Dynamic image analyzer and program

Abstract

To acquire information on pleural adhesion simply and accurately with little dosimetry.SOLUTION: According to a control part of a diagnostic console, a dynamic image of a chest acquired by kymography by a radiation is acquired, and information on pleural adhesion is generated based on a movement amount of an area containing at least an area adjacent to rib cage in a lung area in the acquired dynamic image, and the generated information on pleural adhesion is displayed and outputted by a display part.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to a dynamic image analysis device and a program. [Background technology]

[0002] As a technique for evaluating the presence and location of adhesions between biological tissues before surgery, for example, Patent Document 1 describes a technique for collecting still images at two time phases, during inspiration and expiration, using a CT scanner and evaluating pleural adhesions using the collected still images. Patent Document 2, for example, describes a technique for calculating three-dimensional motion vectors for pairs of physically close inner and outer voxels on the lung surface in 3D CT image data (4D data) collected over time, calculating smoothness, and extracting moving and non-moving areas on the lung contour based on the smoothness. Patent Document 3, for example, describes a technique for calculating the motion vectors of two structures that are physically and image-positionally close in ultrasound images and calculating the degree of adhesion between the two structures.

[0003] However, CT and 4D-CT scanners are difficult to introduce into general medical facilities due to their high cost, and the complicated imaging procedures and radiation exposure make them difficult to apply to general preoperative patients. Furthermore, ultrasound diagnostic devices only capture localized images, so they do not provide an overview of the entire subject, and attempting to capture the entire image requires a huge amount of imaging time. Furthermore, the imaging procedures are difficult. These same problems make them difficult to apply to general preoperative patients.

[0004] Known means for solving these problems include a technique for detecting adhesions based on changes in the shape of the diaphragm in dynamic images, as described in Patent Document 4, and a technique for detecting adhesions from a mismatch between the phase related to diaphragm displacement and the respiratory phase, as described in Patent Document 5. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-67832 [Patent Document 2] Japanese Patent Application Laid-Open No. 2019-180899 [Patent Document 3] Japanese Patent Application Publication No. 2019-88565 [Patent Document 4] International Publication No. 2014 / 185197 [Patent Document 5] Japanese Patent Application Laid-Open No. 2015-136566 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the techniques described in Patent Documents 4 and 5 were unable to detect adhesions that do not appear as changes in the shape or displacement of the diaphragm.

[0007] The present invention has been made in view of the above problems, and has an object to enable information regarding pleural adhesion to be obtained easily and accurately with a small amount of radiation exposure. [Means for solving the problem]

[0008] In order to solve the above problems, a dynamic image analysis device according to a first aspect of the present invention comprises: an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on a movement amount of a region including at least a region adjacent to the thorax in the lung region in the dynamic image; an output unit that outputs the generated information about pleural adhesion; Equipped with.

[0009] Furthermore, a dynamic image analysis device according to a second aspect of the present invention comprises: an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on a difference or ratio between a movement amount of a first region in the lung region in the dynamic image and a movement amount of a second region different from the first region; an output unit that outputs the generated information about pleural adhesion; Equipped with.

[0010] Furthermore, a dynamic image analysis device according to a third aspect of the present invention comprises: an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on the amount of movement of a region in the lung region in the dynamic image that does not include a region adjacent to the thorax; an output unit that outputs the generated information about pleural adhesion; Equipped with.

[0011] Furthermore, the program according to the fourth aspect of the present invention is Computer, an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on the amount of movement of a region including at least a region adjacent to the thorax within the lung region in the dynamic image; an output unit that outputs the generated information about pleural adhesion; Function as.

[0012] Furthermore, the program according to the fifth aspect of the present invention is Computer, an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on a difference or ratio between a movement amount of a first region in the lung region in the dynamic image and a movement amount of a second region different from the first region; an output unit that outputs the generated information about pleural adhesion; Function as.

[0013] Furthermore, the program according to the fifth aspect of the present invention is Computer, an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that generates information about pleural adhesion based on a movement amount of a region in the lung region in the dynamic image that does not include a region adjacent to the thorax; an output unit that outputs the generated information about pleural adhesion; Function as. [Effects of the Invention]

[0014] According to the present invention, it is possible to easily and accurately obtain information about pleural adhesions with a small amount of radiation exposure. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a diagram showing the overall configuration of a dynamic analysis system according to an embodiment of the present invention. [Figure 2] 2 is a flowchart showing an imaging control process executed by a control unit of the imaging console of FIG. 1. [Figure 3] 10 is a flowchart showing a dynamic analysis process A executed by the control unit of the diagnostic console of FIG. 1 in the first embodiment. [Figure 4] FIG. 10 is a diagram for explaining preprocessing. [Figure 5] FIG. 10 is a diagram illustrating merging of motion vectors. [Figure 6] FIG. 10 is a diagram showing an example of a continuous shadow in a small region. [Figure 7] 10 is a flowchart showing an example of adhesion information generation processing A (high sensitivity) executed in step S16 of FIG. [Figure 8] 10 is a flowchart showing an example of adhesion information generation processing A (high specificity) executed in step S16 of FIG. [Figure 9] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 10]FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 11] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 12] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 13] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 14] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the first embodiment. [Figure 15] 10 is a flowchart showing a dynamic analysis process B executed by the control unit of the diagnostic console of FIG. 1 in the second embodiment. [Figure 16] (a) is a graph plotting the amount of vertical movement (representative value) of each block when there is no adhesion, and (b) is a graph plotting the amount of vertical movement (representative value) of each block when there is adhesion. [Figure 17] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the second embodiment. [Figure 18] FIG. 10 is a diagram illustrating the amount of movement for each small region. [Figure 19] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the second embodiment. [Figure 20] FIG. 10 is a diagram showing an example of output of information relating to pleural adhesion in the second embodiment. [Figure 21] 10 is a flowchart showing a dynamic analysis process C executed by the control unit of the diagnostic console of FIG. 1 in the third embodiment. [Figure 22] 10 is a flowchart showing a dynamic analysis process D executed by the control unit of the diagnostic console of FIG. 1 in the fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings, but 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. FIG. 1 shows an example of the overall configuration of a dynamic analysis system 100 according to this embodiment. 1, the dynamic analysis system 100 is configured such that an imaging device 1 and an imaging console 2 are connected by a communication cable or the like, and the imaging console 2 and a 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 the devices is performed in accordance with DICOM.

[0018] [Configuration of the imaging device 1] The imaging device 1 is an imaging means for imaging periodic (cyclic) dynamics of the chest, such as changes in the shape of lung expansion and contraction due to breathing, heartbeat, etc. Dynamic imaging involves repeatedly irradiating a subject with pulsed radiation such as X-rays at predetermined time intervals (pulse imaging). By using a low dose rate and continuously irradiating the subject without interruption (continuous irradiation), This refers to the acquisition of multiple images that show the movement of the body. A series of images obtained by dynamic imaging is called a dynamic image. A dynamic image is a three-dimensional image that includes a time axis. Dynamic images include moving images, but do not include images obtained by taking still images while displaying a moving image. Each of the multiple images that make up a dynamic image is called a frame image. In the following embodiment, a case where dynamic chest imaging is performed on the front side by pulse irradiation will be described.

