.

[0074] If the control unit 31 determines that the amount of movement of the selected small region is below a predetermined threshold (step S183; YES), it detects a shadow continuous with the selected small region within the lung region of the dynamic image (step S184). The detection method in step S184 can be the method described in the third method above. Next, the control unit 31 determines whether or not a continuous shadow is detected within the lung region of the dynamic image with respect to the selected subregion (step S185). If the control unit 31 determines that no continuous shadow was detected in the lung region of the dynamic image with respect to the selected subregion (step S185; NO), it determines that the amount of motion of the selected subregion has not decreased, and stores information indicating that the amount of motion of the subregion has not decreased in RAM or the like, as information regarding pleural adhesion in that subregion, in association with that subregion (step S189), and proceeds to step S190.

[0075] If the control unit 31 determines that a shadow continuous with the selected sub-region is detected within the lung region of the dynamic image (step S185; YES), it calculates the difference between the selected sub-region and the amount of movement (representative value) of other sub-regions on the shadow continuous with the selected sub-region (step S186). Then, the control unit 31 determines whether the calculated difference is greater than or equal to a predetermined threshold. If it determines that the calculated difference is greater than or equal to a predetermined threshold (step S187; YES), the control unit 31 determines that the amount of movement of the selected small region has decreased, and stores information indicating that the amount of movement of that small region has decreased in RAM or the like, as information relating to pleural adhesion in that small region (step S188), and proceeds to step S190. If the calculated difference is determined to be below a predetermined threshold (step S187; NO), the control unit 31 determines that the amount of movement of the selected subregion has not decreased, stores information indicating that the amount of movement of the subregion has not decreased in RAM or the like, as information relating to the subregion's pleural adhesion (step S189), and proceeds to step S190.

[0076] In step S190, the control unit 31 determines whether the processing in steps S181 to S189 for all sub-regions has been completed (step S190). If the control unit 31 determines that the processing in steps S181 to S189 has not been completed for all subregions (step S190; NO), it returns to step S181, selects a subregion that has not yet been processed, and executes the processing in steps S181 to S189. If the control unit 31 determines that the processing in steps S181 to S189 has been completed for all subregions (step S190; YES), it terminates the adhesion information generation process A (high specificity) shown in Figure 8.

[0077] In adhesion information generation process A (high specificity) shown in Figure 8, in step S183, a judgment is made using the first method described above. If the judgment result is YES, a judgment is made using the third method described above. If the judgment result using the third method is YES, it is determined that the amount of movement of the selected small region has decreased, and information regarding pleural adhesions indicating this is generated. If the judgment result using the first method is NO, or if the judgment result using the first method is YES but the judgment result using the third method is NO, it is determined that the amount of movement of the selected small region has not decreased, and information regarding pleural adhesions indicating this is generated. Therefore, compared to generating information regarding pleural adhesions using only the first method or only the third method, it is possible to make a more careful judgment as to whether or not the amount of movement has decreased, and the specificity of adhesions can be increased. Furthermore, in the processing shown in Figure 8, the second method may be used instead of the first method. Also, the fourth method may be used instead of the third method. Alternatively, all of the first to fourth methods may be used, and if the result of all methods is YES, it may be determined that the amount of movement of the selected subregion has decreased. If even one result is NO, it may be determined that the amount of movement of the selected subregion has not decreased, and the results may be generated as information regarding pleural adhesions.

[0078] Returning to Figure 3, once the adhesion information generation process A in step S16 is completed, the control unit 31 outputs the generated information regarding pleural adhesions (step S17). The generated information regarding pleural adhesions may be output as text or numbers, or by coloring the image with text or numbers corresponding to those values.

[0079] For example, if information regarding pleural adhesion is generated by the first method in step S16, the control unit 31 maps motion vectors to each sub-region on the reference frame image (e.g., the frame image at maximum inspiration), as shown in Figure 9, and displays the motion vectors of sub-regions (motion reduction regions) where the amount of movement is determined to be below a predetermined threshold in a different color on the display unit 34 than the motion vectors of other sub-regions. As shown in Figure 9, the sub-regions or motion vectors may be further highlighted by annotation or the like. Alternatively, as shown in Figure 10, each sub-region on the reference frame image may be assigned a color corresponding to the amount of movement and displayed on the display unit 34. As shown in Figure 10, the sub-regions (motion reduction regions) where the amount of movement is determined to be below a predetermined threshold may be further highlighted by annotation or the like. Alternatively, as shown in Figure 11, the sub-regions (motion reduction regions) on the reference frame image where the amount of movement is determined to be below a predetermined threshold may be assigned a predetermined color or marker and displayed on the display unit 34. This makes it possible to highlight areas where the amount of movement of the visceral pleura is reduced in an easily understandable way for the user.

[0080] Furthermore, for example, if information regarding pleural adhesion is generated by the second method in step S16, the control unit 31 displays on the display unit 34, as shown in Figure 11, a predetermined color or marker, in small areas (areas with reduced movement) on the reference frame image where the difference in movement amount from the surrounding small areas is determined to be above a predetermined threshold. This makes it possible to highlight areas where the movement amount of the visceral pleura is reduced in an easy-to-understand manner for the user.

