Dynamic image analysis apparatus, method, and program

Abstract

To quantitatively evaluate, in terms of micro, a spot of a lung such as a lung structure by a dynamic image obtained by dynamic imaging with a less exposure.SOLUTION: A dynamic image analysis apparatus in one embodiment of a disclosure comprises: an acquisition unit which acquires a dynamic image of an imaging object by dynamic imaging; and a calculation unit which calculates, from the dynamic image, two displacement amount data on a region obtained by dividing a static image constituting the dynamic image and variation data on the density of the region.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] This disclosure relates to a dynamic image analysis apparatus, method, and program. [Background technology]

[0002] Dynamic imaging is performed in which a radiation generator emits radiation pulses repeatedly at a pulse cycle (e.g., 15 times per second) for a predetermined period of time (duration) while the radiation instruction is given, and a radiation detection device reads out the amount of charge generated according to the dose of radiation received through the subject as a signal value (intensity). Dynamic imaging captures a dynamic image consisting of multiple (series of) still images, each captured at a different pulse cycle. The period for capturing still images is called the frame rate and is equal to the period of the radiation pulse. Based on the captured dynamic images, physicians make diagnoses of diseases. Physicians can make diagnoses based on the movement of organs such as the lungs and heart by using dynamic imaging of these organs. Physicians can also make diagnoses based on the movement of joints by using dynamic imaging of bones.

[0003] As a method for quantitatively evaluating the movement of the lungs as a whole (macroscopically), such as the lung fields, an index value indicating changes in the lung fields is calculated from dynamic images of the chest, and the flexibility of the lung fields is evaluated based on the calculated index value (for example, Patent Document 1).

[0004] Furthermore, by improving the accuracy of estimating the volume of moving subjects in radiographic images, the accuracy of estimating functional indicators of subjects, which are estimated based on the volume of the subjects, is improved. Currently, the volume of the lung field is estimated based on the extracted frame images for each set, and respiratory function indicators of the lung field are estimated based on the estimated volume (for example, Patent Document 2). [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2017-176202 [Patent Document 2] Japanese Patent Publication No. 2019-122449 [Overview of the project] [Problems that the invention aims to solve]

[0006] Currently, in the treatment of emphysema (emphysematous COPD), pulmonary function tests are performed to diagnose and assess its progression. Furthermore, chest CT imaging is commonly used as a more detailed examination. Chest CT allows for the quantitative evaluation of the areas and volume of lung structures, such as alveoli, that appear black due to destruction, thereby confirming the progression of emphysema.

[0007] However, CT scans involve a high level of radiation exposure. In particular, detailed evaluation of emphysema requires CT scans in two phases, inhalation and exhalation, which significantly increases radiation exposure.

[0008] Dynamic imaging using low-radiation exposure techniques is needed to quantitatively evaluate the deformation of lung structures associated with respiratory movement at a microscopic level, for example, by evaluating localized deformation and its quantitative effect. [Means for solving the problem]

[0009] A dynamic image analysis device in one embodiment of the present disclosure includes an acquisition unit that acquires a dynamic image including the lungs by dynamic imaging using radiation, and two displacement data relating to the displacement of the position of the second region relative to the first region, based on a comparison between a first region set in the first still image and a second region corresponding to the first region in the second still image, in at least a first still image and a second still image that constitute the dynamic image. and Change amount data relating to the change in concentration in the first region and the second region , as a three-dimensional change in lung structure It comprises a calculation unit that performs calculations.

[0010] A dynamic image analysis method in one embodiment of the present disclosure includes an acquisition step of acquiring a dynamic image including the lungs by dynamic radiography, and two displacement data relating to the displacement of the position of the second region relative to the first region, based on a comparison between a first region set in the first still image and a second region corresponding to the first region in the second still image, in at least a first still image and a second still image constituting the dynamic image. and Change amount data relating to the change in concentration in the first region and the second region , as a three-dimensional change in lung structure It comprises a calculation step for performing calculations.

[0011] A dynamic image analysis program in one embodiment of the present disclosure includes an acquisition step of acquiring a dynamic image including the lungs by dynamic radiography using radiation, and two displacement data relating to the displacement of the position of the second region relative to the first region, based on a comparison between a first region set in the first still image and a second region corresponding to the first region in the second still image, in at least a first still image and a second still image constituting the dynamic image. and Change amount data relating to the change in concentration in the first region and the second region , as a three-dimensional change in lung structure The calculation steps to be performed are executed.