[0019] The radiation source 11 is disposed at a position facing the radiation detection unit 13 across the subject M, and irradiates the subject M with radiation (X-rays) under the control of the radiation irradiation control device 12. The radiation irradiation control device 12 is connected to the imaging console 2 and controls the radiation source 11 to perform radiation imaging based on radiation irradiation conditions input from the imaging console 2. The radiation irradiation conditions input from the imaging console 2 include, for example, a pulse rate, a pulse width, a pulse interval, the number of imaging frames per imaging, the value of the X-ray tube current, the value of the X-ray tube voltage, and the type of additional filter. The pulse rate is the number of radiation irradiations per second and corresponds to the frame rate described below. 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 corresponds to the frame interval described below.

[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 or the like, and a plurality of detection elements (pixels) are arranged in a matrix at predetermined positions on the substrate. The detection elements detect radiation emitted from the radiation source 11 and transmitted through at least the subject M according to its intensity, and convert the detected radiation into an electrical signal and store it. Each pixel is equipped with a switching unit such as a TFT (Thin Film Transistor). FPDs can be of an indirect conversion type, in which X-rays are converted into an electrical signal by a photoelectric conversion element via a scintillator, or a direct conversion type, in which X-rays are directly converted into an electrical signal, and either type may be used. The radiation detection unit 13 is disposed opposite the radiation source 11 with the subject M interposed therebetween.

[0021] The reading control device 14 is connected to the radiography console 2. The reading control device 14 controls the switching units of each pixel of the radiation detection unit 13 based on the image reading conditions input from the radiography console 2, switches 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 radiography console 2. The image reading conditions include, for example, the frame rate, frame interval, pixel size, image size (matrix size), etc. The frame rate is the number of frame images acquired per second and coincides with the pulse rate. The frame interval is the time from the start of acquisition of one frame image to the start of acquisition of the next frame image and coincides with the pulse interval.

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

[0023] [Configuration of imaging console 2] The imaging console 2 outputs radiation irradiation conditions and image reading conditions to the imaging device 1 to control the radiation imaging and radiation image reading operations by the imaging device 1, and also displays dynamic images acquired by the imaging device 1 so that the imaging technician or other person performing the imaging can check the positioning and whether the images are suitable for diagnosis. As shown in FIG. 1, the radiography console 2 comprises 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 .

[0024] The control unit 21 is composed of a CPU (Central Processing Unit), RAM (Random Access Memory), etc. In response to an operation of the operation unit 23, the CPU of the control unit 21 reads out a system program and various processing programs stored in the storage unit 22 and loads them into the RAM, and executes various processes including an imaging control process described below in accordance with the loaded programs, thereby centrally controlling the operation of each unit of the imaging console 2 and the radiation irradiation operation and reading operation of the imaging device 1.

[0025] The storage unit 22 is configured with a non-volatile semiconductor memory, a hard disk, etc. The storage unit 22 stores various programs executed by the control unit 21, parameters required for executing processes by the programs, data such as processing results, etc. For example, the storage unit 22 stores a program for executing the imaging control process shown in FIG. 2. The storage unit 22 also stores radiation irradiation conditions and image reading conditions in association with the region to be examined and the imaging direction. The various programs are stored in the form of readable program code, and the control unit 21 sequentially executes operations in accordance with the program code.

[0026] The operation unit 23 is configured with a keyboard having cursor keys, numeric input keys, various function keys, etc., and a pointing device such as a mouse, and outputs instruction signals input by operating the keys on the keyboard or the mouse to the control unit 21. The operation unit 23 may also have a touch panel on the display screen of the display unit 24, and in this case, outputs instruction signals input via the touch panel to the control unit 21.

[0027] The display unit 24 is composed of a monitor such as an LCD (Liquid Crystal Display) or CRT (Cathode Ray Tube), and displays input instructions and data from the operation unit 23 according to instructions of a display signal input from the control unit 21.

[0028] The communication unit 25 includes a LAN adapter, a modem, a TA (Terminal Adapter), etc., and controls data transmission and reception between 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 radiography console 2, and generates and outputs information relating to pleural adhesion based on the acquired dynamic images. As shown in FIG. 1, the diagnostic console 3 comprises a control unit 31, a storage unit 32, an operation unit 33, a display unit , and a communication unit , and each unit is connected by a bus .

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

[0031] The storage unit 32 is configured with a non-volatile semiconductor memory, a hard disk, etc. The storage unit 32 stores various programs, including a program for executing the dynamic analysis process A in the control unit 31, parameters required for executing the processes by the programs, data such as processing results, etc. These various programs are stored in the form of readable program code, and the control unit 31 sequentially executes operations in accordance with the program code.

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

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

[0034] The communication unit 35 includes a LAN adapter, a modem, a TA, etc., and controls data transmission and reception between 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, the imaging operation performed by the imaging device 1 and the imaging console 2 will be described. 2 shows an imaging control process executed in the control unit 21 of the imaging console 2. The imaging control process is executed by the control unit 21 in cooperation with a program stored in the storage unit 22.

[0037] First, the control unit 21 receives input of patient information and examination information of the subject (subject M) through operation of the operation unit 23 by the person performing imaging (step S1).

[0038] Next, the control unit 21 reads out the radiation irradiation conditions from the storage unit 22 and sets them in the radiation irradiation control device 12, and also reads out 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 radiation via operation of the operation unit 23 (step S3). Here, the person performing the imaging performs positioning by placing the subject M between the radiation source 11 and the radiation detection unit 13. When preparations for imaging are complete, the person operates the operation unit 23 to input an instruction to irradiate radiation.

[0040] When a radiation irradiation instruction is input via the operation unit 23 (step S3; YES), the control unit 21 outputs an imaging start instruction to the radiation irradiation control device 12 and the reading control device 14, and starts dynamic imaging (step S4). That is, radiation is emitted from the radiation source 11 at the pulse interval set in the radiation irradiation control device 12, and frame images are acquired by the radiation detection unit 13. During dynamic imaging, the imaging implementer performs breathing guidance, such as "take a breath in" and "take a breath out," and images of the chest are taken while the patient is breathing. Note that the imaging device 1 may be equipped with an audio output unit and a display unit, and when an imaging start instruction is output, breathing guidance such as "take a breath in" and "take a breath out" may be audibly or displayed.

[0041] When an instruction to end radiation irradiation is input by the operation unit 23, the control unit 21 outputs an instruction to end imaging to the radiation irradiation control device 12 and the reading control device 14, and stops the imaging operation.

[0042] The frame images acquired by imaging are input sequentially to the imaging console 2, and the control unit 21 associates the input frame images with numbers (frame numbers) indicating the imaging order, stores them in the memory unit 22 (step S5), and displays them on the display unit 24 (step S6). The imaging practitioner checks the positioning, etc., based on the displayed dynamic images, and determines whether an image suitable for diagnosis has been acquired by imaging (imaging OK) or whether reimaging is necessary (imaging NG). Then, the operator operates the operation unit 23 to input the determination result.

[0043] When a determination result indicating that imaging is OK is input by 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 imaging order (frame number) to each of the series of frame images acquired by dynamic imaging (for example, writes 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, the imaging control process is terminated. On the other hand, when a determination result indicating that imaging is NG is input by a predetermined operation of the operation unit 23 (step S7; NO), the control unit 21 deletes the series of frame images stored in the memory unit 22 (step S9), and terminates the imaging control process. In this case, re-imaging is required.

[0044] (Diagnostic console 3 operation) Next, the operation of the diagnostic console 3 will be described. In the diagnostic console 3, when a series of frame images of dynamic images of the chest are received from the radiography console 2 via the communication unit 35, the control unit 31 cooperates with the program stored in the memory unit 32 to execute the dynamic analysis process A shown in Figure 3.