[0081] Furthermore, for example, if information regarding pleural adhesion is generated by the third method in step S16, the control unit 31 maps motion vectors to a small area located on the thoracic contour of the lung region on the reference frame image, where the difference between the amount of movement of that small area and the amount of movement of a small area of ​​shadow continuous to it is determined to be greater than or equal to a predetermined threshold (referred to as the area of ​​interest), and to other small areas on the shadow continuous to the area of ​​interest, and displays them on the display unit 34. The motion vector of the area of ​​interest is displayed on the display unit 34 in a different color from the motion vectors of other small areas on the shadow. This makes it possible to highlight areas where the amount of movement of the visceral pleura is reduced, making it easier for the user to understand. It is also possible to compare the motion vectors of the area of ​​interest with those of other small areas on the shadow continuous to it. Alternatively, as shown in Figure 11, a predetermined color or marker may be applied to the position of the area of ​​interest on the reference frame image and displayed on the display unit 34. Furthermore, as shown in Figure 13, the area of ​​interest and other small areas of shadow on the shadow continuous to the area of ​​interest may be displayed with markers or the like.

[0082] Furthermore, for example, if information regarding pleural adhesion is generated by the fourth method in step S16, the control unit 31 maps motion vectors to the small area located on the thoracic contour of the lung region on the reference frame image, which is determined to have a variation in the amount of motion within that small area and other small areas on the shadow continuous with that small area (referred to as the area of ​​interest), and to the other small areas on the shadow continuous with that area, and displays them on the display unit 34, as shown in Figure 14. Also, as shown in Figure 14, lines are displayed connecting the start and end points of the motion vectors of the aforementioned small areas on the continuous shadow (including the small areas on the contour). This makes it possible to highlight areas where the amount of motion of the visceral pleura is reduced and show them to the user in an easy-to-understand manner. In addition, the user can check the degree of variation in the amount of motion on the shadow.

[0083] Furthermore, for example, if information regarding pleural adhesions is generated in step S16 using multiple methods from the first to fourth methods, the control unit 31 displays the small areas where the amount of movement is determined to be reduced on the display unit 34, using a predetermined color or marker, as shown in Figure 11. This highlights the areas where the amount of movement of the visceral pleura is reduced, making it easier for the user to understand. When the process in step S17 is completed, the control unit 31 terminates the dynamic analysis process A.

[0084] In the above explanation, the amount of movement of a small region within the lung area was described as the change in the position of the small region from a reference frame image (e.g., a frame image at maximum inspiration) (absolute amount), but it may also be described as the distance (relative amount) from the small region on the thoracic cage in the reference frame image. If the amount of movement is relative, for example, after the preprocessing in step S12 of Figure 3, an optical flow is performed to find corresponding points between adjacent frame images for each small region within the lung area, and the amount of movement is defined as the distance between the position of the small region in each frame image and the position of the small region located on the thoracic cage in the reference frame image (e.g., a small region outside the lung area adjacent to a small region located on the thoracic-side contour of the lung area).

[0085] As described above, the control unit 31 of the diagnostic console 3 in the first embodiment acquires dynamic images of the chest obtained by dynamic radiography, generates information on pleural adhesions based on the amount of movement of the region in the lung area that includes at least the region adjacent to the rib cage in the acquired dynamic images, and outputs the generated information on pleural adhesions via the display unit 34. Therefore, since information about pleural adhesions can be generated using dynamic images of the chest obtained by dynamic radiography, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose without using conventional methods such as 4D-CT, which is difficult to introduce to general medical facilities due to the cost of the equipment, and also has problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow for an overview of the entire subject because it only images a local area, and imaging the whole body would require an enormous amount of time and difficult imaging techniques. As a result, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose in general medical facilities without introducing expensive and large-scale equipment. Furthermore, by focusing on the region adjacent to the thoracic cage (representing the visceral pleura), which is the area where pleural adhesions occur in dynamic images of the lung field, and generating information about pleural adhesions based on the amount of movement of the region including the area adjacent to the thoracic cage, it becomes possible to obtain information about pleural adhesions with greater accuracy compared to conventional techniques that detect adhesions from changes in diaphragmatic shape or inconsistencies between the phase and respiratory phase related to diaphragmatic displacement. In addition, by generating and outputting information about pleural adhesions for each small region, users can easily understand the location and extent of potential adhesions. Moreover, although there are fewer vascular shadows around the thoracic cage compared to the central part of the lung region because it is located in the periphery of the lung, cord-like shadows are easily recognizable, allowing for accurate calculation of the amount of movement and reducing the chance of missing adhesions.

[0086] <Second Embodiment> Next, a second embodiment of the present invention will be described. In the second embodiment, an example is described in which information regarding pleural adhesions is generated based on the difference (or ratio; the same applies hereinafter in this embodiment) between the amount of movement of a first region within the lung region and the amount of movement of a second region different from the first region.

[0087] The configuration of the dynamic analysis system 100, imaging device 1, imaging console 2, and diagnostic console 3 in the second embodiment is the same as that described in the first embodiment, so we will refer to that description. Also, the operation of the imaging device 1 and imaging console 2 is the same as that described in the first embodiment, so we will refer to that description, and below we will describe the operation of the diagnostic console 3 in the second embodiment.

[0088] In the second embodiment, when the diagnostic console 3 receives a series of frame images of dynamic chest images from the imaging console 2 via the communication unit 35, the control unit 31 and the program stored in the storage unit 32 work together to execute the dynamic analysis process B shown in Figure 15. By executing the dynamic analysis process B, the control unit 31 functions as an acquisition unit and a generation unit.

