JP2022116402A - Dynamic image analysis device and program - Google Patents

Abstract

To easily acquire information about the adhesion of a pleura with a less exposure quantity.SOLUTION: A control unit of a diagnostic console acquires a dynamic image of a chest obtained by dynamic imaging with the radiation, derives information about the adhesion of a pleura on the basis of a movement amount in a body axial direction of a lung area in the acquired dynamic image, and displays and outputs the derived information about the adhesion of the pleura by a display unit.SELECTED DRAWING: Figure 3

Description

The present invention relates to a dynamic image analysis device and program.

As a technique for evaluating the presence or absence of adhesion between living tissues and the site of adhesion before surgery, for example, Patent Literature 1 discloses that still images in two time phases during inspiration and expiration are collected by a CT device. Techniques for assessing pleurodesis using time-phased still images are described. Further, for example, in Patent Document 2, a three-dimensional motion vector is obtained for each voxel pair inside and outside the lung surface that is physically close in three-dimensional CT image data (4D data) acquired over time. and extracting a moving part and a non-moving part on the contour line of the lung region based on the slippage. Further, for example, Patent Document 3 describes obtaining the motion vector of two structures that are physically close to each other in an ultrasound image and calculating the degree of adhesion between the two structures. ing.

JP 2016-67832 A JP 2019-180899 A JP 2019-88565 A

However, CT equipment and 4D-CT are difficult to introduce into general medical facilities due to the cost of the equipment, and from the viewpoint of the complexity of the imaging procedure and the amount of radiation exposure, it is difficult to apply to general preoperative patients. There's a problem. In addition, since the ultrasonic diagnostic apparatus performs local imaging, it is not possible to view the entire subject, and if an attempt is made to image the entire subject, the imaging time will be enormous. Also, the shooting technique is difficult. Therefore, similarly, there is a problem that it is difficult to apply to general preoperative patients.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and an object of the present invention is to enable easy acquisition of information on pleural adhesions with a small radiation dose.

In order to solve the above problems, the dynamic image analysis device of the invention according to claim 1 is
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement of the lung region in the body axis direction in the dynamic image;
an output unit that outputs the derived information about the adhesion;
Prepare.

The dynamic image analysis device of the invention according to claim 2,
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement in a specific direction of the lung region in the dynamic image;
an output unit that outputs the derived information about the adhesion;
Prepare.

The invention according to claim 3 is the invention according to claim 2,
The derivation unit analyzes the dynamic image to set the movement direction of the lung region in the dynamic image as the specific direction, and derives information about pleural adhesion based on the amount of movement in the specific direction.

The invention according to claim 4 is the invention according to claim 2,
The derivation unit uses a direction designated by a user operation as the specific direction, and derives information about pleural adhesion based on the amount of movement in the specific direction.

The invention according to claim 5 is the invention according to any one of claims 1 to 4,
The information about the adhesion includes at least one of information indicating the presence or absence of the adhesion, information indicating the strength of the adhesion, and information indicating the likelihood of the adhesion.

The invention according to claim 6 is the invention according to any one of claims 1 to 5,
The information on the adhesion includes the amount of motion for each subregion of the lung region in the dynamic image.

The invention according to claim 7 is the invention according to any one of claims 1 to 6,
The derivation unit derives information about the adhesion for each small region based on the movement amount for each small region of the lung region in the dynamic image,
The output unit outputs information about the adhesion for each of the small regions in the dynamic image.

The invention according to claim 8 is the invention according to claim 7,
The information about the adhesion for each small area includes information as to whether or not the movement amount for each small area of the lung area is equal to or less than a predetermined threshold.

The invention according to claim 9 is the invention according to claim 7,
The output unit outputs an image added with a color corresponding to the adhesion-related information for each of the small regions of the representative frame image of the dynamic image.

The invention according to claim 10 is the invention according to claim 7,
The output unit outputs an image displaying a vector indicating the motion amount for each of the small regions of the representative frame image of the dynamic image.

The invention according to claim 11 is the invention according to claim 7,
The output unit outputs an image in which a color corresponding to the motion amount is added to each of the small regions of the representative frame image of the dynamic image.

The invention according to claim 12 is the invention according to claim 11,
The derivation unit derives a representative value of the amount of movement for each small region at each vertical position of each lung region in the dynamic image,
The output unit outputs an image in which the vicinity of each lung region of the representative frame image of the dynamic image is colored according to the representative value for each vertical position of each lung region.

The invention according to claim 13 is the invention according to any one of claims 1 to 12,
The derivation unit derives information about the adhesion for each of the left and right lungs in the dynamic image,
The output unit outputs information about the adhesion for each of the left and right lungs.

The invention according to claim 14 is the invention according to claim 13,
The information regarding the adhesion derived by the derivation unit for each of the left and right lungs includes numerical values regarding motion reduction regions in which the amount of motion in the lung region is equal to or less than a predetermined threshold.

The invention according to claim 15 is the invention according to claim 13 or 14,
The information regarding the adhesion derived by the derivation unit for each of the left and right lungs includes regions in which the amount of movement in the lung region is equal to or less than a predetermined first value and values equal to or greater than a second value greater than the first value. and at least one of the maximum amount of motion in a region within a predetermined range from a region where the amount of motion in the lung region is equal to or less than a predetermined threshold.

The invention according to claim 16 is the invention according to any one of claims 1 to 15,
The derivation unit further derives information about the adhesion based on the amount of movement of the diaphragm in the dynamic image.

The invention according to claim 17 is the invention according to any one of claims 1 to 16,
The output unit outputs information warning that the accuracy of the adhesion-related information is low when the movement amount of the diaphragm in the dynamic image is equal to or less than a predetermined threshold.

The invention according to claim 18 is the invention according to any one of claims 2 to 4,
When the specific direction is the direction of movement at a specific point of the ribcage, the output unit warns that the accuracy of the information on the adhesion is low when the amount of movement of the specific point in the dynamic image is equal to or less than a predetermined threshold. output the information to

The program of the invention according to claim 19,
the computer,
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a deriving unit that derives information about pleural adhesion based on the amount of movement of the lung region in the body axis direction in the dynamic image;
an output unit that outputs information about the derived adhesion;
function as

The program of the invention according to claim 20,
the computer,
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement in a specific direction of the lung region in the dynamic image;
an output unit that outputs information about the derived adhesion;
function as

ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to acquire easily the information regarding adhesion of a pleura with a small exposure dose.

It is a figure showing the whole dynamics analysis system composition in an embodiment of the present invention. 4 is a flowchart showing imaging control processing executed by a control unit of the imaging console in FIG. 1; 4 is a flowchart showing adhesion information derivation processing executed by the control unit of the diagnostic console of FIG. 1; FIG. 4 is a diagram schematically showing movements of the ribcage, diaphragm, and lungs during respiratory motion. FIG. 4 is a flowchart showing a motion amount MAP creation process executed in step S12 of FIG. 3; FIG. FIG. 6 is a diagram for explaining preprocessing executed in step S121 of FIG. 5; FIG. FIG. 10 is a diagram for explaining merging of motion vectors; FIG. FIG. 4 is a diagram for explaining a threshold value for judging adhesion; FIG. 11 is a diagram for explaining an example of calculation of an in-lung area ratio of a reduced motion region; The non-measurable area of the reduced movement area (adhesion) by combining the amount of diaphragm movement and the lung area ratio of the reduced movement area, and the threshold of the lung area ratio for judging the presence or absence of adhesion according to the amount of diaphragm movement. It is a diagram. It is a figure which shows an example of an analysis result screen. FIG. 10 is a diagram showing an example of a summary image showing the amount of motion for each small area; FIG. 10 is a diagram showing an example of a summary image showing adhesion strength; FIG. 10 is a diagram for explaining a method of designating a moving direction of the ribcage as a specific direction;

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

[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.
As shown in FIG. 1, the dynamic analysis system 100 includes an imaging apparatus 1 and an imaging console 2 connected by a communication cable or the like, and an imaging console 2 and a diagnostic console 3 connected via a LAN (Local Area Network) or the like. are connected via a communication network NT. 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.