[0045] The rib cage and diaphragm are soft containers for the lungs, and when we want to breathe, by moving the rib cage and diaphragm, the lungs expand and contract due to changes in internal pressure, allowing air to go in and out. 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 thoracic cage and lungs are separate, but when inflammation or other factors cause adhesions between the parietal pleura, the membrane inside the thoracic cage, and the visceral pleura that surrounds the lungs (pleural adhesions), the thoracic cage and lungs become tightly attached at that point.As a result, the amount of movement of the visceral pleura in the area of ​​adhesion is reduced compared to the amount of movement in the surrounding area. Therefore, in dynamic analysis process A, dynamic images of the chest are analyzed, and information regarding pleural adhesions is generated and output based on the amount of movement of an area in the lung region in the dynamic image that includes at least an area adjacent to the thorax (the thorax on the side of the body).

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

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

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

[0049] Next, as shown in FIG. 4, the control unit 31 performs bone suppression processing (BS processing) on ​​each acquired frame image (original image) to recognize bone regions and attenuate bone signal components, thereby generating a bone suppression image (BS image), and then performs frequency emphasis processing on the generated BS image to obtain a frequency emphasis image. Each frame image of a dynamic chest image shows not only the lungs but also various other structures, such as ribs, in a single image. Therefore, if corresponding points of the pattern on the image are simply calculated to extract the lung motion vector, they will be mixed with corresponding points of bones, which move differently from the lungs. Therefore, by performing bone attenuation processing on the original image, corresponding points in the lung region can be calculated with high accuracy in subsequent processing. Furthermore, the pattern of the lung region visible in the dynamic image is mainly pulmonary blood vessels, which are composed of high-frequency components, while extrapulmonary organs, fat, and muscle are characterized by low-frequency components. Therefore, it is desirable to perform frequency emphasis 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 preprocessed 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 inspiration frame, referred to as frame 1) is divided into multiple small regions, and for each small region, corresponding points are found between adjacent frame images sequentially using dense optical flow to calculate a motion vector. The small regions may be pixels, or may be pixel blocks (e.g., 5 mm x 5 mm) consisting of multiple pixels. In the case of pixel blocks, the motion vector of the center of the small region is calculated, for example. In this embodiment, a case where the small region is a pixel block of 5 mm x 5 mm is described. Note that while the motion vector is calculated here between adjacent frame images, it is also possible to calculate a motion vector with a frame image n frames later (n is a positive integer). Furthermore, to reduce motion measurement errors due to cardiac motion, n may be the number of frames in one cardiac cycle. Note that the motion vector calculation needs to be performed for at least each small region of the lung region. The amount of motion in each small region of the lung region of the dynamic image is based on the respiratory rate.

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

[0052] FIG. 5 is a diagram illustrating the merging process of step S14. Here, motion vectors from the start frame to the end frame are calculated. As shown in FIG. 5, in step S14, first, the sum (indicated by the thick arrow in FIG. 5) of the motion vector obtained in step S13 from the start frame image (frame 1) and the start frame adjacent frame image (frame 1+n) adjacent to the start frame image, and the motion vector obtained from the start frame adjacent frame image (frame 1+n) and the next adjacent frame image to the start frame (frame 2+n) is calculated. Next, the sum of the calculated motion vectors and the sum of the next motion vector is calculated. This is repeated until all of the calculated motion vectors have been added. This allows a motion vector representing the movement from the start frame image to the end frame image of the analysis target to be calculated. This motion vector is saved in the vector start coordinates or the vector end coordinates. For example, if the reference frame image described below is a frame image at the maximum inspiration position, the motion vector can be saved in the vector start coordinates.

[0053] The above-described motion vector calculation method is merely an example, and the method is not particularly limited as long as it can ultimately calculate a motion vector for each small region from the start frame image to the end frame image of the exhalation period. However, during a deep breathing period (approximately 5 seconds), the lungs undergo significant positional movement and deformation due to breathing, resulting in drastic changes in the appearance of the image, making it extremely 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 calculating motion vectors and merging them, it is possible to accurately calculate the motion vector for the exhalation period. Furthermore, various filtering processes, such as a Gaussian filter, may be performed as post-processing on 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 small region based on the calculated motion vector, and creates a motion amount map indicating the amount of motion for each small region (step S15). When creating the motion amount map, the motion vectors for each small region outside the lung region may be excluded as non-targets. The lung region (lung field region) can be extracted using a known method, such as the edge detection method described in JP 2018-148964 A.

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

[0056] In adhesion information generation processing A in step S13, the control unit 31 generates information about pleural adhesion by using any one of the following first to fourth methods or a combination of a plurality of methods.

[0057] (1st method) When there is pleural adhesion, the movement of the adhesion area (small area) in the visceral pleura is reduced. Therefore, in the first method, the control unit 31 determines whether the amount of motion of each small region located on the thoracic-side contour of the lung region in the dynamic image is equal to or less than a predetermined threshold, and if it determines that the amount of motion is equal to or less than the predetermined threshold, stores information indicating that the amount of motion of the small region is equal to or less than the predetermined threshold or information indicating that the amount of motion of the small region has decreased in association with the small region as information regarding pleural adhesion in RAM, etc. If it determines that the amount of motion exceeds the predetermined threshold, stores information indicating that the amount of motion of the small region is not equal to or less than the predetermined threshold (exceeds the predetermined threshold) or information indicating that the amount of motion of the small region has not decreased in association with the small region as information regarding pleural adhesion in RAM, etc. The predetermined threshold value to be compared with the amount of movement of the small region is a value verified through clinical experiments.

[0058] (Second method) When there is pleural adhesion, the movement of the part (small area) where there is adhesion in the visceral pleura becomes small, and the difference in the amount of movement between the adhesion and the surrounding area becomes larger compared to the part without adhesion. Therefore, in the second and third methods, the control unit 31 determines whether the amount of movement of the area representing the visceral pleura in the dynamic image has decreased by comparing the amount of movement of an area other than the visceral pleura in the lung area, and generates the determination result as information regarding pleural adhesion.

[0059] In the second method, for each small region representing the visceral pleura in the dynamic image, i.e., each small region located on the thoracic-side contour of the lung region in the dynamic image, the control unit 31 calculates the difference (or ratio; the same applies hereinafter in this embodiment) between the amount of movement of that small region and the amount of movement of a small region surrounding that small region within the lung region (for example, a small region within the lung region whose distance from that small region is within a predetermined threshold (for example, a small region within 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 the predetermined threshold. If the control unit 31 determines that the calculated difference is greater than or equal to the predetermined threshold, it 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 small region has decreased in RAM or the like, as information regarding pleural adhesion in that small region. If the control unit 31 determines that the calculated difference is less than the predetermined threshold, it 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 small region has not decreased in RAM or the like, as information regarding pleural adhesion in that small region. The predetermined threshold value to be compared with the difference is a value verified through clinical experiments. The amount of movement of the surrounding small regions is, for example, a representative value (average value, median value, maximum value, etc.; the same applies below) of the amount of movement of the surrounding small regions.