[0012] These comprehensive or specific embodiments may be implemented as systems, devices, methods, integrated circuits, computer programs, or recording media, or as any combination of systems, devices, methods, integrated circuits, computer programs, and recording media. [Effects of the Invention]

[0013] According to this disclosure, by quantitatively evaluating localized areas of the lung (microscopically) using dynamic images, it is possible to provide information for determining the progression of a disease. [Brief explanation of the drawing]

[0014] [Figure 1] Diagram showing the configuration of a motion image analysis device. [Figure 2] Functional block diagram of the processing circuit [Figure 3] A diagram that models the structure of the lungs by relating them to a cube. [Figure 4A] This figure shows a static image of the lungs when they are enlarged, among the dynamic images of the lungs. [Figure 4B] This figure shows a static image of the lungs when they are small, as part of dynamic imaging of the lungs. [Figure 5A] A diagram showing regions obtained by dividing the reference image into 50-pixel intervals. [Figure 5B] A diagram showing a displaced region in a still image separate from the reference image. [Figure 5C] A diagram showing a displaced region in a still image separate from the reference image. [Figure 6] A diagram showing a flowchart of the processing circuit. [Figure 7] A diagram showing a detailed flowchart of the processing circuit. [Figure 8] This figure shows an example of an elastic modulus map displaying the elastic modulus for WIRE, the X, Y, and Z axes, and the combined elastic modulus, both region by region and after smoothing. [Figure 9] This figure shows an example of a strain map displaying the distortion of WIRE, the X-axis, Y-axis, Z-axis, and combined strain, both region by region and after smoothing. [Figure 10A] This figure shows a state where the softness of the lung structure decreases, the elastic modulus increases, and the strain ε relative to stress σ decreases. [Figure 10B] This diagram shows the case where the modulus of elasticity differs along the X, Y, and Z axes. [Figure 10C] A diagram showing the severity of the condition based on its slope. [Figure 11] A diagram showing a window displaying buttons for doctors to select the diseases to be displayed. [Figure 12] This figure shows an example of displaying the elastic modulus E on the 3rd, 4th, and 5th days of hospitalization. [Modes for carrying out the invention]

[0015] The embodiments of this disclosure will be described in detail below with reference to the drawings as appropriate.

[0016] <Dynamic Image Analysis System> The configuration of a dynamic image analysis device 100 according to one embodiment of this disclosure will be described.

[0017] [composition] Figure 1 shows the configuration of the motion image analysis device 100.

[0018] The motion image analysis device 100 includes a processing circuit 110, an input / output unit 120, a communication unit 130, and a memory 140. The input / output unit 120 includes an input unit 121 and an output unit 122. The input unit 121 and the output unit 122 may be integrated. If input and output are performed via the communication unit 130, the input / output unit 120 may be omitted.

[0019] The processing circuit 110 consists of a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), etc., and may also include a neural network. Based on the input medical image, the processing circuit 110 extracts features and estimates the disease level of a specific disease. Details of the processing circuit 110 will be described later.

[0020] The input unit 121 includes at least one of the following: a touch panel, keyboard, mouse, microphone, etc., and input is received based on the user's (doctor, radiologist, etc.) actions.

[0021] The output unit 122 includes at least one of the following: a display, a speaker, a printer, etc., and outputs the result determined by the processing circuit 110 to the outside.

[0022] The communication unit 130 communicates with external devices wirelessly or via wired connections, using buses, LANs (Local Area Networks), the Internet, VPNs (Virtual Private Networks), public lines, etc. The communication unit 130 communicates with hospital information systems (HIS), radiology information systems (RIS), picture archiving and communication systems (PACS), dynamic analysis devices, etc.

[0023] Memory 140 consists of ROM (Read Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically EPROM), HDD (Hard Disk Drive), etc., and stores dynamic images, various programs, etc.

[0024] Figure 2 is a functional block diagram of the processing circuit 110.

[0025] The processing circuit 110 includes an acquisition unit 111 and a calculation unit 112.

[0026] The acquisition unit 111 acquires dynamic images. Dynamic images may be acquired from an external system such as a RIS via the communication unit 130 based on external input via the input unit 121 or the communication unit 130, or they may be acquired from the memory 140. The dynamic image is, for example, a dynamic image of the lungs.

[0027] The calculation unit 112 calculates the distortion and elastic modulus of the captured lung based on the dynamic image acquired by the acquisition unit 111. The calculated distortion and elastic modulus are then displayed on the output unit 122 or on an external device via the communication unit 130. Further details will be described later.

[0028] [Modeling of the lungs] Figure 3 is a diagram that models the structure of the lung by relating it to a cube. In actual lungs, there are many more lung structures along the X, Y, and Z axes. Dynamic images are acquired by a dynamic imaging device that repeatedly irradiates pulsed radiation along the Z axis and acquires the transmitted radiation as a still image in the XY plane (Z plane).