[Configuration of imaging device 1]
The imaging device 1 is imaging means for imaging the dynamics of the chest having a periodicity (cycle), such as morphological changes in expansion and contraction of the lungs associated with respiratory motion, heartbeats, and the like. Dynamic radiography involves irradiating the subject with pulsed radiation such as X-rays repeatedly at predetermined time intervals (pulse irradiation), or continuously irradiating the subject at a low dose rate without interruption (continuous irradiation). Acquisition of a plurality of images showing the dynamics of the subject. A series of images obtained by dynamic imaging are called dynamic images. A dynamic image is a three-dimensional image that includes a time axis. Also, each of the plurality of images forming the dynamic image is called a frame image. In the following embodiment, a case of performing dynamic imaging of the front of the chest by pulse irradiation will be described.

The radiation source 11 is arranged at a position facing the radiation detection unit 13 with the subject M interposed therebetween, 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 based on the radiation irradiation conditions input from the imaging console 2 to perform radiation imaging. Radiation irradiation conditions input from the imaging console 2 include, for example, pulse rate, pulse width, pulse interval, number of imaging frames per imaging, X-ray tube current value, X-ray tube voltage value, additional filter type, and the like. is. The pulse rate is the number of radiation exposures per second and matches the frame rate described later. The pulse width is the radiation exposure time per radiation exposure. The pulse interval is the time from the start of one irradiation to the start of the next irradiation, and coincides with the frame interval described later.

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 detects the radiation emitted from the radiation source 11 at a predetermined position on the substrate and transmitted through at least the subject M according to its intensity, and converts the detected radiation into an electrical signal. A plurality of detection elements (pixels) are arranged in a matrix. Each pixel includes a switching unit such as a TFT (Thin Film Transistor). FPDs include an indirect conversion type in which X-rays are converted into electric signals by a photoelectric conversion element via a scintillator, and a direct conversion type in which X-rays are directly converted into electric signals. Either type may be used.
The radiation detection unit 13 is provided so as to face the radiation source 11 with the subject M interposed therebetween.

The reading control device 14 is connected to the imaging console 2 . The reading control device 14 controls the switching unit of each pixel of the radiation detection unit 13 based on the image reading conditions input from the imaging console 2 to switch the reading of the electric signal accumulated in each pixel. Then, image data is acquired by reading the electrical signals accumulated in the radiation detection unit 13 . This image data is a frame image. The reading control device 14 then outputs the acquired frame image to the imaging console 2 . Image reading conditions include, for example, frame rate, frame interval, pixel size, image size (matrix size), and the like. The frame rate is the number of frame images acquired per second and matches the pulse rate. The frame interval is the time from the start of the acquisition operation of one frame image to the start of the acquisition operation of the next frame image, and coincides with the pulse interval.

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

[Structure of imaging console 2]
The imaging console 2 outputs radiation irradiation conditions and image reading conditions to the imaging device 1 to control radiation imaging and radiographic image reading operations by the imaging device 1, and transmits dynamic images acquired by the imaging device 1 to the imaging technician. The image is displayed for confirmation of positioning by the person performing the imaging, and confirmation of whether or not the image is suitable for diagnosis.
As shown in FIG. 1, the imaging console 2 includes a control section 21, a storage section 22, an operation section 23, a display section 24, and a communication section 25, which are connected by a bus 26. FIG.

The control unit 21 includes a CPU (Central Processing Unit), a RAM (Random Access Memory)
), etc. The CPU of the control unit 21 reads the system program and various processing programs stored in the storage unit 22 in accordance with the operation of the operation unit 23, expands them in the RAM, and executes shooting control processing described later according to the expanded programs. , and centrally controls the operation of each part of the imaging console 2 and the radiation irradiation operation and reading operation of the imaging apparatus 1 .

The storage unit 22 is configured by a nonvolatile semiconductor memory, hard disk, or the like. The storage unit 22 stores various programs executed by the control unit 21, parameters necessary for executing processing by the programs, or data such as processing results. For example, the storage unit 22 stores a program for executing the shooting control process shown in FIG. In addition, the storage unit 22 stores radiation irradiation conditions and image reading conditions in association with inspection target sites and imaging directions. Various programs are stored in the form of readable program codes, and the control unit 21 sequentially executes operations according to the program codes.

The operation unit 23 includes a keyboard having cursor keys, numeric input keys, various function keys, etc., and a pointing device such as a mouse. 21. Further, the operation unit 23 may have a touch panel on the display screen of the display unit 24 , and in this case, outputs an instruction signal input through the touch panel to the control unit 21 .

The display unit 24 is configured by a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube), and displays input instructions, data, etc. from the operation unit 23 according to instructions of display signals input from the control unit 21. do.

The communication unit 25 includes a LAN adapter, modem, TA (Terminal Adapter), etc., and controls data transmission/reception with each device connected to the communication network NT.

[Configuration of Diagnosis Console 3]
The diagnostic console 3 is a dynamic image analysis device that acquires a dynamic image of the chest from the imaging console 2 and calculates and outputs information on pleural adhesion based on the acquired dynamic image.
As shown in FIG. 1, the diagnosis console 3 includes a control section 31, a storage section 32, an operation section 33, a display section 34, and a communication section 35, which are connected by a bus .

The control unit 31 is composed of a CPU, a RAM, and the like. The CPU of the control unit 31 reads the system program and various processing programs stored in the storage unit 32 according to the operation of the operation unit 33, develops them in the RAM, and generates adhesion information (described later) according to the developed programs. Various processing including derivation processing is executed, and the operation of each unit of the diagnostic console 3 is centrally controlled. The control unit 31 functions as an acquisition unit and a derivation unit by executing adhesion information derivation processing.

The storage unit 32 is configured by a nonvolatile semiconductor memory, hard disk, or the like. The storage unit 32 stores various programs such as a program for executing the adhesion information deriving process in the control unit 31, parameters required for execution of processing by the programs, or data such as processing results. These various programs are stored in the form of readable program codes, and the control unit 31 sequentially executes operations according to the program codes.

The operation unit 33 includes a keyboard having cursor keys, numeric input keys, various function keys, etc., and a pointing device such as a mouse. Output to the control unit 31 . Further, the operation unit 33 may have a touch panel on the display screen of the display unit 34 , and in this case, outputs an instruction signal input through the touch panel to the control unit 31 .

The display unit 34 is configured by a monitor such as an LCD or a CRT, and performs various displays according to instructions of display signals input from the control unit 31 . The display section 34 functions as an output section.

The communication unit 35 includes a LAN adapter, modem, TA, etc., and controls data transmission/reception with each device connected to the communication network NT.

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

(Operation of imaging device 1 and imaging console 2)
First, the photographing operation by the photographing device 1 and the photographing console 2 will be described.
FIG. 2 shows imaging control processing executed in the control unit 21 of the imaging console 2 . The shooting control process is executed by cooperation between the control unit 21 and a program stored in the storage unit 22 .

First, the imaging operator operates the operation unit 23 of the imaging console 2 to input patient information and examination information of the subject (subject M) (step S1).

Next, radiation irradiation conditions are read out from the storage unit 22 and set in the radiation irradiation control device 12, and image reading conditions are read out from the storage unit 22 and set in the reading control device 14 (step S2).

Next, a radiation irradiation instruction by operating the operation unit 23 is awaited (step S3). Here, the imaging operator positions the subject M by placing it between the radiation source 11 and the radiation detection unit 13 . When imaging preparations are completed, the operation unit 23 is operated to input a radiation irradiation instruction.

When a radiation irradiation instruction is input by the operation unit 23 (step S3; YES), an imaging start instruction is output to the radiation irradiation control device 12 and the reading control device 14, and dynamic imaging is started (step S4). That is, radiation is emitted from the radiation source 11 at pulse intervals set in the radiation exposure control device 12 , and frame images are acquired by the radiation detection section 13 . During dynamic radiography, the radiographer performs respiration guidance such as "breathe in," "hold your breath," and "exhale." Note that the imaging apparatus 1 includes an audio output unit and a display unit, and when an instruction to start imaging is output, breathing guidance such as “breathe in”, “hold your breath”, and “exhale”, or a breath-holding instruction is displayed. may be performed by voice or display.