[0060] (Third method) In the third method, the control unit 31 first performs a process for detecting a shadow contiguous to each small region located on the thoracic-side contour of the lung region in the dynamic image. Here, a shadow contiguous to a small region located on the thoracic-side contour of the lung region refers to, for example, a funicular shadow as indicated by an arrow in the enlarged view of the lung region in FIG. 6. FIG. 6 shows a shadow contiguous to a small region R on the lung contour. A shadow contiguous to a small region can be detected, for example, by using a small region located on the thoracic-side contour of the lung region as a base point, determining a region whose signal value differs from that small region by a region growing method or the like within a predetermined threshold, and then extracting a region of 2 mm to 3 mm in thickness that is contiguous to the small region from the determined region. When a shadow contiguous to that small region within the lung region is detected, the control unit 31 calculates the difference between the amount of motion of that small region and the amount of motion of other small regions within the lung region that are contiguous to that small region, and determines whether the calculated difference is equal to or greater than a predetermined threshold. If the control unit 31 determines that the calculated difference is equal to or greater than a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is equal to or greater than the predetermined threshold or information indicating that the amount of movement in the small region has decreased as information about pleural adhesion in the small region in RAM, etc. If the control unit 31 determines that the calculated difference is below the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not equal to or greater than the predetermined threshold or information indicating that the amount of movement in the small region has not decreased as information about pleural adhesion in the small region in RAM, etc. The predetermined threshold value used for comparing the difference is a value verified through clinical experiments. The amount of movement of other small regions in the shadow that is adjacent to the small region is, for example, a representative value of the amount of movement of other small regions in the shadow that is adjacent to the small region.

[0061] (4th method) When pleural adhesions are present, the movement of the small region of the visceral pleura where the adhesions are present is reduced, resulting in a difference in the amount of movement between that small region and other small regions in the shadows adjacent to that small region. This results in a large variation in the amount of movement within the region consisting of that small region and other small regions in the shadows adjacent to that small region. Therefore, the fourth method generates information about pleural adhesions based on this variation. For example, for each small region located on the thoracic-side contour of the lung region in the dynamic image, the control unit 31 performs a process to detect a shadow contiguous to the small region within the lung region. If a shadow is detected, the control unit 31 calculates, for example, a standard deviation or variance as the variation in the amount of motion within the region consisting of the small region and other small regions located on the shadow contiguous to the small region. The control unit 31 then determines whether the calculated variation is equal to or greater than a predetermined threshold. If the control unit 31 determines that the calculated variation is equal to or greater than the predetermined threshold, the control unit 31 stores information indicating that the calculated variation is equal to or greater than the predetermined threshold or information indicating that the amount of motion in the small region has decreased as information regarding pleural adhesion in the small region, in association with the small region, in RAM, etc. If the control unit 31 determines that the calculated variation is below the predetermined threshold, the control unit 31 stores information indicating that the calculated variation is not equal to or greater than the predetermined threshold or information indicating that the amount of motion in the small region has not decreased as information regarding pleural adhesion in the small region, in association with the small region, in RAM, etc. The predetermined threshold value to be compared with the above-mentioned variation is a value verified through clinical experiments. The method for detecting continuous shadows in a small area can be, for example, the method described in the third method.

[0062] (A combination of two or more of the first to fourth methods) As described above, information about pleural adhesions may be generated using any of the first to fourth methods, or multiple methods may be combined to generate information about pleural adhesions. The method or combination of methods to be used to generate information about pleural adhesions may be predetermined, or may be set by the user using the operation unit 33. Alternatively, information about pleural adhesions may be generated using a method according to needs set by the user using the operation unit 33, such as increasing detection sensitivity (to avoid missing people with adhesions) or increasing specificity (to carefully detect people with adhesions). Below, an example of generating information about pleural adhesions by combining multiple methods according to needs set by the user will be described.

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

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

[0065] If it is determined that the selected small region is located on the contour of the lung region on the thoracic cage side (step S162; YES), the control unit 31 determines whether the amount of movement of the small region is equal to or less than a predetermined threshold value (step S163). If it is determined that the amount of movement of the selected small area is below a predetermined threshold (step S163; YES), the control unit 31 determines that the movement of the selected small area has decreased, and stores information indicating that the movement of the small area has decreased in RAM or the like in association with the small area as information regarding pleural adhesion in the small area (step S168), and proceeds to step S170.

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

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

[0068] In step S170, the control unit 31 determines whether or not the processes of steps S161 to S169 have been completed for all small regions (step S170). If it is determined that the processing of steps S161 to S169 has not been completed for all small regions (step S170; NO), the control unit 31 returns to step S161, selects a small region that has not yet been processed, and executes the processing of steps S161 to S169. If it is determined that the processing of steps S161 to S169 has been completed for all small regions (step S170; YES), the control unit 31 ends the adhesion information generation processing A (high sensitivity).

[0069] 7, in step S163, a determination is made using the first method described above, and if the determination result is YES, it is determined that the amount of movement of the small region has decreased and information about pleural adhesion indicating this is generated, whereas if the determination result is NO, a determination is made using the third method described above, and if the determination result is YES, it is determined that the amount of movement of the selected small region has decreased and information about pleural adhesion indicating this is generated, whereas if the determination result is NO, it is determined that the amount of movement of the selected small region has not decreased and information about pleural adhesion indicating this is generated. Thus, the sensitivity of adhesions can be increased compared to generating information about pleural adhesions using only the first method or only the third method.

[0070] 7, the second method may be used instead of the first method, and the fourth method may be used instead of the third method. Furthermore, it is also possible to perform all of the determinations of the first to fourth methods, and if the determination results of all the methods are NO, to determine that the amount of movement of the selected small region has not decreased, and if there is even one determination result of YES, to determine that the amount of movement of the selected small region has decreased, and generate the determination result as information related to pleural adhesion.

[0071] 8 is a flowchart showing the flow of collusion information generation process A (high specificity) that is performed when the user's need is set to "want to increase specificity." Collusion information generation process A (high specificity) is executed by cooperation between the control unit 31 and a program stored in the storage unit 32.

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

[0073] If it is determined that the selected small region is located on the contour of the lung region on the thoracic cage side (step S182; YES), the control unit 31 determines whether the amount of movement of the small region is equal to or less than a predetermined threshold value (step S183). If it is determined that the amount of movement of the selected small area exceeds a predetermined threshold (step S183; NO), the control unit 31 determines that the movement of the selected small area has not decreased, and stores information indicating that the movement of the small area has not decreased in RAM or the like in association with the small area as information regarding pleural adhesion in the small area (step S189), and proceeds to step S190.

[0074] If it is determined that the amount of motion of the selected small region is equal to or less than a predetermined threshold (step S183; YES), the control unit 31 detects a shadow that is 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 shadow continuous with the selected small region has been detected within the lung region of the dynamic image (step S185). If it is determined that no shadow continuous with the selected small area has been detected within the lung area of ​​the dynamic image (step S185; NO), the control unit 31 determines that the amount of movement of the selected small area has not decreased, and stores information indicating that the amount of movement of the small area has not decreased in RAM or the like, as information regarding pleural adhesion in the small area, in association with the small area (step S189), and proceeds to step S190.

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

[0076] In step S190, the control unit 31 determines whether or not the processes of steps S181 to S189 have been completed for all small regions (step S190). If it is determined that the processing of steps S181 to S189 has not been completed for all small regions (step S190; NO), the control unit 31 returns to step S181, selects a small region that has not yet been processed, and executes the processing of S181 to S189. If it is determined that the processing of steps S181 to S189 has been completed for all small regions (step S190; YES), the control unit 31 ends the adhesion information generation processing A (high specificity) shown in FIG.