[0089] In the dynamic analysis process B, the control unit 31 first executes the processes in steps S21 to S25. The processes in steps S21 to S25 are the same as those in steps S11 to S15 in Figure 3, so the explanation will be based on that.

[0090] Next, the control unit 31 executes adhesion information generation process B (step S26). In the adhesion information generation process B of step S26, the control unit 31 refers to the motion amount MAP created in step S25 and generates information about pleural adhesion based on the difference between the motion amount of a first region within the lung region and the motion amount of a second region surrounding it. Specifically, the information about pleural adhesion is generated by one of the following methods 1 to 4.

[0091] (1st method) In the first method, the control unit 31 calculates the difference between the amount of movement of each sub-region (first region) within the lung region in the dynamic image and the amount of movement of the surrounding sub-regions (second region; in this case, sub-regions within a predetermined distance from the sub-regions of the first region (for example, within a radius of 30 mm from the center of the sub-regions of the first region)), and determines whether the calculated difference is greater than or equal to a predetermined threshold. If it is determined that the calculated difference is greater than or equal to a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is greater than or equal to a predetermined threshold, or information indicating that the amount of movement of the sub-region has decreased, in RAM or the like, associated with the sub-region, as information regarding pleural adhesion in that sub-region. If it is determined that the calculated difference is less than a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not greater than or equal to a predetermined threshold, or information indicating that the amount of movement of the sub-region has not decreased, in RAM or the like, associated with the sub-region, as information regarding pleural adhesion in that sub-region. Here, the surrounding subregions for which the difference is to be calculated may be limited to subregions adjacent in the vertical direction (up and down direction of the lung region), limited to subregions adjacent in the horizontal direction (left and right direction of the lung region), or subregions adjacent in both directions. If there are multiple surrounding subregions, the amount of movement of the surrounding subregions shall be a representative value of the amount of movement of the multiple surrounding subregions. Furthermore, the predetermined threshold value used for comparison with the calculated difference shall be a value verified in clinical experiments.

[0092] (Second method) In the second method, the control unit 31 divides the lung region in the dynamic image into a plurality of blocks in a predetermined direction (for example, the vertical direction of the lung region, the left-right direction, and the vertical and left-right directions), and generates information regarding pleural adhesion based on the difference between the amount of movement of each block (first region) and the amount of movement of the block adjacent to it (second region), or whether the difference is greater than or equal to a predetermined threshold. For example, the control unit 31 divides the lung region in the dynamic image into multiple blocks in a predetermined direction, calculates a representative value of the amount of movement of the sub-regions contained within each block as the amount of movement of each block, and calculates a representative value of the amount of movement of the sub-regions contained within the block adjacent to that block as the amount of movement of the adjacent block. Then, it calculates the difference between the representative value of the amount of movement of the sub-regions contained within each block and the representative value of the amount of movement of the sub-regions contained within the block adjacent to that block. If the control unit 31 determines that the calculated difference is greater than or equal to a predetermined threshold, it stores information indicating that the calculated difference is greater than or equal to a predetermined threshold, or information indicating that the amount of movement of that block (first region) has decreased, in RAM or the like, associated with that block, as information regarding pleural adhesion. If the control unit 31 determines that the calculated difference is less than or equal to a predetermined threshold, it stores information indicating that the calculated difference is not greater than or equal to a predetermined threshold, or information indicating that the amount of movement of that block (first region) has not decreased, in RAM or the like, associated with that block, as information regarding pleural adhesion. The predetermined thresholds mentioned above are values ​​obtained in clinical experiments.

[0093] The adjacent blocks for which the difference is to be calculated may be blocks adjacent in the vertical direction, blocks adjacent in the horizontal direction, or blocks adjacent in both the vertical and horizontal directions. Furthermore, the difference in movement between adjacent blocks is a representative value of the difference in movement calculated for each of the multiple adjacent blocks for which the difference is to be calculated. When calculating the difference in movement between blocks adjacent in both the vertical and horizontal directions, the vertical difference and the horizontal difference may be calculated separately or combined.

[0094] Figure 16(a) is an example of a graph plotting the amount of movement (representative value of the amount of movement within each block) of a lung region without areas of reduced movement due to adhesions, divided vertically at regular intervals, on a space where the horizontal axis is the amount of movement and the vertical axis is the vertical position in the lung region. Figure 16(b) is an example of a graph plotting the amount of movement (representative value of the amount of movement within each block) of a lung region with adhesions, divided vertically, on a space where the horizontal axis is the amount of movement and the vertical axis is the vertical position in the lung region. As shown in Figure 16(a), in the absence of adhesions, the amount of movement gradually increases as the position of the block decreases. On the other hand, in the presence of adhesions, as shown in Figure 16(b), the difference in the amount of movement between adjacent blocks becomes larger at the locations of adhesions (indicated by arrows). In this way, the presence or absence of areas of reduced movement due to adhesion can be determined by the magnitude of the difference in the amount of movement between adjacent blocks.

[0095] Furthermore, in the second method, it is preferable to divide the lung region in a consistent direction, specifically in the vertical direction of the lung region. This is because lung movement is predominantly due to the movement of the diaphragm, and dividing the lung region vertically makes it easier to represent lung movement. In addition, it is preferable to change the predetermined threshold for comparing the amount of movement of each block with the amount of movement of adjacent blocks according to their position in the lung region. For example, since the amount of movement is small in the upper lung field and large in the lower lung field, it is preferable to set a smaller threshold for the upper lung field and a larger threshold for the lower lung field.