[0029] First, let's consider the changes in the size of the lung structure during respiration. Since the size of the lung structure changes with respiration, in Figure 3, we can think of it as the size of the cube changing.

[0030] Figure 4 shows a still image from among the dynamic images of the lung. For example, it shows the state when radiation is irradiated from the Z-axis direction in Figure 3, and the XY plane is captured.

[0031] Figure 4A shows a static image of the lung in a state where the lung is large (e.g., maximum inspiration). For example, it shows the right lung divided into segments at predetermined intervals. For example, a static image of the lung in a large state is set as a reference image. Figure 4B shows a static image of the lung in a state where the lung is small (e.g., maximum expiration). A static image of the lung in a small state may also be set as a reference image. In Figure 4B, the same region as the reference image is divided into multiple segments, but because the lung is small, the overall size of the segments is smaller. In other words, over time, the lung changes from a state where it is large (Figure 4A) to a state where it is small (Figure 4B).

[0032] The size of the lung structure varies depending on its flexibility (modulus of elasticity). For example, if the lung structure is rigid (high modulus of elasticity), it does not contract much, so the difference between the size of the large lung structure and the small lung structure is small. If the lung structure is flexible (low modulus of elasticity), it contracts greatly, so the difference between the size of the large lung structure and the small lung structure is large.

[0033] Therefore, by comparing the size of the lungs in a series of still images that make up a dynamic image, it is possible to understand the flexibility of the lung structure.

[0034] Thus, by comparing FIGS. 4A and 4B, the softness of the lung structure in the X-axis and Y-axis can be grasped, but the softness of the lung in the Z-axis cannot be grasped.

[0035] For example, in interstitial pneumonia, the lung structure becomes fibrotic, so it contracts in one direction but not in another. Therefore, it is necessary to grasp the softness of the lung structure in all three-dimensional directions.

[0036] [Elastic modulus] The stress that deforms an object is σ , , , zy ,

[0037] ,

[0038] , , , , , , , , , , , zx , , , , σ yy , σ zz , σ xy , σ xz , σ yx , σ yz , σ zx , σ zy and is defined as nine stress components. σ xx , σ yy , σ zz are the normal stresses with respect to the X-plane (YZ plane), Y-plane (XZ plane), and Z-plane (XY plane), respectively, and σ xy , σ xz , σ yx , σ yz , σ zx , σ zy are shear stresses.

Number

[0037] According to Hooke's law, the elastic modulus E has the relationship of σ = Eε That is, looking at the stress components

Number

[0038] Since the change in size of the lung structure only requires considering vertical strain, we can consider only vertical stress and assume that shear stress is 0. Therefore, in this disclosure,

number

number

[0039] Distortion ε xx , ε yy , ε zz These represent the distortions along the X, Y, and Z axes, respectively, and therefore represent the change in the size of the lung structure Δ. x , Δ y , Δ z That is the case.

[0040] Therefore, the stress σ in the X, Y, and Z axes xx , σ yy , σ zz and displacement Δ x , Δ y , Δ z If we know this, the modulus of elasticity E x , E y , E z It is possible to find this.

[0041] [Displacement amount] In dynamic imaging of the lungs, changes in the image are presumed to be due to changes in lung structure. By detecting changes in the image based on optical flow, changes in lung structure can be measured.

[0042] Since dynamic imaging consists of multiple still images, the displacement of the lungs in the X and Y axes (Δ) can be determined by comparing the position of the lungs in each still image. x and Δ y It is possible to calculate ).

[0043] First, one still image from the dynamic image is used as the reference image. Figure 5A shows regions A, B, C, and D obtained by dividing the reference image into, for example, 50-pixel intervals. Figure 5B shows the state in which regions A, B, C, and D have been displaced into regions E, F, G, and H in a still image different from the reference image. The size of the division may correspond to the size of one lung structure, or it may be set to a different size depending on the processing power and image quality.

[0044] In this disclosure, we determine the strain as a change in the size and shape of the region. Since the strain is a change in the positional relationship between each point, we need to determine how much points F, G, and H have been displaced relative to point E with respect to the positions of points B, C, and D relative to point A. Since region ABCD is a small region, region EFGH is approximated as a parallelogram, and Δ x and Δ y It may be required.

[0045] Figure 5C shows an example of calculating the displacement for each point F, G, and H. The average of the displacements with respect to the X axis and the average of the displacements with respect to the Y axis for points F, G, and H is Δ x and Δ y It may also be calculated as follows: If the sum of the displacements of points E, F, G, and H is divided by 2, then the displacement shown in Figure 5B is obtained, and the Δ calculated in Figure 5B is obtained as follows. x and Δ y This will have the same value.