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

Frame images acquired by imaging are sequentially input to the imaging console 2, stored in the storage unit 22 in association with a number (frame number) indicating the order of imaging (step S5), and displayed on the display unit 24. (Step S6). The operator confirms the positioning and the like from the displayed dynamic image, and determines whether an image suitable for diagnosis has been obtained (acquisition OK) or whether re-imaging is necessary (imaging NG). Then, the operation unit 23 is operated to input the judgment result.

When a determination result indicating OK for photographing is input by a predetermined operation of the operation unit 23 (step S7; YES), an identification ID for identifying the dynamic image and , patient information, examination information, irradiation conditions, image reading conditions, a number indicating the order of imaging (frame number), etc. are attached (for example, written in the header area of the image data in DICOM format), and the communication unit 25 is transmitted to the diagnostic console 3 via the diagnostic console 3 (step S8). Then, the process ends. On the other hand, when a judgment result indicating that the shooting is NG is input by a predetermined operation of the operation unit 23 (step S7; NO), the series of frame images stored in the storage unit 22 are deleted (step S9), and the present process is terminated. finish. In this case, re-shooting is required.

(Operation of Diagnosis Console 3)
Next, the operation of the diagnostic console 3 will be described.
In the diagnosis console 3, when a series of frame images of dynamic images of the chest are received from the imaging console 2 via the communication unit 35, cooperation between the control unit 31 and the program stored in the storage unit 32 is performed. , the adhesion information deriving process shown in FIG. 3 is executed.

Here, as shown in FIG. 4, the ribcage (indicated by symbol T in FIG. 4) and the diaphragm (indicated by symbol D in FIG. 4) are containers for soft lungs (indicated by symbol L in FIG. 4), and breathe. If you want to, by moving the ribcage and diaphragm, the lungs expand and contract due to changes in internal pressure, allowing air to move in and out. In respiratory motion, the lungs expand and contract due to vertical movement of the diaphragm and expansion and narrowing of the thorax. In general, vertical movement of the diaphragm is dominant.
Normally, the ribcage and lungs are separated, but if the parietal pleura and pulmonary pleura adhere (pleural adhesion) due to inflammation, the ribcage and lungs become strongly attached at that point. Therefore, as a method for judging pleural adhesion, it is necessary to observe how the lungs move in response to an external force. It can be determined by observing whether or not. However, since dynamic images are transparent images, the positions of the lungs, ribcage, and diaphragm in the depth direction cannot be accurately determined, and the relative positional relationships cannot be accurately determined. there were.
Therefore, the inventor of the present application analyzed the dynamic images of the chests of people with and without pleural adhesions, and measured the amount of movement of the lung region in the body axis direction. As a result, it was found that when there is pleural adhesion, the amount of axial movement of the lung region corresponding to the adhesion site is less than or equal to a certain threshold. In other words, the external force is the movement of the diaphragm, which is the dominant movement of the entire lung during breathing, and the amount of movement is measured in the direction of movement of the diaphragm, that is, the amount of movement in the body axis direction. It was found that adhesion can be determined by We also found that the relative positional relationship between the lungs and the nearby thorax can be replaced by the absolute position of the lung region. This made it possible to distinguish adhesions even in dynamic images. Therefore, in the adhesion information deriving process shown in FIG. 3, the amount of movement of the lung region in the dynamic image in the body axis direction is measured and used to derive information on pleural adhesion.
In order to measure the movement in the body axis direction, the present invention is not limited to frontal imaging, but may be performed from a direction substantially perpendicular to the body axis direction, such as a side view or an oblique position. In addition, since the direction of gravity coincides with the direction of the body axis when imaging is performed in a standing position, stable measurement is possible, but a sitting or lying position may also be used.
The body axis direction is the head-to-tail direction of a person or animal, but it is not necessary to accurately measure the head-to-tail position. It may be processed as if it is. In addition, when the spine angle is greatly inclined, the body axis direction may be redefined according to the photographed image, such as corresponding to the spine angle.

The adhesion information deriving process will be described below with reference to FIG.
First, the control unit 31 acquires the dynamic image received by the communication unit 35 (step S11).
Next, the control unit 31 performs motion amount MAP creation processing on the acquired dynamic image (step S12).
The motion amount MAP creation process is a process of extracting corresponding points for each lung small region between frame images in the dynamic image, and extracting a motion vector for each lung small region based on changes in the positions of the corresponding points. In this embodiment, each small area is defined as a pixel, but each pixel block may be defined as a plurality of pixels.

FIG. 5 is a flow chart showing the flow of motion amount MAP creation processing executed in step S12. The motion amount MAP creation process is executed by cooperation between the control unit 31 and a program stored in the storage unit 32 .
First, the control unit 31 performs preprocessing on the dynamic image (step S121).

In the preprocessing, the control unit 31 acquires a frame image of a section to be analyzed (used for deriving information on pleural adhesion) from the dynamic image.

For example, the control unit 31 acquires the frame image of the expiration period (for example, from the maximum inspiratory position to the maximum expiratory position) as the frame image of the section to be analyzed. The frame images of the expiration period can be acquired, for example, by recognizing the lung region from each frame image of the dynamic image and extracting the frame images from the maximum (maximum) to the minimum (minimum) area of the recognized lung region. can be done. Alternatively, the distance between the lung apex and the diaphragm is measured from each frame image of the dynamic image, and the frame image of the interval from the maximum (maximum) to the minimum (minimum) distance between the lung apex and the diaphragm is taken as the frame of the exhalation period. You may acquire it as an image. Alternatively, the frame images in the section from the maximum (maximum) to the minimum (minimum) density (average density) in the lung region of the dynamic image may be acquired as frame images during the inspiration period. The user may be asked to specify the section to be analyzed.

Next, as shown in FIG. 6, the control unit 31 performs bone suppression processing (Bone Suppression processing (BS Processing)) is performed to generate a bone attenuation image (BS image), and the generated BS image is subjected to frequency enhancement processing to obtain a frequency-enhanced image.
Here, in each frame image of the dynamic image of the chest, not only lungs but also various structures such as ribs are represented on one image. Therefore, if the corresponding points of the pattern on the image are simply calculated in order to extract the motion vector of the lungs, the corresponding points of the bones that move differently from the lungs are mixed. Therefore, by subjecting the original image to bone attenuation processing, it is possible to accurately calculate the corresponding points of the lungs in subsequent processing. Furthermore, the pattern in the lung area visible in the dynamic image is mainly pulmonary blood vessels, which are composed of high-frequency components, and the features of extra-pulmonary organs, fat, and muscle appear in low-frequency components. It is desirable to perform frequency enhancement processing in advance to enhance the high frequency components of . Further, in order to further attenuate the pattern of the bones on the edge of the external ribcage, noise may be added to the outside or boundary of the lung field region. The outside of the lung area used at this time does not include the mediastus or lower diaphragm area where no bone pattern exists.

Next, the control unit 31 performs optical flow between frame images adjacent in the time direction (hereinafter, between adjacent frame images) for the preprocessed frame images of the analysis target section, and performs optical flow for each small region. A motion vector is calculated by finding corresponding points between adjacent frame images (step S122).
For example, a motion vector between adjacent frame images is calculated for each small area by dense optical flow. Note that here, the motion vector between adjacent frame images is calculated, but the motion vector with the frame image n frames ahead (n is a positive integer) may be calculated. Also, n may be the number of frames in one heartbeat cycle in order to reduce motion measurement errors caused by heartbeats. Note that motion vectors may be calculated at least for each small region of the lung region.

Next, the control unit 31 merges (integrates) the plurality of motion vectors obtained in step S122 for each small area (step S123).