[0096] (Third method) In the third method, the control unit 31 calculates the variability (standard deviation or variance) of the difference between the amount of movement of each block (a representative value of the amount of movement of a sub-region included in each block) and the amount of movement of the adjacent block for which the difference is to be calculated (a representative value of the amount of movement of a sub-region included in the adjacent block), as described in the second method, and stores the calculated variability in RAM or the like as information related to pleural adhesion. Alternatively, the control unit 31 compares the calculated variability with a predetermined threshold, and if it determines that the calculated variability is greater than or equal to the predetermined threshold, it stores in RAM or the like information indicating that the calculated variability is greater than or equal to the predetermined threshold, or information indicating that there is a region in the lung area where the amount of movement is reduced, as information related to pleural adhesion. If it determines that the calculated variability is less than or equal to the predetermined threshold, the control unit 31 stores in RAM or the like information indicating that the calculated variability is not greater than or equal to the predetermined threshold, or information indicating that the amount of movement in the lung area is not reduced, as information related to pleural adhesion. The predetermined threshold mentioned above is a value obtained in clinical experiments.

[0097] (4th method) In the fourth method, the control unit 31 calculates the difference between the amount of movement of each sub-region (first region) within the lung region in the dynamic image and the amount of movement of a sub-region (second region) located at a similar position in a different lung (left or right) from the lung in which the sub-region exists, and generates the calculation result as information regarding pleural adhesion. Alternatively, the control unit 31 compares the calculated difference with a predetermined threshold, and if it determines that the calculated difference is greater than or equal to the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is greater than or equal to the predetermined threshold, or information indicating that the amount of movement of the sub-region (first region) has decreased, as information regarding pleural adhesion of that sub-region, associated with that sub-region, in RAM or the like. If it determines that the calculated difference is less than the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not greater than or equal to the predetermined threshold, or information indicating that the amount of movement of the sub-region (first region) has not decreased, as information regarding pleural adhesion of that sub-region, associated with that sub-region, in RAM or the like. The predetermined threshold used to compare the calculated difference is a value verified in clinical experiments.

[0098] Adhesion information generation process B can accurately detect areas where movement is reduced due to adhesions, even in cases where lung movement and body movement are large and it is difficult to judge the decrease in movement of each sub-region. This is achieved by calculating the difference in movement between multiple sub-regions or between blocks consisting of multiple sub-regions. Furthermore, because the magnitude and direction of movement differ depending on the location of the lung region (for example, the apex and base of the lung), the decrease in movement can be detected with greater accuracy by calculating the difference with surrounding regions or regions at equivalent positions in the left and right lungs.

[0099] When the adhesion information generation process B in step S26 of Figure 9 is completed, the control unit 31 outputs the generated information regarding pleural adhesions (step S27). The generated information regarding pleural adhesions may be output as text or numbers, or by coloring the image with text or numbers corresponding to those values.

[0100] For example, if information regarding pleural adhesion is generated by the first method in step S26, the control unit 31 displays each small region on the reference frame image on the display unit 34 with a color corresponding to the difference between the amount of movement of that small region and the amount of movement of the surrounding small regions, as shown in Figure 17. In addition, as shown in Figure 18, each small region on the reference frame image may also be displayed on the display unit 34 with a color corresponding to the amount of movement of that small region. Alternatively, as shown in Figure 19, only small regions on the reference frame image where the difference in the amount of movement of the surrounding small regions exceeds a predetermined threshold may be colored and highlighted. This makes it possible to highlight areas of reduced movement that may be prone to adhesion and show them to the user in an easy-to-understand manner.

[0101] Furthermore, for example, if information regarding pleural adhesion is generated by the second method in step S26, the control unit 31 displays each block on the reference frame image on the display unit 34 with a color corresponding to the difference between the movement amount of that block and the movement amount of the adjacent block. In addition, as shown in Figure 20, each block on the reference frame image may also be displayed on the display unit 34 with a color corresponding to the movement amount of that block. Alternatively, blocks on the reference frame image where the difference between the movement amount of an adjacent block and that block exceeds a predetermined threshold may be highlighted by applying a predetermined color. This makes it possible to highlight areas of reduced movement where adhesion is possible and show them to the user in an easy-to-understand manner.

[0102] Furthermore, for example, if information regarding pleural adhesions is generated by the third method in step S26, the control unit 31 displays the information regarding pleural adhesions on the display unit 34, for example, as a numerical value. Alternatively, each block on the reference frame image may be colored according to the difference calculated for that block and displayed on the display unit 34. This makes it easy for the user to understand that there are areas of reduced movement within the lung region where adhesions may be present.

[0103] Furthermore, for example, if information regarding pleural adhesions is generated by the fourth method in step S26, the control unit 31 displays each small region on the reference frame image on the display unit 34 with a color corresponding to the difference between the amount of movement of that small region and the amount of movement of a small region in a similar position in a different lung region on the left or right side. Alternatively, small regions where the difference between the amount of movement of that small region and a small region in a similar position in a different lung region on the left or right side of the reference frame image exceeds a predetermined threshold may be highlighted by applying a predetermined color. This makes it possible to highlight areas where adhesions are possible and show them to the user in an easy-to-understand manner.

[0104] As described above, the control unit 31 of the diagnostic console 3 in the second embodiment acquires dynamic images of the chest obtained by dynamic radiography, generates information about pleural adhesions based on the difference or ratio between the amount of movement of a first region within the lung region in the acquired dynamic images and the amount of movement of a second region different from the first region, and displays the generated information about pleural adhesions using the display unit 34.