[0077] In adhesion information generation process A (high specificity) shown in Fig. 8, in step S183, a determination is made using the first method described above. If the determination result is YES, a determination is made using the third method described above. If the determination result using the third method is YES, it is determined that the amount of motion of the selected small region has decreased, and information on pleural adhesion indicating this is generated. If the determination result using the first method is NO, or if the determination result using the first method is YES but the determination result using the third method is NO, it is determined that the amount of motion of the selected small region has not decreased, and information on pleural adhesion indicating this is generated. Therefore, it is possible to more carefully determine whether the amount of motion has decreased than when generating information on pleural adhesion using only the first method or only the third method, and the specificity of adhesions can be improved. 8, the second method may be used instead of the first method, and the fourth method may be used instead of the third method. Furthermore, it is also possible to perform all of the determinations of the first to fourth methods, and if the determination results of all the methods are YES, determine that the amount of movement of the selected small region has decreased, and if there is even one determination result of NO, determine that the amount of movement of the selected small region has not decreased, and generate the determination results as information related to pleural adhesion.

[0078] Returning to FIG. 3, when the adhesion information generation process A in step S16 is completed, the control unit 31 outputs the generated information on pleural adhesion (step S17). The generated information regarding pleural adhesions may be output as characters or numbers, or may be output by adding a color to the image corresponding to the characters or numbers.

[0079] For example, when information about pleural adhesion is generated by the first method in step S16, the control unit 31 maps motion vectors to each small region on the reference frame image (e.g., a frame image at the maximum inspiration position), as shown in FIG. 9, and displays the motion vector of a small region (reduced motion region) whose amount of motion is determined to be equal to or less than a predetermined threshold on the display unit 34 in a color different from that of the motion vectors of other small regions. As shown in FIG. 9, the small region or the motion vector may be further highlighted by an annotation or the like. Alternatively, as shown in FIG. 10, each small region on the reference frame image may be assigned a color according to the amount of motion and displayed on the display unit 34. As shown in FIG. 10, the small region (reduced motion region) whose amount of motion is determined to be equal to or less than a predetermined threshold may be further highlighted by an annotation or the like. Alternatively, as shown in FIG. 11, the small region (reduced motion region) on the reference frame image whose amount of motion is determined to be equal to or less than a predetermined threshold may be assigned a predetermined color or marker and displayed on the display unit 34. This makes it possible to highlight the region where the amount of motion of the visceral pleura is reduced, making it easier for the user to understand.

[0080] Furthermore, for example, when information about pleural adhesion is generated by the second method in step S16, the control unit 31 applies a predetermined color or marker to small regions (regions with reduced movement) on the reference frame image that are determined to have a difference in movement amount from surrounding small regions equal to or greater than a predetermined threshold, and displays the small regions on the display unit 34, as shown in Fig. 11. This makes it possible to highlight the regions where the movement amount of the visceral pleura is reduced and clearly show them to the user.

[0081] Furthermore, for example, when information regarding pleural adhesion is generated using the third method in step S16, the control unit 31 maps motion vectors to a small region (referred to as a region of interest) located on the thoracic-side contour of the lung region on the reference frame image, where the difference between the amount of movement of the small region and the amount of movement of a small region in the shadow contiguous to the small region is determined to be equal to or greater than a predetermined threshold, and to other small regions in the shadow contiguous to the small region of interest, and displays these on the display unit 34. The motion vector of the region of interest is displayed on the display unit 34 in a color different from that of the other small regions in the shadow. This allows the area where the amount of movement of the visceral pleura is reduced to be emphasized and clearly indicated to the user. Furthermore, the motion vectors of the region of interest and other small regions in the shadow contiguous to the region of interest can be compared. Alternatively, as shown in FIG. 11, the position of the region of interest on the reference frame image may be displayed on the display unit 34 with a predetermined color or marker. Furthermore, as shown in FIG. 13, the region of interest and other small regions in the shadow contiguous to the region of interest may be displayed with a marker or the like.

[0082] Furthermore, for example, if information regarding pleural adhesion is generated using the fourth method in step S16, the control unit 31 maps motion vectors to a small region (referred to as a region of interest) located on the thoracic-side contour of the lung region on the reference frame image, where the variation in the amount of motion within the region consisting of the small region and other small regions on the contiguous shadows is determined to be equal to or greater than a predetermined threshold, as well as to other small regions on the contiguous shadows of the small region of interest, and displays the mapping results on the display unit 34. Also, as shown in FIG. 14, lines connecting the start points and end points of the motion vectors of the small regions on the contiguous shadows (including the small regions on the contours) are displayed. This allows the user to easily see the areas where the amount of motion of the visceral pleura is reduced by emphasizing them. Furthermore, the user can check the degree of variation in the amount of motion within the shadows.

[0083] Furthermore, for example, in step S16, when information about pleural adhesions is generated using a plurality of techniques, namely, the first to fourth techniques, the control unit 31 applies a predetermined color or marker to small regions determined to have reduced movement, and displays them on the display unit 34, as shown in Fig. 11. This makes it possible to highlight the regions where the movement of the visceral pleura is reduced, making it easier for the user to understand. When the process of step S17 ends, the control unit 31 ends the dynamic analysis process A.

[0084] In the above explanation, the amount of movement of a small area in the lung area has been explained as the amount of change (absolute amount) in the position of the small area from a reference frame image (for example, a frame image at the maximum inspiration position), but it may also be the distance (relative amount) from the small area on the thorax in the reference frame image. When the amount of movement is a relative amount, for example, after the preprocessing in step S12 in Fig. 3, optical flow is executed to find corresponding points between adjacent frame images for each small area in the lung area, and the distance between the position of the small area in each frame image and the position of a small area located on the thorax in the reference frame image (for example, a small area outside the lung area adjacent to a small area located on the thorax-side contour of the lung area) is taken as the amount of movement.

[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 regarding pleural adhesions based on the amount of movement of an area in the lung region in the acquired dynamic image that includes at least an area adjacent to the thorax, and displays the generated information regarding pleural adhesions on the display unit 34. Therefore, because 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 low radiation exposure, without using conventional 4D-CT scans, which are difficult to introduce into general medical facilities due to the cost of the equipment and have problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow an overview of the subject as a whole because it captures localized images, requires enormous imaging time when attempting to capture the entire body, and has difficult imaging techniques.As a result, it becomes possible to easily obtain information about pleural adhesions with low radiation exposure, without introducing large, expensive equipment, in general medical facilities. In addition, the system focuses on the region adjacent to the thorax (the region representing the visceral pleura), which is the region in the lung field where pleural adhesions occur in dynamic images, and generates information about pleural adhesions based on the amount of movement in the region including the region adjacent to the thorax. This allows for more accurate acquisition of information about pleural adhesions than conventional techniques that detect adhesions based on changes in diaphragm shape or mismatches between the phase related to diaphragm displacement and the respiratory phase. Furthermore, by generating and outputting information about pleural adhesions for each small region, users can easily grasp the location and extent of potential adhesions. Furthermore, because the thorax area is located at the periphery of the lungs and therefore has fewer vascular shadows than the central lung area, funicular shadows are easier to recognize. However, because the area around the thorax is located at the periphery of the lungs and therefore has fewer vascular shadows than the central lung area, funicular shadows are easier to recognize, the amount of movement can be calculated with high accuracy, reducing the chance of overlooking adhesions.

[0086] <Second embodiment> Next, a second embodiment of the present invention will be described. In the second embodiment, an example will be described in which information regarding pleural adhesion is generated based on the difference (or ratio; the same applies to the following embodiments) 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 configurations of the dynamic analysis system 100, the imaging device 1, the imaging console 2, and the diagnostic console 3 in the second embodiment are the same as those described in the first embodiment, and therefore the same explanations are used. In addition, the operations of the imaging device 1 and the imaging console 2 are also the same as those described in the first embodiment, and therefore the same explanations are used. Below, the operation of the diagnostic console 3 in the second embodiment will be described.