FIG. 7 is a diagram for explaining the merge processing in step S123. Here, a motion vector from the start frame to the end frame is calculated. As shown in FIG. 7, in step S123, first, the motion vector obtained from the start frame (frame 1) and the start frame adjacent frame (frame 1+n) in step S122, the start frame adjacent frame (frame 1+n) and the start frame A sum of motion vectors (indicated by a thick arrow in FIG. 7) obtained from the next adjacent frame (frame 2+n) is calculated. Next, the sum of the calculated motion vectors and the sum of the next motion vector are calculated. This is done until all the calculated motion vectors are added.
Thereby, a motion vector representing the motion from the start frame image to be analyzed to the end frame image can be calculated. This motion vector is saved in vector start point coordinates or vector end point coordinates. For example, if a representative frame image, which will be described later, is the frame image of the maximum exhalation position, the motion vector may be stored in the vector end point coordinates.

Note that the motion vector calculation method described above is merely an example, and the method is not particularly limited as long as a motion vector can be finally calculated for each small region from the start frame image to the end frame image of the exhalation period. However, during a deep breathing period (about 5 seconds), the positional movement and deformation of the lungs due to breathing are large, so that the appearance of the image changes drastically, and it is very difficult to calculate the corresponding points on the image. Therefore, as described above, for example, motion vectors are calculated by calculating corresponding points in short time units, such as between frame images adjacent in the time direction, and by merging them, the motion vector in the expiration period can be obtained with high accuracy. can be calculated.
As post-processing of the motion vector calculation result, various filter processing such as Gaussian filtering may be performed to remove noise.

Then, based on the calculated motion vector, the control unit 31 calculates the amount of motion in the body axis direction (upward direction) (the length of the motion vector in the body axis direction (upward direction)) for each small region. A movement amount MAP indicating the amount of movement in the body axis direction for each region is created (step S124), and the process proceeds to step S13 in FIG. When creating the motion amount MAP, motion vectors outside the lung area may be excluded from the motion vectors for each small area. An existing lung field recognition process may be used to extract the lung field area.

Since the dynamic image is a transparent image, a plurality of structures (mainly pulmonary vessels with different movements) may overlap in the depth direction from the abdomen to the back. Therefore, in step S12, the motion vector of each of the pulmonary vessels overlapping in the depth direction is calculated in each small region, and the amount of motion in the body axis direction (upward direction) (length of the motion vector in the body axis direction (upward direction) ) may be used as the motion amount of the small region in the body axis direction to create the motion amount MAP.
For example, after performing the preprocessing described above, the pulmonary vessels running in multiple angular directions are emphasized using multiple Gabor filters with different angles (directions) for the frame image of the section to be analyzed. A motion vector is calculated by executing the above-described steps S121 to S123 for each image in which the pulmonary vessels in each angular direction are emphasized. Then, the amount of motion in the body axis direction (upward direction) (the length of the motion vector in the body axis direction (upward direction)) is calculated for each small region of the image in which the pulmonary vessels in each angular direction are emphasized, and each small region may be used as the motion amount of the small region in the body axis direction to create the motion amount MAP.

Returning to FIG. 3, in step S13, the control unit 31 derives information about pleural adhesion based on the created motion amount MAP (step S13).
Information about pleural adhesion may be derived for each small region of the dynamic image, or may be derived for each of the left and right lungs. Each of these will be described below. In the following description, adhesion refers to pleural adhesion.

(1) Information on adhesion for each small region The information on adhesion for each small region includes, for example, information indicating the presence or absence of adhesion, information indicating the likelihood of adhesion (likelihood of adhesion), information indicating the strength of adhesion, and information indicating the strength of adhesion. information indicating the amount of motion.
The information indicating the presence or absence of adhesion is obtained by comparing the length of the motion vector in the direction of the body axis (upward direction) (that is, the amount of motion in the direction of the body axis (upward direction)) with a predetermined threshold value, and the comparison result It is information derived based on For example, if the length of the motion vector in the body axis direction is less than or equal to a predetermined threshold, it is determined that there is adhesion, and if it exceeds the predetermined threshold, it is determined that there is no adhesion, and the determination result is used as information indicating the presence or absence of adhesion.
For the information indicating adhesion strength, multiple thresholds for the length of the motion vector in the direction of the body axis (upward direction) are prepared, and the length of the motion vector in the direction of the body axis (upward direction) is set as multiple thresholds for each small region. It is information that is compared and derived based on the comparison result. For example, a first threshold and a second threshold are provided (first threshold>second threshold), and if the length of the motion vector in the body axis direction (upward direction) exceeds the first threshold, adhesion None, if it exceeds the second threshold but is equal to or less than the first threshold, it is determined as weak adhesion, and if it is less than the second threshold, it is determined as strong adhesion, and the determination result is used as information indicating the adhesion strength.
The information indicating the likelihood of adhesion is information derived based on the difference between a predetermined threshold value and the length of the motion vector in the body axis direction (upward direction) (see the arrow in FIG. 8) for each small region. For example, a difference is calculated by subtracting the length of the motion vector in the body axis direction (upward direction) from a predetermined threshold. ) is assumed to be adhesion likelihood 1, and the calculated value is used as information indicating the adhesion likelihood.

The thresholds for deriving the presence or absence of adhesion, the likelihood of adhesion, and the strength of adhesion are experimentally or empirically determined values, and as shown in FIG. 8, may be fixed values (TH1), It may be increased as the distance from the lung apex of the small region is larger (TH2), or may be increased as the distance from the lung apex of the small region×the movement amount of the diaphragm is larger (TH3). This is because the movement of the lung apex is small even if there is no adhesion, and if the movement of the diaphragm is small, the movement of the lung is small even if there is no adhesion. As for the likelihood of adhesion, the likelihood of adhesion calculated with a fixed threshold value may be multiplied by a coefficient according to the distance from the lung apex or by a coefficient according to the amount of movement of the diaphragm.

Here, as the threshold for determining the presence or absence of adhesion, for example, it is preferable to use a threshold for determining that strong adhesion corresponding to grade 2 or higher in Reference 1 is "adhesion" (Reference 1; Junko Tokuno et al, ``Preoperative detection of pleural adhesions by respiratory dynamic computed tomography'', World Journal of Surgical Oncology 15, 2017). Strong adhesions of grade 2 or higher in Reference 1 require advanced techniques to remove the adhesions during surgery, and are adhesions that increase the risk of bleeding. Surgery is preferable. By providing information on the presence or absence of such strong adhesions to a doctor who is a user, it is possible to assist the doctor in selecting a surgical technique.
In addition, as thresholds for judging adhesion strength, for example, strong adhesions corresponding to grade 2 or higher in Reference 1 are classified as "strong adhesions," grade 1 band adhesions as "weak adhesions," and grade 0 as "no adhesions." A decision threshold is preferably used.
For example, when the threshold is a fixed value, the threshold (first threshold) for determining weak adhesion can be 6 mm, and the threshold (second threshold) for determining strong adhesion can be 1 mm.

The information indicating the presence or absence of adhesion and the information indicating the adhesion strength may be derived based on the result of comparison between the information indicating the likelihood of adhesion and a threshold value. For example, if the likelihood of adhesion is greater than or equal to a predetermined threshold (which is different from the threshold for the length of the motion vector in the body axis direction), there is adhesion, and if the likelihood of adhesion is less than the predetermined threshold, there is no adhesion.
Further, the information related to the adhesion for each small region may be the amount of motion in the body axis direction itself, and the result of determining whether the length of the motion vector in the body axis direction is equal to or less than a predetermined threshold value. may be Based on the motion reduction determination result, the motion reduction region can be calculated.
In calculating the motion reduction area, various filtering processes such as a morphology filter may be performed as post-processing to remove minute noise areas.

In this way, by deriving information indicating the presence or absence of adhesion for each small region in the lung region of the dynamic image, the doctor, who is the user, can grasp whether or not there is pleural adhesion, and if so, where it is located. It becomes possible to Further, by deriving information indicating the likelihood of adhesion for each small region in the lung region of the dynamic image, it is possible for the doctor to grasp the likelihood of adhesion for each local region of the lung. In addition, by deriving information indicating the adhesion strength for each small region in the lung region of the dynamic image, it is possible to determine whether there are pleural adhesions, and if so, whether there are strong adhesions that affect the selection of surgical techniques. A doctor can grasp the place.