[0105] Therefore, since information about pleural adhesions can be generated using dynamic images of the chest obtained by dynamic radiography, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose without using conventional methods such as 4D-CT, which is difficult to introduce to general medical facilities due to the cost of the equipment, and also has problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow for an overview of the entire subject because it only images a local area, and imaging the whole body would require an enormous amount of time and difficult imaging techniques. As a result, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose in general medical facilities without introducing expensive and large-scale equipment. Furthermore, since it generates information about pleural adhesions based on the difference or ratio of movement between multiple regions within the lung area in dynamic images (for example, between small regions or between blocks consisting of multiple small regions), it is possible to easily and accurately acquire information about pleural adhesions with a low radiation dose, even in cases where lung movement and body movement are large and it is difficult to judge the decrease in movement of each region. In addition, it is possible to acquire information about pleural adhesions with higher accuracy compared to conventional techniques that detect adhesions from the mismatch between the phase related to changes in diaphragm shape and diaphragm displacement and the respiratory phase. Moreover, by generating and outputting information about pleural adhesions for each small region or block, users can easily understand the location and extent of potential adhesions.

[0106] <Third Embodiment> Next, a third embodiment of the present invention will be described. In a third embodiment, an example is described in which information regarding pleural adhesions is generated based on the amount of movement and threshold of a region within the lung area that does not include the region adjacent to the rib cage.

[0107] The configuration of the dynamic analysis system 100, imaging device 1, imaging console 2, and diagnostic console 3 in the third embodiment is the same as that described in the first embodiment, so we will refer to that description. Also, the operation of the imaging device 1 and imaging console 2 is the same as that described in the first embodiment, so we will refer to that description, and below we will describe the operation of the diagnostic console 3 in the third embodiment.

[0108] In the third embodiment, when the diagnostic console 3 receives a series of frame images of dynamic chest images from the imaging console 2 via the communication unit 35, the control unit 31 and the program stored in the storage unit 32 cooperate to execute the dynamic analysis process C shown in Figure 21. By executing the dynamic analysis process C, the control unit 31 functions as an acquisition unit and a generation unit.

[0109] In the dynamic analysis process C, the control unit 31 first executes the processes in steps S31 to S35. The processes in steps S31 to S35 are the same as those in steps S11 to S15 in Figure 3, so the explanation will be based on that.

[0110] Next, the control unit 31 executes the adhesion information generation process C (step S36). In the adhesion information generation process C of step S36, the control unit 31 refers to the movement amount MAP created in step S35 and generates information regarding pleural adhesions based on the movement amount of the region within the lung area that does not include the region adjacent to the rib cage.

[0111] In adhesion information generation process C, for example, the control unit 31 determines whether the amount of movement of each small region within the lung region, excluding the small region located on the thoracic contour of the lung region in the dynamic image, is below a predetermined threshold. If it determines that the amount of movement is below the predetermined threshold, it stores information indicating that the amount of movement of the small region is below the predetermined threshold, or information indicating that the amount of movement of the small region is decreasing, in RAM or the like, associated with the small region, as information regarding pleural adhesion in that small region. If it determines that the amount of movement exceeds the predetermined threshold, the control unit 31 stores information indicating that the amount of movement of the small region is not below the predetermined threshold (exceeds the predetermined threshold), or information indicating that the amount of movement of the small region is not decreasing, in RAM or the like, associated with the small region, as information regarding pleural adhesion in that small region. The predetermined threshold used for comparison with the amount of motion in a small area is a value verified in clinical trials.

[0112] In areas with pleural adhesions, the amount of movement is smaller compared to other areas. In adhesion information generation process C, for each small area within the lung region, excluding small areas located on the thoracic contour of the lung region in the dynamic image, it is determined whether the amount of movement is below a predetermined threshold. If it is determined that the amount of movement is below the predetermined threshold, it is determined that the small area may have adhesions, and information on pleural adhesions is generated based on the determination result. Therefore, information on pleural adhesions in the ventral or dorsal side of the body within the lung region can be easily and accurately generated with a low radiation dose.

[0113] When the adhesion information generation process C in step S36 of Figure 21 is completed, the control unit 31 outputs the generated information regarding pleural adhesions (step S37). The generated information regarding pleural adhesions may be output as text or numbers, or by coloring the image with text or numbers corresponding to those values.

[0114] For example, each small region on the reference frame image may be colored according to the amount of motion based on the motion vector and displayed on the display unit 34. Alternatively, motion vectors may be mapped to each small region on the reference frame image, and the motion vectors of small regions whose amount of motion is below a predetermined threshold may be displayed on the display unit 34 in a different color from the motion vectors of other small regions. Alternatively, small regions whose amount of motion is below a predetermined threshold may be highlighted by annotation or the like. Alternatively, small regions on the reference frame image whose amount of motion is below a predetermined threshold may be colored or marked with a predetermined color and displayed on the display unit 34. This makes it possible to highlight areas where adhesion is possible and the amount of motion is reduced, making it easy for the user to understand.

[0115] As described above, the control unit 31 of the diagnostic console 3 in the third embodiment acquires dynamic images of the chest obtained by dynamic radiography, generates information on pleural adhesions based on the amount of movement of the region in the lung area that does not include the region adjacent to the rib cage in the acquired dynamic images, and outputs the generated information on pleural adhesions via the display unit 34.

