JP7381081B2 - Wall thickness estimation method, wall thickness estimation device, and wall thickness estimation system - Google Patents

Description

The present invention relates to a wall thickness estimation method for estimating the thickness of an organ wall or a blood vessel wall.

Cerebral aneurysm, a type of vascular disease, is an extremely high-risk disease with a mortality rate of over 50% once it ruptures, and is also a disease that has a large social impact and leaves a high rate of sequelae. be. Therefore, preventive treatment (preemptive medicine) that prevents the rupture of cerebral aneurysms is highly important, and appropriate therapeutic intervention is essential.

For appropriate treatment, it is effective to know information about the aneurysm wall (eg, thickness) of the cerebral aneurysm. This is because it is known that rupture of a cerebral aneurysm is more likely to occur in a thin part of the aneurysm wall than in a thick part of the aneurysm wall. However, even within a single cerebral aneurysm, the thickness and other shapes of the aneurysm walls vary from one cerebral aneurysm to another.

Therefore, information regarding the shape of the aneurysm wall, such as its thickness, can be obtained using CT (Computed Tomography), MRI (Magnetic Resonance Imaging), and MRA (Magnetic Resonance Angiography). by It is difficult even for experts to make inferences based solely on the morphology of the lumen of the resulting aneurysm wall.

For example, as a method for predicting the thickness of the aneurysm wall of a cerebral aneurysm, imaging or visual observation through craniotomy performed by a doctor is known. However, this method is highly invasive, imposes a heavy burden on the patient, and does not allow easy prediction of the thickness of the aneurysm wall of a cerebral aneurysm.

Further, for example, an ultrasonic diagnostic apparatus disclosed in Patent Document 1 is known as a minimally invasive method for predicting the thickness of a blood vessel wall such as an aneurysm wall of a cerebral aneurysm. Patent Document 1 discloses an ultrasonic diagnostic apparatus that generates image data using ultrasonic signals and displays information regarding the thickness of a blood vessel wall of a subject based on the image data.

Japanese Patent Application Publication No. 2013-118932

However, since the image data obtained by the conventional technique disclosed in Patent Document 1 has low precision, it is difficult to obtain highly accurate information regarding the blood vessel wall. Furthermore, with conventional technology, it is difficult to obtain highly accurate information not only on the walls of blood vessels but also on the walls of organs within the human body. That is difficult.

Therefore, the present invention proposes useful information for taking specific treatments for diseases of organs or blood vessels by generating highly accurate information regarding organ walls or blood vessel walls using a minimally invasive method. The purpose is to provide methods that can be used.

A wall thickness estimation method according to one aspect of the present invention includes a plurality of predetermined points on the organ wall or the blood vessel wall based on a moving image including the organ wall or the blood vessel wall obtained using four-dimensional angiography. an acquisition step of acquiring behavior information that is numerical information regarding temporal changes in each position of the device; and a step of estimating the thickness of the organ wall or the blood vessel wall based on the behavior information acquired by the acquisition step. The method includes a generation step of generating estimated information that is information in which the mass of each of a plurality of predetermined points is visualized, and an output step of outputting the estimated information generated by the generation step.

Further, a computer program according to one aspect of the present invention causes a computer to execute the wall thickness estimation method described above.

Furthermore, the wall thickness estimating device according to one aspect of the present invention includes a plurality of predetermined values in the organ wall or the blood vessel wall based on a moving image including the organ wall or the blood vessel wall obtained using four-dimensional angiography. an acquisition unit that acquires behavioral information that is numerical information regarding temporal changes in the positions of the respective points; and for estimating the thickness of the organ wall or the blood vessel wall based on the behavior information acquired by the acquisition unit. , and an output unit that outputs the estimated information generated by the generating unit.

Further, a wall thickness estimation system according to one aspect of the present invention includes the wall thickness estimation device described above, a video information processing device that acquires the video image, generates the behavior information, and outputs the behavior information to the acquisition unit. and a display device that displays the estimated information output by the output unit.