(2) Information about adhesion for each left and right lung The information about adhesion for each left and right lung includes, for example, information indicating the presence or absence of adhesion for each left and right lung, information indicating likelihood of adhesion (likelihood of adhesion), information indicating adhesion strength, At least one of: information indicating the area of the motion-reduced region, information on the degree of proximity between the small-motion region and the large-motion region, the lower end position of the motion-reduced region, the symmetry of the motion-reduced region, and the presence or absence of the motion reduction at a specific position. including.

As mentioned above, it is important for the doctor to know the presence or absence of adhesions prior to lung surgery. Increased need for thoracotomy over thoracoscopy. Therefore, for each of the left and right lungs, the reduced motion region is calculated and the lung area ratio is calculated, and based on the calculated lung area ratio of the reduced motion region, information indicating the presence or absence of adhesion for each of the left and right lungs is derived. . For example, for each small area, the length of the motion vector in the direction of the body axis (upward direction) is compared with a predetermined threshold value, and the small areas equal to or less than the predetermined threshold value are defined as motion reduction areas. Then, if the lung area ratio of the decreased movement region is equal to or greater than a predetermined threshold value, it is determined that there is adhesion, and if it is less than the predetermined threshold value, it is determined that there is no adhesion. do. For example, the same threshold as that used for deriving the information indicating the presence or absence of adhesion for each small region can be used as the threshold for determining the motion-reduced region. Further, as a predetermined threshold value (threshold value of lung area ratio) used for adhesion determination for each of the left and right lungs, for example, ⅓ or 0.5 can be used. This is due to the fact that a representative example of the size of the adhesion that should be ascertained for surgery is about 1/3 of the pleura, and that the movement of about half of the lung has decreased in experimental cases with large adhesions.

When the diaphragm is moved in a standing position, the movement of the upper lung is small due to the effect of gravity even if there is no adhesion. Therefore, the upper lung (for example, the top ⅓ region of the lung as shown in FIG. 9) may be excluded from the calculation range of the in-lung area ratio of the reduced motion region. For example, if the region determined to be the reduced motion region in FIG. 9 is the region surrounded by the thick frame, the middle and lower lung region for the entire middle and lower lung region (the region where the two hatched regions with different intervals are combined in FIG. 9) It is also possible to calculate the ratio of the reduced motion region (the finely hatched region in FIG. 9) within the lung region as the lung area ratio. In addition, when normalizing the position from the lung apex to the diaphragm apex (lower end) from 0 to 1, if there is a decreased movement area in the area above a predetermined threshold, such as 1/3 or more, the presence or absence of adhesion for each of the left and right lungs information may be derived. This is because adhesion spreads and progresses from top to bottom, so if there is adhesion below, it can be determined that the adhesion has spread over a wide area.

Further, the information on adhesion for each of the left and right lungs may be derived based on the information on adhesion for each small region described above.
For example, based on information indicating the presence or absence of adhesion for each small region, information regarding the presence or absence of adhesion for each of the left and right lungs may be derived. For example, based on the information on the presence or absence of adhesion for each small region, the lung area ratio of the region with adhesion is calculated for each of the left and right lungs. is less than the threshold value, it is determined that there is no adhesion, and the determination result is used as information indicating the presence or absence of adhesion for each of the left and right lungs. Further, as the predetermined threshold (the threshold of the lung area ratio) used for adhesion determination for each of the left and right lungs, for example, 1/3, 0.5, or the like can be used as described above.

Further, for example, information regarding adhesion strength for each of the left and right lungs may be derived based on information indicating adhesion strength for each small region. For example, based on the information about the adhesion strength for each small region, the lung area ratio of the region determined to be strong adhesion, the lung area ratio of the region determined to be weak adhesion, and the lung area determined to be no adhesion for each of the left and right lungs. It is also possible to calculate the in-lung area ratios of the regions that have been drawn, and calculate the calculation results as information on the adhesion strength for each of the left and right lungs. Alternatively, if the lung area ratio of the region determined to be strong adhesion is a predetermined threshold or more, it is determined that there is strong adhesion, and the lung area ratio of the region determined to be strong adhesion is less than the predetermined threshold and is weak. If the lung area ratio of the region determined to be adhesion is equal to or greater than a predetermined threshold, it may be determined that there is weak adhesion, and the determination result may be used as information regarding the adhesion strength for each of the left and right lungs. As the predetermined threshold value (the threshold value of the lung area ratio) used to determine the adhesion strength for each of the left and right lungs, for example, ⅓ or 0.5 can be used as described above.

Further, for example, information regarding the adhesion likelihood for each of the left and right lungs may be derived based on information indicating the adhesion likelihood for each small region.
Information on the adhesion likelihood for each of the left and right lungs can be obtained, for example, by the following (Equation 1).
Adhesion likelihood for each left and right lung = mean value of (adhesion likelihood for each small region) (Formula 1)
Alternatively, information regarding the likelihood of adhesion for each of the left and right lungs may be obtained by any of the following (formula 2) to (formula 4).
Adhesion likelihood for each left and right lung = maximum value of (adhesion likelihood for each small region) (Formula 2)
Adhesion likelihood for each left and right lung = mean value of squares of (adhesion likelihood for each small region) (Formula 3)
Average value of adhesion likelihood for each left and right lung = {MAX(0, 1 - log_a (adhesion likelihood for each small region) )} (Formula 4)
For example, it is preferable to use (Formula 2) when it is desired to reflect a high likelihood of adhesion to the likelihood of adhesion for each of the left and right lungs even if the area of adhesion is small.
Further, for example, when it is desired to reflect the adhesion likelihood of adhesion in a large area to the adhesion likelihood of each of the left and right lungs, it is preferable to use (equation 3) or (equation 4). In (Formula 3) or (Formula 4), the weight can be lowered when the adhesion likelihood for each small region is low. For example, in (Formula 4), by setting low a to, for example, 0.6, it is designed to contribute to the calculation of the adhesion likelihood for each of the left and right lungs when the adhesion likelihood for each small region is 0.6 or more. can be

Here, as described above, normal lungs move in the body axis direction in accordance with the movement of the diaphragm. The amount of movement is also reduced. That is, as shown in FIG. 10, in a graph in which the vertical axis is the lung area ratio of the decreased movement region and the horizontal axis is the amount of movement of the diaphragm, the area with almost no movement of the diaphragm (in FIG. 10, the amount of movement of the diaphragm is TH), the amount of movement of the lungs is small regardless of whether there is adhesion or not, and it is not possible to accurately determine whether the lungs are moving. Therefore, it is possible to calculate the movement amount of the diaphragm, and output (for example, display on the display unit 34) a warning indicating that the accuracy of the information regarding the derived adhesion is low when the movement amount of the diaphragm is equal to or less than a predetermined threshold TH. preferable. For example, it is preferable to output a message such as "WARNING: Due to the small movement of the diaphragm, the decrease in movement due to adhesion cannot be sufficiently measured."
It should be noted that the motion amount of the diaphragm in FIG. 10 can be obtained, for example, by recognizing the diaphragm from the dynamic image by image processing such as edge detection, and by detecting the The movement amount of (fixed x-coordinate position (horizontal coordinate position of the image), etc.) is set as the movement amount of the diaphragm.

In addition, the lung area ratio threshold for determining the presence or absence of adhesion and adhesion strength for each of the left and right lungs may be changed based on the movement amount of the diaphragm, as indicated by the curve l in FIG. Specifically, the larger the amount of movement of the diaphragm, the smaller the threshold. This is because, if there is no adhesion, the movement of the lungs increases as the amount of movement of the diaphragm increases, so that the area of decreased movement and the area determined to have adhesion become smaller.
It should be noted that the lung area ratio of the decreased motion area for each of the left and right lungs may be calculated as information related to adhesion for each of the left and right lungs.