[0116] Therefore, since information about pleural adhesions can be generated using dynamic images of the chest obtained by dynamic radiography, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose without using conventional methods such as 4D-CT, which is difficult to introduce to general medical facilities due to the cost of the equipment, and also has problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow for an overview of the entire subject because it only images a local area, and imaging the whole body would require an enormous amount of time and difficult imaging techniques. As a result, it becomes possible to easily obtain information about pleural adhesions with a low radiation dose in general medical facilities without introducing expensive and large-scale equipment. Furthermore, since information on pleural adhesions is generated based on the amount of movement in the lung region in dynamic images, excluding the region adjacent to the rib cage, it is possible to easily and accurately generate information on pleural adhesions in the ventral or dorsal side of the body within the lung region with less radiation exposure compared to conventional techniques that detect adhesions from the mismatch between the phase related to diaphragmatic shape changes and diaphragmatic displacement and the respiratory phase. In addition, in the third embodiment, if adhesions are present, a decrease in the amount of movement is observed over a wide area of ​​the lung, making it easier for the user to intuitively determine the presence or absence of adhesions, resulting in improved image interpretation efficiency. Moreover, by generating and outputting information on pleural adhesions for each small region, the user can easily grasp the location and extent of potential adhesions.

[0117] <Fourth Embodiment> Next, a fourth embodiment of the present invention will be described. In the fourth embodiment, we will describe an example in which the adhesion information generation process A, adhesion information generation process B, and adhesion information generation process C described above are performed in sequence, and the presence or absence of adhesions is determined comprehensively based on the information on pleural adhesions generated in adhesion information generation processes A to C.

[0118] The configuration of the dynamic analysis system 100, imaging device 1, imaging console 2, and diagnostic console 3 in the fourth embodiment is the same as that described in the first embodiment, so we will refer to that description. Also, the operation of the imaging device 1 and imaging console 2 is the same as that described in the first embodiment, so we will refer to that description, and below we will describe the operation of the diagnostic console 3 in the fourth embodiment.

[0119] In the fourth embodiment, when the diagnostic console 3 receives a series of frame images of dynamic images of the chest from the imaging console 2 via the communication unit 35, the control unit 31 and the program stored in the storage unit 32 work together to execute the dynamic analysis process D shown in Figure 22. By executing the dynamic analysis process D, the control unit 31 functions as an acquisition unit, a generation unit, a second generation unit, a third generation unit, a determination unit, and a calculation unit.

[0120] In the dynamic analysis process D, the control unit 31 first executes the processes in steps S41 to S45. The processes in steps S41 to S45 are the same as those in steps S11 to S15 in Figure 3, so the explanation will be based on that.

[0121] Next, the control unit 31 executes adhesion information generation process A (step S46). Next, the control unit 31 executes adhesion information generation process B (step S47). Next, the control unit 31 executes the adhesion information generation process C (step S48).

[0122] Next, the control unit 31 determines whether or not there is a possibility of adhesion in the lung region of the dynamic image based on the information on pleural adhesion generated in adhesion information generation processes A to C (step S49). For example, if the control unit 31 determines that there is a possibility of adhesion in the lung region of the dynamic image if the information on pleural adhesion generated in at least one of the adhesion information generation processes A to C includes information indicating a decrease in the amount of motion. Furthermore, if you want to increase the sensitivity of adhesion detection, you may determine that there is a possibility of adhesion in the lung region if the information on pleural adhesion generated in at least one of the adhesion information generation processes A to C includes information indicating a decrease in the amount of movement. If you want to increase the specificity, you may determine that there is adhesion in the lung region if the information on pleural adhesion generated in at least two of the adhesion information generation processes A to C includes information indicating a decrease in the amount of movement.

[0123] Next, the control unit 31 calculates the accuracy (confidence level) of the determination in step S49 (step S50) based on the number of pieces of information in the pleural adhesion information generated in adhesion information generation processes A to C that indicate a decrease in the amount of movement within the lung region (within a small region). For example, in the pleural adhesion information generated by each of the adhesion information generation processes A to C, if the information regarding pleural adhesions in at least one small region includes information indicating a decrease in movement, a count of 1 is assigned, and the total number of counts for adhesion information generation processes A to C is calculated. Since adhesion information generation processes A and C are processes for different regions, there are no small regions where the amount of movement is judged to be reduced in both processes, and the maximum number of counts is 2. Then, the total number of calculated counts / maximum count (in this case, 2) is calculated as the confidence level of the judgment. That is, the confidence level of the judgment is 0, 1 / 2 = 50%, and 2 / 2 = 100%. Weights may be assigned to the counts for adhesion information generation processes A to C. For example, since adhesion information generation process C is likely to judge that the amount of movement is reduced over a wide area, its weight may be doubled compared to adhesion information generation processes A and B. Alternatively, for each sub-region, the number of sub-regions that contain information indicating a decrease in the amount of movement in the pleural adhesion information generated by adhesion information generation processes A to C can be counted (maximum 2), and the total count divided by the maximum count (here, 2) can be calculated as the accuracy of the determination for that sub-region (0, 1 / 2 = 50%, 2 / 2 = 100%). Furthermore, as described above, the counts from adhesion information generation processes A to C may be weighted.

[0124] The control unit 31 then outputs the determination result and probability of whether or not adhesion is possible (step S51). For example, the determination result determined in step S49 and the determination accuracy calculated in step S50 are displayed on the display unit 34. Information regarding pleural adhesions generated in adhesion information generation processes A to C may also be displayed.