[0046] The reference image may be the very first still image in a series of still images that make up the dynamic image.

[0047] As described above, when region ABCD is displaced to region EFGH, the displacement amount Δ x and Δ y It is possible to find this.

[0048] On the other hand, Δ z This can be determined from the change in concentration.

[0049] The surface of the lung structure has capillaries, and blood flows through these capillaries. The density of capillaries changes with changes in the size of the lung structure. As the lung structure becomes smaller, the density of capillaries increases, and as the lung structure becomes larger, the density of capillaries decreases. A change in capillary density means a change in blood density, and this change in blood density is detected as a change in density (rate of change or density difference) in a still image. The density of region A, B, C, and D may be calculated as the average density of the entire region, or as the average density of points A, B, C, and D.

[0050] By detecting the change in concentration in region ABCD, the displacement amount Δ in the Z-axis direction can be determined. z The following can be calculated. Since the density of a still image is the received intensity of radiation, the amount of charge (signal value) detected by a radiation detector having multiple detection elements may be used as the density. Assuming that the lung structure is spherical and that the Z-axis direction is the same size as the X and Y axes in the reference image, the density of the reference image is the density when the Z-axis direction is 50 pixels, so the displacement Δ can be calculated from the rate of change. z It is possible to find this.

[0051] Since the displacement Δ is the strain ε, according to Hooke's Law (σ=Eε), if we can determine the stress σ, we can determine the modulus of elasticity E.

[0052] [stress] Respiratory motion is the motion of air, a fluid. Since changes in lung size are based on respiratory motion, changes in lung size are based on dynamic pressure. According to Berne's theorem, dynamic pressure q is given by, with respect to density ρ and fluid velocity v, q=pv 2 / 2 That is the case.

[0053] In this disclosure, dynamic pressure q is stress σ, density ρ is gas density P, and fluid velocity v is moving velocity V, σ = PV 2 / 2 They have a relationship.

[0054] stress σ xx, σ yy , σ zz These are the normal stresses in the X, Y, and Z planes, and therefore the stresses in the X, Y, and Z axes, respectively. The gas density P is decomposed into X, Y, and Z axis components. x , P y , P z , V when the movement speed V is decomposed into the X, Y, and Z axes. x , V y , V z Similarly, σ xx =P x V x 2 / 2 σ yy =P y V y 2 / 2 σ zz =P z V z 2 / 2 They have a relationship.

[0055] In this disclosure, the lung structure expands when air enters the lungs, and contracts when air leaves the lungs. In other words, the change in lung structure is caused by the stress of air. Since the stress due to air is equal in the X, Y, and Z axes, σ xx =σ yy =σ zz That is the case.

[0056] Here, since the density P of air is 1, if we know the velocity V of air movement, we can determine the stress σ. xx , σ yy , σ zz It is possible to find ,

[0057] For example, if a patient is on a ventilator, the air velocity V is the flow rate set on the ventilator. Alternatively, the amount of diaphragmatic movement can be detected based on dynamic imaging, and the air velocity V can be determined based on the amount of diaphragmatic movement. In this way, the air velocity V for both exhalation and inhalation can be determined, and therefore the stress σ can be calculated.

[0058] [process] Figure 6 is a flowchart of the processing circuit 110.

[0059] The acquisition unit 111 acquires a dynamic image (step S601).

[0060] The calculation unit 112 calculates the displacement amounts of the X and Y axes based on the dynamic image acquired by the acquisition unit 111. The calculation unit 112 also calculates the displacement amount of the Z axis based on the density difference of the still images that make up the dynamic image (step S602).

[0061] The calculation unit 112 calculates the modulus of elasticity of the X, Y, and Z axes from the displacement amounts of the X, Y, and Z axes (step S603).

[0062] The calculation unit 112 displays the displacement amounts and modulus of elasticity of the X, Y, and Z axes calculated by the calculation unit 112 on the output unit 122 (step S604). Alternatively, a display control unit (not shown) within the processing circuit 110 may display the information on the display unit instead of the calculation unit 112. The displacement amounts of the X, Y, and Z axes may be displayed between steps S602 and S603.

[0063] Figure 7 is a diagram showing a detailed flowchart of the processing circuit 110.

[0064] The acquisition unit 111 acquires a dynamic image (step S701).

[0065] The calculation unit 112 extracts a still image of maximum inhalation and a still image of maximum exhalation from the dynamic images acquired by the acquisition unit 111 (step S702).