According to the wall thickness estimation method and the like of the present invention, by generating highly accurate information regarding organ walls or blood vessel walls using a minimally invasive method, it is possible to generate highly accurate information regarding organ walls or blood vessel walls, thereby providing useful information for taking specific treatments for diseases of organs or blood vessels. can suggest information.

FIG. 1 is a diagram showing the configuration of a wall thickness estimation system according to an embodiment. FIG. 2 is a block diagram showing a characteristic functional configuration of the wall thickness estimating device according to the embodiment. FIG. 3 is a perspective view showing a cerebral aneurysm according to an embodiment. FIG. 4 is a cross-sectional view of the cerebral aneurysm according to the embodiment taken along line IV-IV in FIG. FIG. 5 is a cross-sectional view of the cerebral aneurysm according to the embodiment taken along line VV in FIG. 4. FIG. 6 is a flowchart showing a processing procedure in which the wall thickness estimating device according to the embodiment estimates the thickness of the aneurysm wall of a cerebral aneurysm. FIG. 7 is a diagram showing an example of estimation information according to the embodiment and a still image of a case cerebral aneurysm and parent blood vessel. FIG. 8 is a schematic diagram showing masses of a cerebral aneurysm and a parent blood vessel, which are other examples of estimation information according to the embodiment.

Embodiments will be described below with reference to the drawings. Note that the embodiments described below are all inclusive or specific examples. The numerical values, shapes, materials, components, arrangement positions and connection forms of the components, steps, order of steps, etc. shown in the following embodiments are merely examples, and do not limit the present invention. Further, among the constituent elements in the following embodiments, constituent elements that are not described in the independent claims will be described as arbitrary constituent elements.

Note that each figure is a schematic diagram and is not necessarily strictly illustrated. Furthermore, in each figure, substantially the same configurations are denoted by the same reference numerals, and overlapping explanations may be omitted or simplified.

(Embodiment)
[Configuration of wall thickness estimation system]
The configuration of wall thickness estimation system 1000 according to this embodiment will be explained. FIG. 1 is a diagram showing the configuration of a wall thickness estimation system 1000 according to the present embodiment.

The wall thickness estimation system 1000 uses four-dimensional angiography to acquire behavior information, which is numerical information regarding time changes in the positions of predetermined points, from a moving image including the organ wall or blood vessel wall of the subject U. Furthermore, the wall thickness estimation system 1000 is a system that generates estimation information for estimating the thickness of an organ wall or a blood vessel wall based on the acquired behavior information. For example, the wall thickness estimation system 1000 estimates the thickness of a cerebral aneurysm, which is an example of a blood vessel wall of the subject U.

The four-dimensional angiography method is a method that adds a time axis to the three-dimensional angiography method. Three-dimensional angiography is a method of collecting three-dimensional data of blood vessels using an X-ray CT device or an MRI device and extracting blood vessel information. Note that the four-dimensional angiography method using an X-ray CT device is also referred to as 4DCTA (4 Dimensional Computed Tomography Angiography).

Moving images are obtained by four-dimensional angiography. The moving image may be a time series of three or more still images, and may be, for example, a moving image over the time when the heart beats n times (n is a natural number). Further, for example, the moving image may be a moving image within a predetermined time. The predetermined time may be, for example, m seconds (m is a natural number).

Here, the organ wall is a wall that an organ has, and the organ includes a thoracic organ and an abdominal organ. For example, thoracic organs include the heart and lungs, and abdominal organs include, but are not limited to, the stomach, intestines, liver, kidneys, and pancreas. Further, the organ may include a thoracic organ having a lumen and an abdominal organ having a lumen.