[0088] In the second embodiment, when the diagnostic console 3 receives a series of frame images of dynamic chest images from the radiography console 2 via the communication unit 35, the diagnostic console 3 executes dynamic analysis processing B shown in Fig. 15 in cooperation with the control unit 31 and a program stored in the storage unit 32. By executing dynamic analysis processing 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 of steps S21 to S25. The processes of steps S21 to S25 are similar to steps S11 to S15 in Fig. 3, and therefore the same explanation will be used.

[0090] Next, the control unit 31 executes adhesion information generation processing B (step S26). In adhesion information generation processing B in 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 in the lung region and the motion amount of a second region around it. Specifically, the information about pleural adhesion is generated by any one of the following first to fourth methods.

[0091] (1st method) In the first method, the control unit 31 calculates the difference between the amount of motion of each small region (first region) within the lung region in the dynamic image and the amount of motion of the small regions surrounding that small region (second region; in this case, small regions within a predetermined distance from the first region (e.g., within a 30 mm radius from the center of the first region)), and determines whether the calculated difference is equal to or greater than a predetermined threshold. If the control unit 31 determines that the calculated difference is equal to or greater than the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is equal to or greater than the predetermined threshold or information indicating that the amount of motion of the small region has decreased in RAM or the like, as information regarding pleural adhesion in the small region. If the control unit 31 determines that the calculated difference is below the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not equal to or greater than the predetermined threshold or information indicating that the amount of motion of the small region has not decreased in RAM or the like, as information regarding pleural adhesion in the small region. Here, the surrounding small regions for which the difference is calculated may be only those small regions adjacent in the vertical direction (up and down of the lung region), only those small regions adjacent in the horizontal direction (left and right of the lung region), or those small regions adjacent in both directions.If there are multiple surrounding small regions, the amount of movement of the surrounding small regions is a representative value of the amount of movement of the multiple surrounding small regions.In addition, the predetermined threshold value for comparing the calculated difference is a value verified in clinical experiments.

[0092] (Second method) In the second method, the control unit 31 divides the lung area in the dynamic image into multiple blocks in a predetermined direction (for example, the up-down direction, the left-right direction, or the up-down and left-right directions of the lung area), and generates information regarding pleural adhesion based on the difference between the amount of movement of each block (first area) and the amount of movement of the block adjacent to that block (second area), 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 motion of each small region included in each block as the amount of motion of each block, and calculates a representative value of the amount of motion of each small region included in a block adjacent to that block as the amount of motion of the adjacent block. Then, the control unit 31 calculates the difference between the representative value of the amount of motion of each small region included in each block and the representative value of the amount of motion of each small region included in the block adjacent to that block. If the control unit 31 determines that the calculated difference is equal to or greater than a predetermined threshold, the control unit 31 stores information indicating that the calculated difference is equal to or greater than the predetermined threshold or information indicating that the amount of motion of that block (first region) has decreased as information regarding pleural adhesion in the block in RAM or the like, in association with the block. If the control unit 31 determines that the calculated difference is less than the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not equal to or greater than the predetermined threshold or information indicating that the amount of motion of that block (first region) has not decreased as information regarding pleural adhesion in the block in RAM or the like, in association with the block. The predetermined threshold is a value determined through clinical experiments.

[0093] The adjacent blocks for which the difference is to be calculated may be vertically adjacent blocks, horizontally adjacent blocks, or both vertically and horizontally adjacent blocks. The difference in the amount of motion between an adjacent block is a representative value of the differences in the amount of motion calculated between each of the adjacent blocks for which the difference is to be calculated. When calculating the difference in the amount of motion between vertically and horizontally adjacent blocks, the vertical difference and the horizontal difference may be calculated separately or may be combined.

[0094] FIG. 16(a) is an example of a graph plotting the amount of motion (representative value of the amount of motion within each block) of blocks obtained by dividing a lung region without areas of reduced motion due to adhesions vertically at regular intervals in a space with the horizontal axis representing the amount of motion and the vertical axis representing the vertical position in the lung region. FIG. 16(b) is an example of a graph plotting the amount of motion (representative value of the amount of motion within each block) of blocks obtained by dividing a lung region with adhesions vertically in a space with the horizontal axis representing the amount of motion and the vertical axis representing the vertical position in the lung region. As shown in FIG. 16(a), when there is no adhesion, the amount of motion gradually increases as the block position decreases. On the other hand, when there is adhesion, the difference in the amount of motion between adjacent blocks increases at the adhesion locations (locations indicated by arrows) as shown in FIG. 16(b). In this way, the presence or absence of an area with reduced motion due to adhesion can be determined based on the magnitude of the difference in the amount of motion between adjacent blocks.

[0095] In the second method, it is preferable to divide the lung region in a certain direction, specifically, in the vertical direction of the lung region. This is because lung movement is mainly due to movement of the diaphragm, and therefore dividing the lung region in the vertical direction makes it easier to represent lung movement. Furthermore, it is preferable to change the predetermined threshold for comparing the difference between the amount of movement of each block and the amount of movement of its adjacent block according to the 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 small threshold for the upper lung field and a large threshold for the lower lung field.

[0096] (Third method) In the third method, the control unit 31 calculates the variance (standard deviation or variance) of the difference between the amount of motion of each block (a representative value of the amount of motion of a small region included in each block) and the amount of motion of an adjacent block (a representative value of the amount of motion of a small region included in the adjacent block) for which the difference is to be calculated, as described in the second method, and stores the calculated variance in RAM or the like as information related to pleural adhesion. Alternatively, the control unit 31 compares the calculated variance with a predetermined threshold. If the control unit 31 determines that the calculated variance is equal to or greater than the predetermined threshold, the control unit 31 stores information indicating that the calculated variance is equal to or greater than the predetermined threshold or information indicating that there is an area in the lung region where the amount of motion is reduced, as information related to pleural adhesion. If the control unit 31 determines that the calculated variance is below the predetermined threshold, the control unit 31 stores information indicating that the calculated variance is not equal to or greater than the predetermined threshold or information indicating that the amount of motion of the lung region is not reduced, as information related to pleural adhesion. Note that the predetermined threshold is a value determined through clinical experiments.

[0097] (4th method) In the fourth method, the control unit 31 calculates the difference between the amount of motion of each small region (first region) within a lung region in a dynamic image and the amount of motion of a small region (second region) located at the same position as the small region in the lung opposite the one in which the small region is located, and generates the calculation result as information about pleural adhesion. Alternatively, the control unit 31 compares the calculated difference with a predetermined threshold. If the control unit 31 determines that the calculated difference is equal to or greater than the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is equal to or greater than the predetermined threshold or information indicating that the amount of motion of the small region (first region) has decreased as information about pleural adhesion in RAM or the like, in association with the small region. If the control unit 31 determines that the calculated difference is below the predetermined threshold, the control unit 31 stores information indicating that the calculated difference is not equal to or greater than the predetermined threshold or information indicating that the amount of motion of the small region (first region) has not decreased as information about pleural adhesion in RAM or the like, in association with the small region. The predetermined threshold used for comparing the calculated difference is a value verified through clinical experiments.

[0098] In adhesion information generation process B, even in cases where lung movement or body movement is large and it is difficult to determine the decrease in the amount of movement of each small region, by calculating the difference in the amount of movement between multiple small regions or between blocks consisting of multiple small regions, it is possible to accurately detect regions where the amount of movement has decreased due to adhesion. 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), it is possible to more accurately detect the decrease in the amount of movement by calculating the difference between 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 FIG. 9 is completed, the control unit 31 outputs the generated information on pleural adhesion (step S27). The generated information regarding pleural adhesions may be output as characters or numbers, or may be output by adding a color to the image corresponding to the characters or numbers.