[0066] The calculation unit 112 determines either the still image of maximum inhalation or the still image of maximum exhalation as the reference image, and sets regions of 50 x 50 pixels in the reference image (step S703).

[0067] The calculation unit 112 calculates the X-axis and Y-axis distortion ε for each region based on the optical flow relative to the reference image. x and ε yCalculate it (step S704).

[0068] The calculation unit 112 calculates the strain ε of the Z axis based on the change rate of the signal value (amount of charge) for each region. z Calculate it (step S705). Assuming that the lung structure is spherical and that the Z-axis direction has the same size as the X-axis and Y-axis in the reference image, since the density of the reference image is the density when the Z-axis direction is 50 pixels, the strain ε can be calculated from the change rate. z can be calculated.

[0069] The calculation unit 112 generates a strain map based on the ε x and ε y calculated in step S704 and the ε z calculated in step S705 (step S706). The strain map is a map showing the strain ε x of the X axis, the strain ε y of the Y axis, and the strain ε z of the Z axis for each region.

[0070] The calculation unit 112 calculates the movement amount of the diaphragm based on the dynamic image. The calculation unit 112 calculates the movement speed v of the diaphragm based on the calculated movement amount and the frame rate of the dynamic image (step S707). Instead of calculating the movement speed v of the diaphragm, the calculation unit 112 may acquire the flow rate set in the ventilator. The flow rate set in the ventilator may be acquired from the ventilator via the communication unit 130, may be acquired from the patient's electronic medical record, may be acquired from the memory 140, or may be acquired by other means.

[0071] The calculation unit 112 calculates the stress σ x , σ y , σ z from the movement speed v of the diaphragm or the flow rate set in the ventilator (step S708).

[0072] The calculation unit 112 calculates the strain ε x , ε y , and ε zand the stress σ calculated in step S708 x σ y σ z Based on σ, the modulus of elasticity E x E y E z is calculated (step S709).

[0073] The calculation unit 112 generates an elastic modulus map based on the modulus of elasticity E x E y E z calculated in step S709 (step S710). The elastic modulus map is a map showing the modulus of elasticity E x on the X-axis, the modulus of elasticity E y on the Y-axis, and the modulus of elasticity E z on the Z-axis for each region.

[0074] The calculation unit 112 calculates the combined strain ε x ε y ε z based on ε. Further, the calculation unit 112 calculates the combined modulus of elasticity E xyz based on the modulus of elasticity E x E y E z E xyz (step S711). The calculated combined strain ε xyz and the combined modulus of elasticity E xyz are calculated as maps.

[0075] [Display] By obtaining the strain ε (displacement amount Δ) and the stress σ, the modulus of elasticity E can be obtained.

[0076] Since the strain ε and the stress σ are obtained for each region ABCD obtained by dividing the reference image constituting the dynamic image, the modulus of elasticity E is obtained for each divided region.

[0077] The obtained strain ε on the X-axis, Y-axis, and Z-axis is displayed for each region (strain map).

[0078] Also, the obtained modulus of elasticity E on the X-axis, Y-axis, and Z-axis x E y Ez Display the modulus of elasticity for each region (elastic modulus map).

[0079] The calculated strain or elastic modulus may be normalized by setting the strain or elastic modulus of a normal lung structure to 1. By normalizing the strain or elastic modulus of a normal lung structure to 1, the severity of the lung structure can be easily assessed.

[0080] The color of the strain or modulus displayed on the X, Y, and Z axes may be changed. For example, the X axis may be displayed in red, the Y axis in green, and the Z axis in blue. The color (intensity) may also be changed depending on the strain or modulus value of each region. For example, regions with high strain or modulus on the X axis may be displayed in dark red (e.g., crimson), and regions with low strain or modulus on the X axis may be displayed in light red (e.g., pink). For example, regions with high strain or modulus on the Y axis may be displayed in dark green (e.g., deep green), and regions with low strain or modulus on the Y axis may be displayed in light green (e.g., light green). For example, regions with high strain or modulus on the Z axis may be displayed in dark blue (e.g., indigo), and regions with low strain or modulus on the Z axis may be displayed in light blue (e.g., cyan).

[0081] The combined strain or elastic modulus (combined elastic modulus) obtained by combining the strain or elastic modulus of the X, Y, and Z axes may be displayed. For example, the combined strain or elastic modulus may be displayed in purple, with areas of high combined strain or elastic modulus displayed in dark purple (e.g., dark color) and areas of low combined strain or elastic modulus displayed in light purple (e.g., wisteria color). The combination may be performed by vector addition, or by calculating the average of the strain or elastic modulus of the X, Y, and Z axes (by dividing the sum by 3).