The organ wall may be, for example, a wall that separates the relevant organ from an organ or an organ other than the relevant organ. As an example, when the organ is the heart, the organ wall is a wall composed of muscle (myocardial muscle) that separates the heart from other organs. Furthermore, the organ wall may be, for example, a wall that separates regions within the organ. As an example, when the organ is the heart, the organ wall is the midventricular wall that separates the left ventricle and right ventricle, which is an example of a region within the heart.

Further, the thickness of the blood vessel wall may be the thickness of the wall of blood vessels including arteries or veins, or may be the thickness of the aneurysm wall of an aneurysm or varicose aneurysm, for example, a cerebral aneurysm, an aortic aneurysm or It may be the thickness of the aneurysm wall of a visceral aneurysm.

As shown in FIG. 1, the wall thickness estimation system 1000 includes a wall thickness estimation device 100, a display device 200, a moving image information processing device 300, and a moving image photographing device 400.

The moving image capturing device 400 is a device that generates a moving image including an organ wall or a blood vessel wall using four-dimensional angiography. The moving image photographing device 400 is, for example, an X-ray CT device or an MRI device. In this embodiment, the moving image photographing apparatus 400 is an X-ray CT apparatus, and the moving image photographing apparatus 400 includes an X-ray tube that emits X-rays, a detector that receives signals, and a computer.

The detector is located opposite the X-ray tube and detects the X-rays after passing through the body of the subject U. At this time, the computer generates a moving image that includes an organ wall or a blood vessel wall at a specific part of the subject U by utilizing the fact that X-ray absorption differs depending on the body part of the subject U. Note that the moving image capturing device 400 also has a function of measuring and acquiring the electrocardiogram waveform of the subject U.

Unlike techniques such as laparotomy, open heart surgery, or craniotomy, the method using an X-ray CT device or MRI device and four-dimensional angiography does not require incisions that place a large burden on the body of the subject U. It is a minimally invasive method. Further, a method using an X-ray CT device or an MRI device and four-dimensional angiography can generate highly accurate moving images.

The moving image information processing device 300 acquires a moving image including an organ wall or blood vessel wall generated by the moving image capturing device 400 using four-dimensional angiography, and detects each of a plurality of predetermined points on the organ wall or blood vessel wall. Behavior information, which is numerical information regarding time changes in the position of , is generated. That is, the behavior information is information based on a moving image containing an organ wall or a blood vessel wall obtained using four-dimensional angiography.

For example, behavior information is a set of a specific time in a moving image and the three-dimensional coordinate positions of a plurality of predetermined points on an organ wall or a blood vessel wall at the specific time, and This is numerical information in which a plurality of sets are arranged according to the elapsed time for one heart beat. The moving image information processing device 300 outputs behavior information to the wall thickness estimation device 100. The moving image information processing device 300 is, for example, a personal computer, but may also be a server device connected to a network and having high computing power.

The wall thickness estimation device 100 acquires the behavior information generated by the video information processing device 300, and generates estimation information for estimating the thickness of an organ wall or a blood vessel wall based on the acquired behavior information, The generated estimated information is output to the display device 200. The wall thickness estimating device 100 is, for example, a personal computer, but may also be a server device connected to a network and having a high calculation capacity.

The display device 200 displays estimated information output from the wall thickness estimating device 100. Specifically, the display device 200 is a monitor device configured with a liquid crystal panel, an organic EL (Electro Luminescence) panel, or the like. As the display device 200, a television, a smartphone, a tablet terminal, or the like may be used.

The wall thickness estimating device 100, the display device 200, and the video information processing device 300 only need to be able to send and receive behavior information or estimation information, and may be connected by wire or wirelessly. It's okay.

The moving image information processing device 300 acquires a moving image that includes an organ wall or a blood vessel wall, and generates behavior information that is numerical information regarding temporal changes in the positions of each of a plurality of predetermined points on the organ wall or blood vessel wall. The wall thickness estimating device 100 acquires the behavior information generated by the video information processing device 300, and generates estimation information for estimating the thickness of an organ wall or a blood vessel wall based on the acquired behavior information. Further, the wall thickness estimating device 100 outputs the generated estimation information to the display device 200.