[0125] As described above, in the fourth embodiment, the control unit 31 of the diagnostic console 3 determines and outputs the possibility of pleural adhesion based on the information on pleural adhesion generated by adhesion information generation processes A to C. Therefore, it becomes possible for the user to easily determine whether or not there is adhesion in the lung region.

[0126] The descriptions in the first to fourth embodiments above are preferred examples of the present invention and are not limited thereto.

[0127] For example, in the above embodiment, the case in which the display unit 34 is used as an output unit and information regarding pleural adhesions is displayed on the display unit 34 was described as an example. However, for example, the output unit may be a communication unit 35, and information regarding pleural adhesions may be output to an external device by the communication unit 35, and the external device may display, output, or print the information regarding pleural adhesions.

[0128] Furthermore, while the above description discloses examples using hard disks, semiconductor non-volatile memory, etc., as computer-readable media for the program according to the present invention, the invention is not limited to these examples. Portable recording media such as CD-ROMs can also be used as other computer-readable media. In addition, carrier waves can be used as a medium for providing the data of the program according to the present invention via a communication line.

[0129] Furthermore, the detailed configuration and operation of each device constituting the dynamic analysis system can also be modified as appropriate without departing from the spirit of the present invention. [Explanation of symbols]

[0130] 100 Dynamic Analysis System 1. Imaging device 11 Radiation source 12. Radiation irradiation control device 13. Radiation detection unit 14. Reading control device 2. Shooting console 21 Control Unit 22 Memory section 23 Control section 24 Display 25 Communications Department 26 bus 3. Diagnostic console 31 Control Unit 32 Storage section 33 Operation section 34 Display section 35 Communications Department 36 bus

Claims

1. An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesions based on the amount of movement of a region within the lung area in the dynamic image that includes at least a region adjacent to the thoracic cage, An output unit that outputs information regarding the adhesion of the pleura that has been generated, A dynamic image analysis device equipped with the following features.

2. The dynamic image analysis device according to claim 1, wherein the region adjacent to the thoracic cage is the region on the thoracic side contour of the lung region in the dynamic image.

3. The dynamic image analysis device according to claim 1 or 2, wherein the region adjacent to the thoracic cage represents the visceral pleura.

4. The dynamic image analysis device according to claim 3, wherein the generation unit compares the amount of movement of the region representing the visceral pleura with a region within the lung region that is different from the region representing the visceral pleura, and generates information indicating whether or not the amount of movement of the visceral pleura has decreased as information regarding pleural adhesion.

5. The dynamic image analysis device according to any one of claims 1 to 3, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the amount of movement of the small regions located on the contour of the lung region on the thoracic side is below a predetermined threshold, and generates information indicating whether the amount of movement of the small regions is below the predetermined threshold, or information indicating whether the amount of movement of the small regions is decreasing, as information regarding pleural adhesion, based on the determination result.

6. The dynamic image analysis device according to any one of claims 1 to 5, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the difference or ratio between the amount of movement of a small region located on the contour of the lung region on the thoracic side and the amount of movement of other small regions within a predetermined distance from the said small region is greater than or equal to a predetermined threshold, and generates information indicating whether the difference or ratio is greater than or equal to the predetermined threshold, or information indicating whether the amount of movement of the small region is decreasing, as information regarding pleural adhesion.

7. The dynamic image analysis device according to any one of claims 1 to 6, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the difference or ratio between the amount of movement of a small region located on the contour of the lung region on the thoracic side and the amount of movement of other small regions on a continuous shadow with the said small region is greater than or equal to a predetermined threshold, and generates information indicating whether the difference or ratio is greater than or equal to the predetermined threshold, or information indicating whether the amount of movement of the small region is decreasing, as information regarding pleural adhesion.

8. The dynamic image analysis device according to any one of claims 1 to 7, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the variation in the amount of movement within a region consisting of a small region located on the contour of the lung region on the thoracic side and other small regions on the shadow continuous with the small region is above a predetermined threshold, and generates information indicating whether the variation is above the predetermined threshold, or information indicating whether the amount of movement of the small region is decreasing, as information regarding adhesion of the pleura.

9. The motion amount is the amount of change in position in the motion image from a reference frame image, according to any one of claims 1 to 8.

10. The motion image analysis device according to any one of claims 1 to 8, wherein the amount of motion is the distance between the motion image and a small region on the thoracic cage in a reference frame image.

11. A second generation unit generates information regarding pleural adhesion based on the difference or ratio between the amount of movement of a first region within the lung region in the dynamic image and the amount of movement of a second region different from the first region. A third generation unit generates information regarding pleural adhesions based on the amount of movement in the region of the lung area in the dynamic image that does not include the region adjacent to the thoracic cage, The system includes a determination unit that determines whether or not there is a possibility of adhesion in the lung region of the dynamic image, based on information regarding pleural adhesion generated by the generation unit, information regarding pleural adhesion generated by the second generation unit, and information regarding pleural adhesion generated by the third generation unit. The dynamic image analysis apparatus according to any one of claims 1 to 10, wherein the output unit outputs the determination result by the determination unit.

12. The dynamic image analysis apparatus according to claim 11, wherein the determination unit determines that there is a possibility of adhesion in the lung region in the dynamic image when the information regarding pleural adhesion generated by at least one or more of the generation unit, the second generation unit, or the third generation unit includes information indicating that the amount of movement within the lung region is reduced.