[0082] The average elastic modulus of the region belonging to the upper leaf, the average elastic modulus of the region belonging to the middle leaf, and the average elastic modulus of the region belonging to the lower leaf can be calculated and the elastic modulus can be overlaid on the corresponding locations.

[0083] In addition to displaying each region, the results of smoothing the regions may also be displayed.

[0084] By displaying the strain or elastic modulus for each region, it is possible to understand the condition of each part of the lung.

[0085] Additionally, a WIRE (Wall-In-Return) display may be used to show how the region obtained by dividing the reference image into 50-pixel intervals in the XY plane is distorted. The still image used to display the WIRE may be the still image exhibiting the greatest distortion.

[0086] Figure 8 shows an example of an elastic modulus map displaying the WIRE, X-axis, Y-axis, Z-axis, and combined elastic modulus for each region and the results after smoothing. The average elastic modulus may be omitted if necessary. WIRE directly represents the strain, and the distorted state is shown as is, as in Figure 5C. The upper panel displays the results for each region, and the lower panel displays the results after smoothing. Figure 8 shows an example where the average elastic modulus of the region belonging to the upper lobe, the region belonging to the middle lobe, and the region belonging to the lower lobe are displayed overlapping for the X-axis. The average elastic modulus of the upper, middle, and lower lobes may also be displayed overlapping for the Y-axis, Z-axis, and combined elastic modulus. The average elastic modulus displayed in overlapping mode may be the elastic modulus normalized with the elastic modulus of a normal lung structure set to 1.

[0087] Figure 9 shows an example of a strain map displaying the WIRE and the strains of the X, Y, and Z axes, both region by region and after smoothing. The upper panel displays the strains by region, and the lower panel displays the results after smoothing. The average strains of the upper, middle, and lower leaves for the X, Y, and Z axes may also be displayed as an overlap.

[0088] Figure 10 shows the relationship between strain ε and stress σ. In Figure 10, the elastic modulus for a normal lung (lung structure) is normalized to 1. Normalization may also be the average elastic modulus for a normal lung structure, or the upper limit of the elastic modulus. Figure 10A shows a state where the softness of the lung structure decreases, the elastic modulus increases, and the strain ε relative to stress σ decreases. Figure 10B shows the case where the elastic modulus differs for the X, Y, and Z axes. The severity of the disease can be determined by the slope of the graph (magnitude of the elastic modulus). Figure 10C shows a graph displaying the severity based on the slope. For example, region 1001 shows the normal range, region 1002 shows the mild range, and region 1003 shows the severe range. For example, region 1001 may be displayed in blue, region 1002 in yellow, and region 1003 in red. The number of regions is not limited to three. The severe / mild / normal ranges may be set according to the disease. In Figure 10, the elastic modulus may be shown for each of the upper, middle, and lower lobes. Figure 10C shows that the upper lobe is severely affected, the middle lobe is mildly affected, and the lower lobe is normal. Figure 10 may also be shown in the lower part of the WIRE in Figures 8 and 9.

[0089] [disease] The patient has been diagnosed by other means with what disease they have, for example, COPD, interstitial pneumonia, or pneumothorax. The doctor can input the patient's disease from the input unit 121, and the output unit 122 will display information corresponding to the disease.

[0090] Figure 11 shows a window 1100 displaying buttons for a physician to select the diseases to be displayed. Window 1100 may not display all diseases, or it may display other diseases, and the number of displayed buttons may not be limited to four. For example, if a health checkup is entered, the dynamic image analysis device 100 may display information for multiple diseases.

[0091] [COPD] In patients with COPD, a condition called air-trapping can be observed, where multiple lung structures become emphysematous, meaning air cannot be pushed out of the lung structures. Because air-trapping prevents the lung structures from contracting, their flexibility decreases, and their elasticity increases. In other words, the higher the elasticity (the lower the flexibility), the more severe the COPD condition is judged to be.

[0092] Therefore, physicians can diagnose the condition of COPD in patients using the diagrams shown in Figures 8, 9, and 10.

[0093] [Interstitial pneumonia] In patients with interstitial pneumonia, fibrosis of the lung structure can be observed. Due to fibrosis of the lung structure, it becomes more difficult for the lung structure to contract in certain directions. In other words, in dynamic images of patients with interstitial pneumonia, as shown in Figure 10B, there are differences in flexibility (elastic modulus) along the X, Y, and Z axes.

[0094] Therefore, physicians can understand the condition of interstitial pneumonia in patients by referring to the diagrams in Figures 8, 9, and 10B.

[0095] [pneumothorax] In patients with pneumothorax (lung collapse), air leaks into the pleural cavity, compressing the lung and causing it to collapse. Consequently, the lung structure in the compressed area experiences a decrease in strain ε, and therefore its elastic modulus increases.