[0100] For example, when information on pleural adhesions 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 surrounding small regions, as shown in Fig. 17. In addition, as shown in Fig. 18, each small region on the reference frame image may be displayed on the display unit 34 with a color corresponding to the amount of movement of that small region. Alternatively, as shown in Fig. 19, only small regions on the reference frame image whose difference in amount of movement from surrounding small regions is equal to or greater than a predetermined threshold may be highlighted with a color. This makes it possible to highlight areas with reduced motion that may be adhesions and make them easy to understand for the user.

[0101] Furthermore, for example, when information on pleural adhesion is generated by the second method in step S26, the control unit 31 assigns a color to each block on the reference frame image according to the difference between the amount of movement of that block and the amount of movement of its adjacent blocks, and displays the result on the display unit 34. In addition, as shown in FIG. 20, each block on the reference frame image may be assigned a color according to the amount of movement of that block, and displayed on the display unit 34. Alternatively, blocks on the reference frame image whose difference in amount of movement with their adjacent blocks is equal to or greater than a predetermined threshold may be highlighted by being assigned a predetermined color. This makes it possible to highlight areas with reduced movement that may be adhesions, making them easy to understand for the user.

[0102] Furthermore, for example, when information about pleural adhesion is generated by the third method in step S26, the control unit 31 displays the information about pleural adhesion, for example, as a numerical value, on the display unit 34. Furthermore, 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 possible to clearly indicate to the user that there is a motion-decreased region in the lung region where adhesion may occur.

[0103] Furthermore, for example, when information about pleural adhesions is generated by the fourth method in step S26, the control unit 31 applies a color to each small region on the reference frame image according to the difference between the amount of movement of that small region and the amount of movement of a small region at the same position as that small region in a different lung region on the left and right, and displays the color on the display unit 34. Alternatively, small regions on the reference frame image where the difference in the amount of movement between that small region and a small region at the same position as that small region in a different lung region on the left and right is equal to or greater than a predetermined threshold may be highlighted by applying a predetermined color. This makes it possible to highlight regions where adhesions may exist and make them easy for the user to understand.

[0104] As described above, the control unit 31 of the diagnostic console 3 in the second embodiment acquires a dynamic image of the chest obtained by dynamic radiography, 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 acquired dynamic image and the amount of movement of a second region different from the first region, and displays the generated information regarding pleural adhesion on the display unit 34.

[0105] Therefore, because 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 low radiation exposure, without using conventional 4D-CT scans, which are difficult to introduce into general medical facilities due to the cost of the equipment and have problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow an overview of the subject as a whole because it captures localized images, requires enormous imaging time when attempting to capture the entire body, and has difficult imaging techniques.As a result, it becomes possible to easily obtain information about pleural adhesions with low radiation exposure, without introducing large, expensive equipment, in general medical facilities. Furthermore, since information about pleural adhesions is generated based on the difference or ratio of the amount of movement between multiple regions (e.g., between small regions or between blocks consisting of multiple small regions) within the lung region in a dynamic image, it is possible to easily and accurately obtain information about pleural adhesions with a low dose of radiation, even in cases where lung movement or body movement is large and it is difficult to determine a decrease in the amount of movement in each region. Furthermore, compared to conventional techniques that detect adhesions from a mismatch between the phase related to diaphragm shape change or diaphragm displacement and the respiratory phase, it is possible to obtain information about pleural adhesions with a high degree of accuracy. Furthermore, by generating and outputting information about pleural adhesions for each small region or block, the user can easily grasp the location and extent of possible adhesions.

[0106] <Third embodiment> Next, a third embodiment of the present invention will be described. In the third embodiment, an example will be described in which information about pleural adhesion is generated based on the amount of movement of a region in the lung region that does not include a region adjacent to the thorax and a threshold value.

[0107] The configurations of the dynamic analysis system 100, imaging device 1, imaging console 2, and diagnostic console 3 in the third embodiment are the same as those described in the first embodiment, and therefore the same explanations are used. In addition, the operations of the imaging device 1 and imaging console 2 are also the same as those described in the first embodiment, and therefore the same explanations are used. Below, the operation of the diagnostic console 3 in the third embodiment will be described.

[0108] In the third embodiment, when the diagnostic console 3 receives a series of frame images of dynamic chest images from the radiography console 2 via the communication unit 35, the diagnostic console 3 executes a dynamic analysis process C shown in Fig. 21 in cooperation with the control unit 31 and a program stored in the storage unit 32. 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 of steps S31 to S35. The processes of steps S31 to S35 are the same as steps S11 to S15 in Fig. 3, and therefore the same explanation will be used.

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

[0111] In adhesion information generation process C, for example, for each small region in the lung region excluding the small region located on the thoracic-side contour of the lung region in the dynamic image, the control unit 31 determines whether the amount of movement of that small region is equal to or less than a predetermined threshold, and if it determines that the amount of movement is equal to or less than the predetermined threshold, stores information indicating that the amount of movement of that small region is equal to or less than the predetermined threshold or information indicating that the amount of movement of that small region has decreased in association with the small region as information about pleural adhesion in RAM, etc. 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 that small region is not equal to or less than the predetermined threshold (exceeds the predetermined threshold) or information indicating that the amount of movement of that small region has not decreased in association with the small region as information about pleural adhesion in RAM, etc. The predetermined threshold value to be compared with the amount of movement of the small region is a value verified through clinical experiments.

[0112] In regions where pleural adhesions exist, the amount of movement is smaller than in other regions. In adhesion information generation process C, for each small region in the lung region, excluding a small region located on the thoracic-side contour of the lung region in the dynamic image, it is determined whether the amount of movement is equal to or less than a predetermined threshold, and if it is determined that the amount of movement is equal to or less than the predetermined threshold, it is determined that the small region may have adhesions, and information about pleural adhesions is generated based on the determination result. Therefore, information about pleural adhesions on the ventral or dorsal side of the body in the lung region can be generated easily and accurately with a small amount of radiation exposure.

[0113] When the adhesion information generation process C in step S36 in FIG. 21 is completed, the control unit 31 outputs the generated information on pleural adhesion (step S37). The generated information regarding pleural adhesions may be output as characters or numbers, or may be output by adding a color to the image corresponding to the characters or numbers.

[0114] For example, each small region on the reference frame image is assigned a color according to the amount of movement based on the motion vector, and displayed on the display unit 34. Alternatively, a motion vector is mapped to each small region on the reference frame image, and the motion vector of a small region with an amount of movement equal to or less than a predetermined threshold is displayed on the display unit 34 in a color different from the motion vectors of other small regions. Alternatively, small regions with an amount of movement equal to or less than a predetermined threshold may be highlighted using an annotation or the like. Alternatively, small regions on the reference frame image with an amount of movement equal to or less than 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 there is a possibility of adhesion and the amount of movement has decreased, 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 about pleural adhesions based on the amount of movement of areas in the lung region in the acquired dynamic images that do not include areas adjacent to the thorax, and displays the generated information about pleural adhesions on the display unit 34.