13. The system includes a calculation unit that calculates the accuracy of the determination result by the determination unit based on the number of pieces of information indicating a decrease in the amount of movement within the lung region, which are included in the information on pleural adhesion generated by the generation unit, the second generation unit, and the third generation unit. The dynamic image analysis apparatus according to claim 11 or 12, wherein the output unit further outputs the accuracy of the determination result.

14. An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesion based on the difference or ratio between the amount of movement of a first region within the lung region in the dynamic image and the amount of movement of a second region different from the first region, An output unit that outputs information regarding the adhesion of the pleura that has been generated, A dynamic image analysis device equipped with the following features.

15. The dynamic image analysis apparatus according to claim 11, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, and generates the difference or ratio between the amount of movement of the small region of the lung region and the amount of movement of other small regions within a predetermined distance from the small region as information regarding pleural adhesion.

16. The dynamic image analysis device according to claim 14 or 15, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the difference or ratio between the amount of movement of the small region of the lung region and the amount of movement of other small regions within a predetermined distance from the small region is greater than or equal to a predetermined threshold, and generates information indicating whether the difference or ratio is greater than or equal to the predetermined threshold, or information indicating whether the amount of movement of the small region is decreasing, as information regarding pleural adhesion.

17. The dynamic image analysis apparatus according to any one of claims 14 to 16, wherein the generation unit divides the lung region in the dynamic image into a plurality of blocks in a predetermined direction to generate a plurality of blocks consisting of a plurality of sub-regions, and generates the difference or ratio between the amount of movement of the block and the amount of movement of the block adjacent to the block as information relating to the adhesion of the pleura.

18. The dynamic image analysis device according to any one of claims 14 to 17, wherein the generation unit divides the lung region in the dynamic image into a plurality of blocks in a predetermined direction, determines whether the difference or ratio between the amount of movement of the block and the amount of movement of the block adjacent to the block is greater than or equal to a predetermined threshold, and generates information indicating whether the difference or ratio is greater than or equal to the predetermined threshold, or information indicating whether the amount of movement of the block is decreasing, as information relating to pleural adhesion.

19. The dynamic image analysis apparatus according to any one of claims 14 to 18, wherein the generation unit divides the lung region in the dynamic image into a plurality of blocks in a predetermined direction, and generates information regarding pleural adhesion by determining the difference or variation in the ratio between the amount of movement of each of the plurality of blocks and the amount of movement of blocks adjacent to that block.

20. The dynamic image analysis device according to any one of claims 14 to 19, wherein the generation unit divides the lung region in the dynamic image into a plurality of blocks in a predetermined direction, determines whether the variation in the difference or ratio between the amount of movement of each of the plurality of blocks and the amount of movement of blocks adjacent to that block is greater than or equal to a predetermined threshold, and generates information indicating whether the variation is greater than or equal to the predetermined threshold, or information indicating whether the amount of movement of the lung region is decreasing, as information relating to pleural adhesion.

21. The motion image analysis apparatus according to any one of claims 17 to 20, wherein the amount of movement of the block is a representative value of the amount of movement within the block.

22. The dynamic image analysis device according to any one of claims 17 to 21, wherein the predetermined direction is the vertical direction of the lung region.

23. The dynamic image analysis device according to claim 18, wherein the predetermined threshold is changed based on the position of each block within the lung region.

24. The dynamic image analysis device according to any one of claims 14 to 23, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, and generates the difference or ratio between the amount of movement of each small region of the lung region and the amount of movement of a small region located at the same position as the small region in a lung that is on the left or right side of the lung in which the small region is located, as information regarding pleural adhesion.

25. An acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit that generates information regarding pleural adhesions based on the amount of movement in the region of the lung area in the dynamic image that does not include the region adjacent to the thoracic cage, An output unit that outputs information regarding the adhesion of the pleura that has been generated, A dynamic image analysis device equipped with the following features.

26. The dynamic image analysis apparatus according to claim 25, wherein the generation unit divides the lung region in the dynamic image into small regions consisting of one or more pixels, determines whether the amount of movement of the small regions that do not include the region adjacent to the rib cage within the lung region is below a predetermined threshold, and generates information indicating whether the amount of movement of the small regions is below the predetermined threshold, or information indicating whether the amount of movement of the small regions is decreasing, as information relating to pleural adhesion, based on the determination result.

27. The dynamic image analysis device according to any one of claims 1 to 26, wherein the dynamic image is a dynamic image taken while the subject is breathing.

28. Computers Acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit generates information regarding pleural adhesions based on the amount of movement in the lung region, including at least the region adjacent to the thoracic cage, in the aforementioned dynamic image. An output unit that outputs information regarding the adhesion of the pleura that has been generated. A program designed to function as such.

29. Computers Acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generating unit that generates information regarding pleural adhesions based on the difference or ratio between the amount of movement of a first region within the lung area in the dynamic image and the amount of movement of a second region different from the first region. An output unit that outputs information regarding the adhesion of the pleura that has been generated. A program designed to function as such.

30. Computers Acquisition unit that acquires dynamic images of the chest obtained by dynamic imaging using radiation, A generation unit generates information regarding pleural adhesions based on the amount of movement in the region of the lung area in the dynamic image that does not include the region adjacent to the thoracic cage. An output unit that outputs information regarding the adhesion of the pleura that has been generated. A program designed to function as such.

Images