[0096] Therefore, physicians can diagnose the condition of pneumothorax in patients using the diagrams shown in Figures 8, 9, and 10.

[0097] [Treatment status] Because dynamic imaging involves minimal radiation exposure to the patient (subject), multiple dynamic imaging sessions can be performed on the patient. Therefore, by comparing the elastic modulus E according to the treatment status, it is possible to understand the degree of disease improvement due to the treatment effect. Figure 12 shows an example of displaying the elastic modulus E on the 3rd, 4th, and 5th days of hospitalization. The elastic modulus E to be displayed is Ex , E y , E z , E xyz Any of these may be used, or multiple elastic moduli may be used. When the dynamic image analysis device 100 displays multiple elastic moduli, for example, it may display them in different colors for each of the 3rd, 4th, and 5th days of hospitalization, or E x , E y , E z , E xyz Each item may be displayed in a different color. The display in Figure 12 allows the doctor to determine whether the patient's condition is improving. By understanding the progress of the disease's condition, the doctor can decide on future treatment plans.

[0098] While embodiments have been described above with reference to the drawings, this disclosure is not limited to such examples. It will be apparent to those skilled in the art that various modifications or alterations can be conceived within the scope of the claims. Such modifications or alterations are also understood to fall within the technical scope of this disclosure. Furthermore, the components in the embodiments may be combined in any way without departing from the spirit of this disclosure.

[0099] (1) A motion image analysis device in one embodiment of the present disclosure includes an acquisition unit that acquires a motion image of a subject by motion photography, and a calculation unit that calculates two displacement amount data and change amount data relating to the density of the region from the motion image.

[0100] (2) In one embodiment of the present disclosure, the image determination device is the image determination device of (1), wherein the density is the signal value of the motion image.

[0101] (3) In one embodiment of the present disclosure, the image determination device is the image determination device of (2), wherein the two displacement data relating to the region are distortion in the X-axis direction and distortion in the Y-axis direction, which are the distortion of the region over time, and the change data relating to the density of the dynamic image is the rate of change of charge detected by the radiation detection device over time.

[0102] (4) In one embodiment of the present disclosure, the image determination device in (3) is configured such that the calculation unit calculates the strain in the Z-axis direction based on the rate of change of the charge.

[0103] (5) In one embodiment of the present disclosure, the image determination device is the image determination device of (4), wherein the calculation unit determines the elastic modulus in the X-axis direction based on the strain in the X-axis direction, determines the elastic modulus in the Y-axis direction based on the strain in the Y-axis direction, and determines the elastic modulus in the Z-axis direction based on the strain in the Z-axis direction.

[0104] (6) In one embodiment of the present disclosure, the image determination device is the image determination device of (5), wherein the object to be photographed is an organ.

[0105] (7) In one embodiment of the present disclosure, the image determination device is the image determination device of (6), wherein the organ is the lung.

[0106] (8) In one embodiment of the present disclosure, the image determination device is the image determination device of (4), wherein the calculation unit causes the X-axis distortion, the Y-axis distortion, and the Z-axis distortion to be displayed in different colors on the display unit.

[0107] (9) In one embodiment of the present disclosure, the image determination device in (8) displays the distortion in the X-axis direction, the distortion in the Y-axis direction, and the distortion in the Z-axis direction with a density corresponding to the distortion.

[0108] (10) In one embodiment of the present disclosure, the image determination device in (4) calculates a combined distortion of the distortion in the X-axis direction, the distortion in the Y-axis direction, and the distortion in the Z-axis direction.

[0109] (11) In one embodiment of the present disclosure, the image determination device is the image determination device of (5), wherein the calculation unit causes the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction to be displayed on the display unit in different colors.

[0110] (12) In one embodiment of the present disclosure, the image determination device in (11) displays the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction with a density corresponding to the elastic modulus.

[0111] (13) In one embodiment of the present disclosure, the image determination device in (5) calculates an elastic modulus obtained by combining the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction.

[0112] (14) In one embodiment of the present disclosure, the image determination device in (5) displays the normal modulus of elasticity and the calculated modulus of elasticity.

[0113] (15) In one embodiment of the present disclosure, the image determination device is the image determination device of (5), wherein the calculation unit displays the elastic modulus of a plurality of dynamic images.

[0114] (16) A motion image analysis method in one embodiment of the present disclosure comprises an acquisition step of acquiring a motion image of a subject by motion photography, and a calculation step of calculating two displacement amount data and change amount data relating to the density of the region from the motion image.