Thereby, in the wall thickness estimation system 1000, a moving image including an organ wall or a blood vessel wall can be obtained using a minimally invasive method. Furthermore, the wall thickness estimation system 1000 can generate estimation information for estimating the thickness of an organ wall or a blood vessel wall using behavior information regarding the video image. Therefore, the wall thickness estimation system 1000 can generate highly accurate information about the wall thickness near each of a plurality of predetermined points on an organ wall or a blood vessel wall.

Next, the functional configuration of the wall thickness estimating device 100 according to this embodiment will be specifically described.

FIG. 2 is a block diagram showing a characteristic functional configuration of wall thickness estimating device 100 according to this embodiment. The wall thickness estimating device 100 includes an acquisition section 110, a generation section 120, and an output section 130.

The acquisition unit 110 obtains numerical information regarding temporal changes in the position of each of a plurality of predetermined points on an organ wall or a blood vessel wall, based on a moving image including the organ wall or blood vessel wall obtained using four-dimensional angiography. Obtain the behavior information that is. Specifically, the acquisition unit 110 acquires behavior information generated by the video information processing device 300. The acquisition unit 110 is, for example, a communication interface that performs wired or wireless communication.

The generation unit 120 generates estimation information for estimating the thickness of an organ wall or a blood vessel wall based on the behavior information acquired by the acquisition unit 110. The estimated information is information that visualizes the mass of each of a plurality of predetermined points on an organ wall or a blood vessel wall.

The estimated information is, for example, image data in which information regarding the mass of each of a plurality of predetermined points is graphed. Note that a method for generating estimated information will be described later using FIGS. 6 to 8. Specifically, the generation unit 120 is realized by a processor that executes a program, a microcomputer, or a dedicated circuit.

Here, the inventors' hypothesis regarding the estimated information generated by the generation unit 120 will be explained. As described above, the estimation information is information that visualizes the mass of each of a plurality of predetermined points for estimating the thickness of an organ wall or a blood vessel wall. The inventors hypothesized that there is a correlation between the mass and the thickness of the organ wall or blood vessel wall at each of a plurality of predetermined points on the organ wall or blood vessel wall, and have proceeded with verification.

That is, according to this hypothesis, the larger the mass, the thicker the organ wall or blood vessel wall, and the smaller the mass, the thinner the organ wall or blood vessel wall. If this hypothesis is correct, the thickness of the organ wall or blood vessel wall can be estimated by obtaining the estimation information according to this embodiment.

The output unit 130 outputs the estimated information generated by the generation unit 120. The output unit 130 may output the estimated information generated by the generation unit 120 to the display device 200. The output unit 130 is, for example, a communication interface that performs wired or wireless communication.

Here, a plurality of predetermined points, which are one piece of information related to behavior information, will be explained using FIGS. 3 to 5. Although this embodiment will be explained using a blood vessel wall, the same applies to an organ wall. Furthermore, here, the blood vessel wall is the aneurysm wall 11 of the cerebral aneurysm 10.

3 to 5, for example, the x-axis positive direction is the direction in which the cerebral aneurysm 10 extends from the mother blood vessel 20, the z-axis is the direction in which the mother blood vessel 20 extends, and the y-axis is the direction in which the mother blood vessel 20 extends. and a direction extending perpendicular to the z-axis.

FIG. 3 is a perspective view showing the cerebral aneurysm 10 according to the present embodiment. FIG. 4 is a cross-sectional view of the cerebral aneurysm 10 according to the present embodiment taken along line IV-IV in FIG. The mother blood vessel 20 is one of the blood vessels that constitute arteries in the brain of the subject U. The cerebral aneurysm 10 is an aneurysm in which a portion of the parent blood vessel 20 bulges, and is an aneurysm that extends from the parent blood vessel 20 in the x-axis direction.