Also, the following information may be derived as information on adhesion for each of the left and right lungs.
・Information on the degree of proximity between small movement regions and large movement regions Information on the degree of proximity between regions with small axial motion and large axial motion is calculated and used as information on adhesion between the left and right lungs. may be displayed. For example, as one method of calculating the above-mentioned information on the degree of proximity, a region in which the amount of movement in the body axis direction is a predetermined value (first value) or less (for example, 1 cm or less) and a region in which the amount of movement in the body axis direction is a predetermined value ( Second value) or more (for example, 4 cm or more) is calculated as the distance between the two regions, and if the distance between the two regions is less than a predetermined threshold (for example, within 3 cm), the possibility of adhesion is high. Therefore, it is preferable to derive and output the distance between two regions itself and whether or not the distance between two regions is equal to or less than a predetermined threshold value as the information related to the degree of proximity. Another method is to calculate the maximum amount of motion in a neighboring area (for example, a range within 3 cm around) of an area where the amount of motion in the body axis direction is a predetermined value or less (eg, 1 mm or less). The greater the maximum amount of movement, the higher the possibility of adhesion. Therefore, the maximum amount of movement itself and the determination result of whether the maximum amount of movement is greater than or equal to a predetermined threshold (for example, 3 cm or more) are output as information related to the degree of proximity. do it. Here, the distance between the two regions and the distance defining the neighborhood described above are preferably distances measured in the direction of the body axis in both of the two methods described above.
・Lower end position of the reduced motion region For example, the distance from the lung apex to the upper end of the diaphragm or the lower end of the lung region is set to 1.0, and the lower end position of the reduced motion region is calculated as a numerical value from 0 to 1.0.
This is because the position of the lower end of the region of reduced movement can be useful for estimating the range of adhesions because of the effect of gravity when the diaphragm is moved in a standing position and because adhesions tend to progress from the upper lung.
・Left-right symmetry of the decreased motion area When the movement of the lungs is small due to the small movement of the diaphragm, the movement decreases symmetrically. This is because there is a possibility of adhesion in case of left-right asymmetry).
・Presence or absence of decreased movement of a specific position (position of specific pulmonary vessels or bronchi) Movement of pulmonary vessels or bronchi (e.g., A3b of the right pulmonary artery) anatomically known to be near the pleura (body axis) This is because, if the movement in the direction) is decreased, the site is considered to be adhered.
For example, A3b of the right pulmonary artery runs in the direction of X-ray irradiation and appears round and conspicuous like a pulmonary nodule in the chest front image. Therefore, for example, in the vicinity of the hilum of the right lung in the dynamic image, A3b of the right pulmonary artery is recognized by detecting a circle of about 5 mm square using a Hessian filter, etc. If the amount (eg, the amount of upward motion during expiration) is less than or equal to a predetermined threshold, it is determined that there is motion reduction, and if it exceeds the threshold, it is determined that there is no motion reduction. Note that A3b of the right pulmonary artery is an example, and for other pulmonary vessels or bronchi that are anatomically known to be near the pleura, for example, at a specific position in the lung region of the dynamic image, the same method as A3b , and if the amount of motion in the body axis direction is equal to or less than a predetermined threshold, it may be determined that there is a decrease in motion, and if the amount exceeds the threshold, it may be determined that there is no decrease in motion.

When the derivation of the adhesion-related information is completed, the control unit 31 causes the display unit 34 to display (output) an analysis result screen 341 displaying the adhesion-related information (step S14).

FIG. 11 is a diagram showing an example of the analysis result screen 341. As shown in FIG. As shown in FIG. 11, the analysis result screen 341 is provided with a moving image display area 341a, a summary image display area 341b, a diaphragm movement amount display area 341c, and the like.

In the moving image display area 341a, a moving image of a moving image acquired by shooting is displayed. Animated display of the dynamic images allows the physician to directly observe the movement of the diaphragm and lungs and their relationship. Here, as described above, in an X-ray image, the lungs are composed of high-frequency components such as pulmonary vessels, and features of extra-pulmonary organs, fat, muscle, etc. appear in low-frequency components. Preferably, image processing for enhancing specific high-frequency components or image processing for attenuating specific low-frequency components is performed in advance. In addition, on the dynamic image, ON/OFF of the overlay display (indicated by A in FIG. 11) that surrounds the motion reduction area derived in step S13 can be switched by operating the operation unit 33 . By overlaying the hypomotion area, the doctor can observe the likelihood of adhesion by contrastingly concentrating on the difference in motion between the inside and outside of the hypomotion area. It should be noted that there is no problem in using the same display for all frame images as long as body motion is small.

The summary image display area 341b displays a summary image showing the analysis result derived by analyzing the dynamic image displayed in the moving image display area 341a.
As a summary image, for example, as shown in FIG. 12A, for each small region of one frame image of the dynamic image (representative frame image; for example, a frame image of the maximum inspiratory position), the body of the small region An image (motion amount MAP) displaying a vector indicating the amount of motion in the axial direction (upward direction) is displayed. FIG. 12A shows an example in which vectors are mapped (displayed) on the frame image of the maximum inspiratory position, but the vector may be displayed on the frame image of the maximum expiratory position. It should be noted that the amount of motion in all directions, not limited to the body axis direction, may be displayed as a vector.
Alternatively, as a summary image, as shown in FIG. 12B, each small region of the representative frame image is colored according to the body axis direction (upward) motion amount (vector length) of the small region. An image (movement amount MAP) may be displayed. As a motion amount and color conversion table, for example, with black (R, G, B=0, 0, 0) as a base point, a value obtained by multiplying the motion amount by a predetermined coefficient is assigned to either R, G, or B. All you have to do is For example, it is desirable to distinguish positive and negative colors, such as G for a positive amount of motion and B for a negative amount of motion, for better visibility. Negative movements often represent changes in the absolute position of the lungs due to thoracic respiration, so they may be hidden (left black). The value of any one of R, G, and B may be increased linearly or non-linearly according to an increase in the amount of motion. In order to improve the expressiveness, multiple different colors are assigned, such as assigning the amount of movement 0 to +1 cm to G: 0 to 255, and assigning the amount of movement +1 cm to +4 cm to R: 0 to 255 (G = 255 fixed). good too. Since the monitor used by doctors is often a high-definition monochrome monitor, it is possible to allocate the amount of movement 0 to +4 cm to 0 to 255 in monochrome (R=G=B). In order to focus on the reduction in motion, it is desirable to improve the expressiveness of small movements, such as 1 cm or less, within the limited color gamut. lower is desirable. Although it is filled with a predetermined color, it may be displayed in a transparent color.
It is desirable that the amount of motion MAP in the body axis direction to be displayed is limited to the lung region, which is the object to be observed. As a result, it becomes easier to observe the dynamic image itself outside the lungs, and there is also the effect that it becomes easier to grasp the positional relationship. The lung field region is normally composed of two regions, the right lung and the left lung, but may include the mediastinal region and the lower diaphragm region. Alternatively, user input may be enabled, and the lung region input by the user may be adopted.
As with the moving image display area 341a, the dynamic image to be displayed is desirably subjected in advance to image processing for emphasizing specific high frequency components or image processing for attenuating specific low frequency components.
In addition, for each vertical position of each lung in the dynamic image, a representative value (e.g., minimum value, average value, median value , or maximum value) is calculated, and a vertical gradation bar 342, which is an image colored according to the representative value for each vertical position of each lung region, is displayed near each lung region in the representative frame image. It is also possible to The vertical gradation bar 342 may identifiably display the bottom position of the motion reduction region (indicated by E in FIG. 12(b)).
Alternatively, as a summary image, as shown in FIG. 12(c), for each vertical position of each lung, the representative value of the amount of movement in the body axis direction (upward direction) of the small region existing at that vertical position (For example, the minimum value, the average value, the median value, or the maximum value) may be calculated, and the color corresponding to the calculated representative value may be displayed in a strip shape on the lung region of the representative frame image of the dynamic image.