[0116] Therefore, because 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 low radiation exposure, without using conventional 4D-CT scans, which are difficult to introduce into general medical facilities due to the cost of the equipment and have problems such as complicated imaging procedures and high radiation exposure, or ultrasound diagnostic equipment, which does not allow an overview of the subject as a whole because it captures localized images, requires enormous imaging time when attempting to capture the entire body, and has difficult imaging techniques.As a result, it becomes possible to easily obtain information about pleural adhesions with low radiation exposure, without introducing large, expensive equipment, in general medical facilities. Furthermore, since information about pleural adhesions is generated based on the amount of movement of a region in the lung region in a dynamic image that does not include a region adjacent to the thorax, compared to conventional technologies that detect adhesions based on a mismatch between the phase related to diaphragm shape change or diaphragm displacement and the respiratory phase, information about pleural adhesions on the ventral or dorsal side of the body in the lung region can be generated easily and accurately with a low dose of radiation. Furthermore, in the third embodiment, when 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 whether adhesions exist, improving interpretation efficiency. Furthermore, by generating and outputting information about pleural adhesions for each small region, the user can easily grasp the location and extent of possible adhesions.

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

[0118] The configurations of the dynamic analysis system 100, imaging device 1, imaging console 2, and diagnostic console 3 in the fourth embodiment are the same as those described in the first embodiment, and therefore the same explanations are used. In addition, the operations of the imaging device 1 and imaging console 2 are also the same as those described in the first embodiment, and therefore the same explanations are used. Below, the operation of the diagnostic console 3 in the fourth embodiment will be described.

[0119] In the fourth embodiment, when the diagnostic console 3 receives a series of frame images of dynamic chest images from the radiography console 2 via the communication unit 35, the diagnostic console 3 executes a dynamic analysis process D shown in Fig. 22 in cooperation with the control unit 31 and a program stored in the storage unit 32. 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 of steps S41 to S45. The processes of steps S41 to S45 are similar to steps S11 to S15 in Fig. 3, and therefore the same explanation will be used.

[0121] Next, the control unit 31 executes adhesion information generation processing A (step S46). Next, the control unit 31 executes adhesion information generation processing B (step S47). Next, the control unit 31 executes adhesion information generation processing 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 about pleural adhesion generated in the adhesion information generation processes A to C (step S49). For example, if the information regarding pleural adhesions generated in at least one of adhesion information generation processes A to C includes information indicating a decrease in the amount of movement, the control unit 31 determines that there is a possibility of adhesions in the lung region of the dynamic image. In addition, if you want to increase the sensitivity of adhesion detection, you can determine that there is a possibility of adhesion in the lung region if the information about pleural adhesions generated in at least one of the adhesion information generation processes A to C contains information indicating a decrease in the amount of movement, and if you want to increase specificity, you can determine that there is adhesion in the lung region if the information about pleural adhesions generated in at least two of the adhesion information generation processes A to C contains information indicating a decrease in the amount of movement.

[0123] Next, the control unit 31 calculates the accuracy (reliability) of the judgment in step S49 based on the number of pieces of information indicating a decrease in the amount of movement within the lung area (small area) contained in the information on pleural adhesions generated by the adhesion information generation processes A to C (step S50). For example, if the information on pleural adhesions generated by each of adhesion information generation processes A to C includes information indicating a decrease in the amount of movement in at least one small region, a count of 1 is calculated, and the total number of counts for adhesion information generation processes A to C is calculated. Because adhesion information generation processes A and C are performed on different regions, there is no small region in which the amount of movement is determined to be decreased in both processes, and the maximum number of counts is 2. The total number of calculated counts divided by the maximum count (2 in this case) is then calculated as the accuracy of the determination. In other words, the accuracy of the determination is 0, 1 / 2 = 50%, or 2 / 2 = 100%. The counts for adhesion information generation processes A to C may be weighted. For example, adhesion information generation process C is weighted twice as much as adhesion information generation processes A and B because it is more likely to have determined that the amount of movement is decreased over a wide range. Alternatively, for each small region, the number of times that the information about pleural adhesions generated by adhesion information generation processes A to C includes information indicating a decrease in the amount of movement (maximum 2) may be counted, and the total number of counts / maximum count (here, 2) may be calculated as the accuracy of the determination of that small region (0, 1 / 2=50%, 2 / 2=100%). Also, as described above, the counts of adhesion information generation processes A to C may be weighted.

[0124] Then, the control unit 31 outputs the determination result of the possibility of adhesion and the accuracy (step S51). For example, the determination result determined in step S49 and the accuracy of the determination calculated in step S50 are displayed on the display unit 34. Information on pleural adhesion generated in the adhesion information generation processes A to C may also be displayed together.

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

[0126] The contents of the description in the first to fourth embodiments are preferred examples of the present invention, and the present invention is not limited to these.

[0127] For example, in the above embodiment, the display unit 34 is used as the output unit and information regarding pleural adhesion is displayed on the display unit 34. However, for example, the output unit may be the communication unit 35, and information regarding pleural adhesion may be output to an external device via the communication unit 35, and the information regarding pleural adhesion may be displayed, output, or printed on the external device.

[0128] In the above description, examples have been disclosed in which a hard disk or a semiconductor nonvolatile memory is used as a computer-readable medium for the program according to the present invention, but the present invention is not limited to these examples. Portable recording media such as CD-ROMs can also be used as other computer-readable media. Furthermore, carrier waves can also be used as a medium for providing data for the program according to the present invention via a communication line.

[0129] In addition, the detailed configuration and operation of each device constituting the dynamic analysis system can 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 exposure control device 13 Radiation detection unit 14 Reading control device 2. Filming console 21 Control section 22 Memory section 23 Control section 24 Display section 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 for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that divides a lung region in the dynamic image into small regions each consisting of one or more pixels, and generates information about pleural adhesion based on the amount of movement of a small region in the lung region that does not include an area adjacent to the thorax; an output unit that outputs the generated information about pleural adhesion; A dynamic image analysis device comprising:

2. The dynamic image analysis device of claim 1, wherein the generation unit divides the lung area in the dynamic image into small areas consisting of one or more pixels, determines whether the amount of movement of a small area within the lung area that does not include an area adjacent to the thorax is below a predetermined threshold, and based on the determination result, generates information indicating whether the amount of movement of the small area is below the predetermined threshold or information indicating whether the amount of movement of the small area has decreased as information regarding the pleural adhesion.

3. The dynamic image analysis device according to claim 1 or 2, wherein the dynamic image is a dynamic image captured while the subject is breathing.

4. 4. The dynamic image analysis device according to claim 1, wherein the region adjacent to the thorax is a region on the contour of the lung region on the thorax side in the dynamic image.

5. 5. The dynamic image analysis device according to claim 1, wherein the region adjacent to the thorax is a region representing the visceral pleura.

6. 6. The dynamic image analysis device according to claim 5, wherein the generation unit compares the amount of movement of the region representing the visceral pleura with the amount of movement of a region within the lung region that is different from the region representing the visceral pleura, and generates information indicating whether the amount of movement of the visceral pleura has decreased as information regarding pleural adhesion.

7. 7. The dynamic image analysis device according to claim 1, wherein the amount of movement is a change in position of the dynamic image from a reference frame image.

8. 7. The dynamic image analysis device according to claim 1, wherein the amount of movement is a distance between the dynamic image and a small region on the rib cage in a reference frame image.

9. Computer, an acquisition unit for acquiring dynamic images of the chest obtained by dynamic radiography; a generating unit that divides a lung region in the dynamic image into small regions each consisting of one or more pixels, and generates information about pleural adhesion based on the amount of movement of a small region in the lung region that does not include a region adjacent to the thorax; an output unit that outputs the generated information about pleural adhesion; A program to function as a

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