[0115] (17) A dynamic image analysis program in one embodiment of the present disclosure causes a computer to perform an acquisition step of acquiring a dynamic image of a subject to be photographed by dynamic photography, and a calculation step of calculating two displacement amount data and change amount data relating to the density of the region from the dynamic image. [Industrial applicability]

[0116] This disclosure is useful for dynamic image analysis devices, methods, and programs. [Explanation of symbols]

[0117] 100 Dynamic Image Analysis Device 110 Processing Circuit 111 Acquisition Department 112 Calculation Section 120 Input / output section 121 Input section 122 Output section 130 Communications Department 140 memory 1100 Window

Claims

1. An acquisition unit that acquires dynamic images including the lungs by dynamic imaging using radiation, A calculation unit calculates two displacement data points relating to the positional displacement of the second region relative to the first region and change data points relating to the change in concentration between the first and second regions, based on a comparison between a first region set in the first still image and a second region corresponding to the first region in the second still image, as a three-dimensional change in the lung structure, in at least one first still image and a second still image constituting the dynamic image. A dynamic image analysis device equipped with the following features.

2. The aforementioned concentration is a value corresponding to the received radiation intensity. The dynamic image analysis apparatus according to claim 1.

3. The two displacement data mentioned above represent the distortion in the X-axis direction and the distortion in the Y-axis direction, where the first region is distorted into the second region over time. The aforementioned change amount data is the rate of change or difference in charge detected by the radiation detection device over time. The dynamic image analysis device according to claim 2.

4. The calculation unit calculates the strain in the Z-axis direction based on the rate of change of the charge. The dynamic image analysis apparatus according to claim 3.

5. The calculation unit calculates the modulus of elasticity in the X-axis direction based on the strain in the X-axis direction, calculates the modulus of elasticity in the Y-axis direction based on the strain in the Y-axis direction, and calculates the modulus of elasticity in the Z-axis direction based on the strain in the Z-axis direction. The dynamic image analysis apparatus according to claim 4.

6. The calculation unit causes the distortion in the X-axis direction, the distortion in the Y-axis direction, and the distortion in the Z-axis direction to be displayed on the display unit in different colors. The dynamic image analysis apparatus according to claim 4.

7. The calculation unit displays the distortion in the X-axis direction, the distortion in the Y-axis direction, and the distortion in the Z-axis direction with a density corresponding to the distortion. The dynamic image analysis apparatus according to claim 6.

8. The calculation unit calculates a combined strain of the strain in the X-axis direction, the strain in the Y-axis direction, and the strain in the Z-axis direction. The dynamic image analysis apparatus according to claim 4.

9. The calculation unit displays the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction in different colors on the display unit. The dynamic image analysis device according to claim 5.

10. The calculation unit displays the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction with a concentration corresponding to the elastic modulus. The dynamic image analysis apparatus according to claim 9.

11. The calculation unit calculates the elastic modulus obtained by combining the elastic modulus in the X-axis direction, the elastic modulus in the Y-axis direction, and the elastic modulus in the Z-axis direction. The dynamic image analysis device according to claim 5.

12. The calculation unit displays the normal modulus of elasticity and the calculated modulus of elasticity. The dynamic image analysis device according to claim 5.

13. The calculation unit displays the elastic modulus of multiple dynamic images. The dynamic image analysis device according to claim 5.

14. The first still image and the second still image are images taken at different times. The dynamic image analysis apparatus according to claim 1.

15. The first still image is a still image of maximum inspiration, and the second still image is a still image of maximum expiration, or the first still image is a still image of maximum expiration, and the second still image is a still image of maximum inspiration. The dynamic image analysis apparatus according to claim 1.

16. Acquisition step: Obtaining dynamic images including the lungs by dynamic imaging using radiation, A calculation step in which, in at least a first still image and a second still image constituting the dynamic image, two displacement amount data relating to the displacement of the position of the second region relative to the first region and change amount data relating to the change in concentration between the first and second regions are calculated as a three-dimensional change in the lung structure, based on a comparison of a first region set in the first still image and a second region corresponding to the first region in the second still image. A method for analyzing dynamic images of an information processing device equipped with the following features.

17. On the computer, Acquisition step: Obtaining dynamic images including the lungs by dynamic imaging using radiation, A calculation step in which, in at least a first still image and a second still image constituting the dynamic image, two displacement amount data relating to the displacement of the position of the second region relative to the first region and change amount data relating to the change in concentration between the first and second regions are calculated as a three-dimensional change in the lung structure, based on a comparison of a first region set in the first still image and a second region corresponding to the first region in the second still image. A dynamic image analysis program that performs this operation.

Images