In addition, in the display method shown in FIG. 12B, each small region of the representative frame image may be colored according to the adhesion likelihood of the small region instead of the amount of movement in the body axis direction. Also, a vertical gradation bar of adhesion likelihood may be displayed. As for the presence/absence of adhesion (reduced motion area), as shown in FIG. 13A, only the area (reduced movement area) determined to have adhesion in the representative frame image may be colored. As for the adhesion strength, as shown in FIG. 13B, only the small regions in the representative frame image determined to have adhesion may be given a color corresponding to the adhesion strength of the small region.
In addition to the summary image, information on adhesions for each of the left and right lungs (presence or absence of adhesions, adhesion strength, adhesion likelihood, adhesion probability, area ratio of areas with reduced motion, lower end position of areas with reduced motion, symmetry of areas with reduced motion) , information on the degree of proximity between the small motion region and the large motion region, or whether or not there is a decrease in motion at a specific position, etc.).
In FIGS. 12A to 12C and FIGS. 13A and 13B, only one lung is displayed, but both lungs are actually displayed. Further, the user can set which summary image is to be displayed in the summary image display area 341b by operating the operation unit 33. FIG.

In this way, by displaying the summary image, it is possible to display the amount of movement in the axial direction of each small region of the lung region, the presence or absence of adhesion, the strength of adhesion, the likelihood of adhesion, the distribution of regions with decreased movement, and the like. It is possible to easily grasp the distribution of adhesions, assist in interpretation, and improve the performance and efficiency of interpretation. In addition, if a summary image in which adhesion information is aggregated is sent to the PACS in advance, it can be used as a trigger for the doctor to determine whether or not it is necessary to interpret the dynamic image in detail. In addition, by superimposing the motion reduction region and the motion amount MAP in the body axis direction on the summary image, it is possible to extract the "motion reduction region" overlaid on the dynamic image of the moving image display area 341a. Extraction from the motion amount MAP in the axial direction can be visually confirmed, and explanation to the doctor can be ensured.

Further, for example, by displaying information about pleural adhesions for each of the left and right lungs in the summary image display area 341b, the doctor can easily grasp the pleural adhesions for each of the left and right lungs, thereby improving the performance and efficiency of image interpretation. can be improved.

In the diaphragm movement amount display area 341c, a graph showing the temporal change of the movement amount of the diaphragm (for example, the movement amount of the representative point of the diaphragm) in the dynamic image is displayed. On the graph, a playback position 343 of the dynamic image in the moving image display area 341a is displayed, and a range 344 of the frame image to be analyzed is displayed in an identifiable manner (for example, surrounded). As a result, the doctor can compare the reproduced frame image with the movement of the diaphragm while displaying (reproducing) the dynamic image as a moving image. In addition, it is possible to grasp the frame image to be analyzed. It should be noted that the position of the representative point used for calculating the motion amount of the diaphragm may be shown in the dynamic image displayed in the moving image display area 341a.

In the above description, an example was explained in which the expiration period of the dynamic image was the analysis target, but the inhalation period may be the analysis target. In this case, a motion vector in the body axis direction (downward direction) is calculated instead of the body axis direction (upward direction) described above, and information on adhesion is obtained based on the amount of motion in the body axis direction (downward direction). Derived and output. In addition, the analysis may be performed for each expiration period and each inspiration period for a plurality of times.

(Modification)
Modifications of the above embodiment will be described below.
In general, the lungs move mainly in the body axis direction when breathing. In addition, there are patients whose lungs move obliquely with breathing. For example, if there is a bulla in one lung, the left and right lungs become uneven, and when breathing, the lungs may move obliquely at a slight angle from the body axis. In such a case, it is desirable to derive information about adhesions based on the amount of movement in the oblique direction, which is the movement direction of the lungs.
Therefore, for example, the control unit 31 analyzes the dynamic image to acquire the trend of the movement direction of the lungs, sets the acquired direction as a specific direction, and derives information about the adhesion based on the amount of movement of the lung region in the specific direction. You can do it. For example, motion vectors are obtained by optical flow or the like for each small area of the frame image in the analysis target section of the dynamic image. As the direction, information regarding adhesion may be derived based on the amount of movement in a specific direction in the lung region. Alternatively, the user may be allowed to specify the movement direction of the lungs by operating the operation unit 33, the specified direction may be set as a specific region, and information regarding adhesion may be derived based on the amount of movement in the specific direction in the lung region. For example, a dynamic image may be displayed on the display unit 34 as a moving image, and the operation unit 33 may be used to allow the user to specify the direction of movement. Alternatively, the motion vector is obtained by optical flow or the like for each small area of the frame image in the analysis target section of the dynamic image, and the motion vector is displayed. may be In addition, when it is desired to determine adhesions in the upper lung field and the apex of the lung, since the movement of the diaphragm in the upper lung field and the apex of the lung is small, the movement direction of the ribcage around the lung field to be observed may be the specific direction. good. For example, as shown in FIG. 14, the user designates one point on the outer thoracic edge ET of the ribcage that moves with breathing as a specific point P, extracts the movement of the specific point P during inspiration, and extracts the movement direction. is a specific direction. When there is adhesion in the lung apex, it is observed that the amount of movement in a specific direction is reduced in the apex of the lung. detectable. On the other hand, if the amount of movement (in a specific direction) of the specific point P is less than a predetermined threshold value (for example, 3 mm), the ribcage, which is an external force, is not sufficiently moving, and the accuracy of the derived adhesion-related information is low. It is desirable to output (for example, display on the display unit 34) information warning that Although the user designates the specific point P here, the amount of movement of the ribcage may be extracted for each of the left and right lungs, and the point with the maximum amount of movement may be set as the specific point P. It should be noted that the body axis direction of the method described in the above embodiment may be used as the above-described specific direction for the method of deriving information about adhesion. Then, the derived information on adhesion may be displayed on the analysis result screen 341 .

In this way, by deriving information about adhesions based on the amount of movement of the lung region in a specific direction, it is possible to accurately derive information about adhesions for patients whose lungs do not move in the body axis direction due to disease. Become.

As described above, the control unit 31 of the diagnostic console 3 in this embodiment acquires a dynamic image of the chest obtained by radiographic dynamic imaging, and the amount of movement of the lung region in the body axis direction in the acquired dynamic image Based on , the information on the adhesion of the pleura is derived, and the derived information on the adhesion of the pleura is displayed on the display unit 34 .
Further, the control unit 31 of the diagnostic console 3 in the modified example of the present embodiment acquires a dynamic image of the chest obtained by radiographic dynamic imaging, and based on the amount of movement of the lung region in a specific direction in the acquired dynamic image, Then, information on pleural adhesion is derived, and the display unit 34 displays and outputs the derived information on adhesion.
Therefore, since it is possible to obtain information on pleural adhesions using dynamic images of the chest obtained by dynamic radiography, it is difficult to introduce the system into general medical facilities due to the cost of the system, as in the past. In addition, 4D-CT, which has problems such as the complexity of the imaging procedure and the amount of radiation exposure, and local imaging, do not allow an overview of the entire subject. It is possible to easily obtain information on pleural adhesions with a small radiation dose without using a difficult ultrasonic diagnostic apparatus. As a result, it is possible to easily obtain information on pleural adhesion of a patient before surgery with a small amount of radiation exposure without introducing costly and large-scale equipment in a general medical facility.

For example, the control unit 31 can include information indicating the presence or absence of adhesion, information indicating the strength of adhesion, or information indicating the likelihood of adhesion as the information regarding adhesion of the pleura. Therefore, the doctor can grasp the presence or absence of adhesion of the patient's pleura, the strength of adhesion, and the likelihood of adhesion.

Further, for example, the control unit 31 derives the information about the adhesion based on the amount of movement of each small region of the lung region in the dynamic image, so that the information about the adhesion of the pleura can be derived with high accuracy. Furthermore, by deriving information about adhesion based on the amount of motion of the diaphragm in the dynamic image, it is possible to derive information about pleural adhesion more accurately.

Further, for example, the control unit 31 derives information about pleural adhesion for each small region based on the movement amount for each small region of the lung region in the dynamic image, and outputs the information to the display unit 34 for display. The distribution of pleural adhesions can be easily grasped, and the performance and efficiency of image interpretation can be improved.

For example, by outputting an image that is colored according to the information about pleural adhesion derived for each small region of the lung region of the representative frame image of the dynamic image, the doctor can grasp the distribution of pleural adhesion at a glance. It is possible to improve the performance and efficiency of image interpretation.

Further, for example, by outputting an image displaying a vector indicating the amount of movement in the body axis direction (or in a specific direction) for each small region of the lung region of the representative frame image of the dynamic image, the doctor can glance at the lung region. It becomes possible to grasp the motion amount in the body axis direction (or in a specific direction) for each small region, and the performance and efficiency of image interpretation can be improved.

In addition, by outputting an image in which a color is added according to the amount of movement in the body axis direction (or in a specific direction) for each small region of the lung region of the representative frame image of the dynamic image, the doctor can see at a glance. It becomes possible to grasp the amount of movement in the body axis direction (or in a specific direction) for each small region of the lung region, thereby improving the performance and efficiency of interpretation.

Also, for each vertical position of each lung region in the dynamic image, a representative value of the amount of motion in the body axis direction (or in a specific direction) for each small region at that vertical position is derived, and the representative frame of the dynamic image is calculated. By outputting an image that is colored according to the representative value for each vertical position of each lung region in the vicinity of each lung region of the image, the doctor can see at a glance the body axis direction for each vertical position. (or in a specific direction) can be grasped, and the performance and efficiency of image interpretation can be improved.

In addition, by deriving information about pleural adhesion for each of the left and right lungs in the dynamic image and outputting the information about the adhesion for each of the left and right lungs, the doctor can easily grasp the pleural adhesion for each of the left and right lungs. Reading performance and efficiency can be improved.

In addition, based on the amount of movement in each small region of the lung region in the dynamic image, the motion reduction region is derived, and the display showing the derived motion reduction region is overlaid to display the dynamic image as a moving image, thereby enabling the doctor to move. It is possible to observe the difference in movement inside and outside the depressed area in a comparatively focused manner.

In addition, when the amount of movement of the diaphragm in the dynamic image is equal to or less than a predetermined threshold, information is output to warn that the accuracy of the information on adhesion is low, so that the doctor can recognize that the accuracy of the information on adhesion is low. becomes possible.

It should be noted that the descriptions in the above-described embodiment and modified examples are preferred examples of the present invention, and the present invention is not limited thereto.

For example, in the above embodiment, the case of deriving information about pleural adhesion based on the amount of movement of the lung region in the axial direction or a specific direction in the dynamic image of the front chest has been described as an example. Information on pleural adhesion may be derived based on the amount of movement of the lung region in the image in the body axis direction or in a specific direction.

In the above-described embodiment, the display unit 34 is used as an output unit to display information about pleural adhesion on the display unit 34. However, for example, the output unit is used as the communication unit 35 to display information about pleural adhesion. may be output to an external device by the communication unit 35, and the information on the pleural adhesion may be displayed on the external device. Further, the diagnosis console 3 may be configured to include a printing unit, and the printing unit may output information on pleural adhesion on paper.

In addition, in the above description, an example using a hard disk, a semiconductor non-volatile memory, or the like is disclosed as a computer-readable medium for the program according to the present invention, but the present invention is not limited to this example. As another computer-readable medium, a portable recording medium such as a CD-ROM can be applied. A carrier wave is also applied as a medium for providing program data according to the present invention via a communication line.

In addition, the detailed configuration and detailed operation of each device that constitutes the mobile radiographic imaging device can be changed as appropriate without departing from the scope of the present invention.

100 Dynamic analysis system 1 Imaging device 11 Radiation source 12 Radiation irradiation control device 13 Radiation detection unit 14 Reading control device 2 Imaging console 21 Control unit 22 Storage unit 23 Operation unit 24 Display unit 25 Communication unit 26 Bus 3 Diagnosis console 31 Control Unit 32 Storage unit 33 Operation unit 34 Display unit 35 Communication unit 36 Bus

Claims (20)

an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement of the lung region in the body axis direction in the dynamic image;
an output unit that outputs the derived information about the adhesion;
A dynamic image analysis device comprising an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement in a specific direction of the lung region in the dynamic image;
an output unit that outputs the derived information about the adhesion;
A dynamic image analysis device comprising 3. The derivation unit, by analyzing the dynamic image, sets the movement direction of the lung region in the dynamic image as the specific direction, and derives information about pleural adhesion based on the amount of movement in the specific direction. The dynamic image analysis device described. 3. The dynamic image analysis apparatus according to claim 2, wherein the derivation unit uses a direction specified by a user operation as the specific direction, and derives information about pleural adhesion based on the amount of movement in the specific direction. The dynamics according to any one of claims 1 to 4, wherein the information about the adhesion includes at least one of information indicating the presence or absence of the adhesion, information indicating the strength of the adhesion, and information indicating the likelihood of the adhesion. Image analysis device. The dynamic image analysis apparatus according to any one of claims 1 to 5, wherein the information on adhesion includes the amount of movement for each small region of the lung region in the dynamic image. The derivation unit derives information about the adhesion for each small region based on the movement amount for each small region of the lung region in the dynamic image,
The dynamic image analysis apparatus according to any one of claims 1 to 6, wherein the output unit outputs information regarding the adhesion for each of the small regions in the dynamic image. 8. The dynamic image analysis apparatus according to claim 7, wherein the information about the adhesion for each small area includes information as to whether or not the movement amount for each small area of the lung area is equal to or less than a predetermined threshold. 8. The dynamic image analysis apparatus according to claim 7, wherein the output unit outputs an image added with a color corresponding to the information on the adhesion for each of the small regions of the representative frame image of the dynamic image. 8. The dynamic image analysis apparatus according to claim 7, wherein the output unit outputs an image displaying a vector indicating the motion amount for each of the small regions of the representative frame image of the dynamic image. 8. The dynamic image analysis apparatus according to claim 7, wherein the output unit outputs an image in which a color corresponding to the motion amount is added to each of the small regions of the representative frame image of the dynamic image. The derivation unit derives a representative value of the amount of movement for each small region at each vertical position of each lung region in the dynamic image,
12. The output unit according to claim 11, wherein the output unit outputs an image in which the vicinity of each lung region of the representative frame image of the dynamic image is colored according to the representative value for each vertical position of each lung region. dynamic image analysis equipment. The derivation unit derives information about the adhesion for each of the left and right lungs in the dynamic image,
The dynamic image analysis apparatus according to any one of claims 1 to 12, wherein the output unit outputs information regarding the adhesion for each of the left and right lungs. 14. The dynamic image analysis apparatus according to claim 13, wherein the information about the adhesion derived by the deriving unit for each of the left and right lungs includes a numerical value related to a reduced motion region in which the amount of motion in the lung region is equal to or less than a predetermined threshold. The information regarding the adhesion derived by the derivation unit for each of the left and right lungs includes regions in which the amount of movement in the lung region is equal to or less than a predetermined first value and values equal to or greater than a second value greater than the first value. 15. The dynamic image analysis apparatus according to claim 13 or 14, wherein the distance to the region or the movement amount in the lung region includes at least one of the maximum movement amount in a region within a predetermined range from a region below a predetermined threshold. . 16. The dynamic image analysis apparatus according to any one of claims 1 to 15, wherein the derivation unit further derives the information regarding the adhesion based on the amount of movement of the diaphragm in the dynamic image. 17. The output unit according to any one of claims 1 to 16, wherein the output unit outputs information warning that the accuracy of the information on the adhesion is low when the movement amount of the diaphragm in the dynamic image is equal to or less than a predetermined threshold. dynamic image analysis equipment. When the specific direction is the direction of movement at a specific point of the ribcage, the output unit warns that the accuracy of the information on the adhesion is low when the amount of movement of the specific point in the dynamic image is equal to or less than a predetermined threshold. 5. The dynamic image analysis apparatus according to any one of claims 2 to 4, which outputs information to be performed. the computer,
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a deriving unit that derives information about pleural adhesion based on the amount of movement of the lung region in the body axis direction in the dynamic image;
an output unit that outputs information about the derived adhesion;
A program to function as the computer,
an acquisition unit that acquires a dynamic image of the chest obtained by radiographic dynamic imaging;
a derivation unit that derives information about pleural adhesion based on the amount of movement in a specific direction of the lung region in the dynamic image;
an output unit that outputs information about the derived adhesion;
A program to function as

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