FIG. 5 is a cross-sectional view of the cerebral aneurysm 10 according to the present embodiment taken along line VV in FIG. 4.

As shown in FIG. 5, in the cross-sectional view of the cerebral aneurysm 10, points are provided to correspond to 0:00 to 3:00 as indicated by the clock face. Here, a point p1 is provided in the 0 o'clock direction, a point p30 is provided in the 3 o'clock direction, and 28 points p2 to p29 are provided to equally divide the period between 0 o'clock and 3 o'clock. ing. Note that the number of points is not limited to this, and one cross-sectional view may include, for example, 10 to 1000 points. Further, in this embodiment, one cross-sectional view is used, but the present invention is not limited to this, and a plurality of cross-sectional views (for example, 10 to 1000 cross-sectional views) may be used.

The plurality of predetermined points on the blood vessel wall (aneurysm wall 11) according to this embodiment are points p1 to p30. That is, there are a total of 30 predetermined points on the aneurysm wall 11.

The acquisition unit 110 acquires behavior information, which is numerical information regarding changes in position over time, at each of these 30 predetermined points. The generation unit 120 generates estimation information for estimating the thickness of the aneurysm wall 11 near a predetermined point based on this behavior information. Further, the plurality of predetermined points on the blood vessel wall are not limited to the above, and can be selected from two or more points on the blood vessel wall.

Note that in this embodiment, the behavior information is numerical information regarding a change in position over time over a certain period of time. For example, the fixed period of time is the period of time during which the heart beats once. Further, the time during one heartbeat is divided evenly into, for example, 100 steps.

Note that the time for one pulse of the heart is not limited to this, and may be equally divided by any number of steps selected from 10 steps to 10,000 steps. At this time, the time when the pulsation starts is set as 0 step, and the time when the pulsation ends is set as 100 steps. Therefore, the behavior information includes information regarding the x-axis, y-axis, and z-axis positions of 30 predetermined points in each of steps 0 to 100.

Note that the certain period of time may be a specific number of seconds, for example, 1 second, 5 seconds, or 10 seconds. Further, the certain period of time may be subdivided in any way as long as it is divided into three or more. For example, unlike the above, the certain period of time may be divided by a different number of steps instead of 100 steps. Furthermore, the certain period of time does not need to be divided evenly.

[Processing procedure of wall thickness estimation method]
Next, specific processing steps in the wall thickness estimation method executed by the wall thickness estimation device 100 will be described. Here, the description will be made using the blood vessel wall, but the same applies to the organ wall. FIG. 6 is a flowchart showing a processing procedure in which the wall thickness estimating device 100 according to the present embodiment estimates the thickness of the aneurysm wall 11 of the cerebral aneurysm 10.

The acquisition unit 110 acquires behavior information, which is numerical information regarding temporal changes in the positions of a plurality of predetermined points on the aneurysm wall 11 of the cerebral aneurysm 10 of the subject U, via the video information processing device 300 (acquisition step S101).

Next, the generation unit 120 generates estimation information that is information visualizing the mass of each of a plurality of predetermined points for estimating the thickness of the blood vessel wall from the behavior information acquired by the acquisition unit 110 in the acquisition step S101. (generation step S102). The processing performed by the generation unit 120 will be described in more detail below. The generation unit 120 generates estimation information using equation (1). Note that, for each of the plurality of predetermined points, the position is x, the mass is ρ, the resistance is μ, the spring constant is k, and the external force is P, where m is a rational number.

Here, the position x, rational number m, and external force P are variables, and the mass ρ, resistance μ, and spring constant k are constants. Furthermore, estimated information may be generated using all the mathematical expressions expressed in equation (1).

The generation unit 120 calculates the mass ρ, the resistance μ, and the spring constant k for each of the plurality of predetermined points, assuming that Equation (1) is satisfied. Note that at this time, at least one of the mass ρ, the resistance μ, and the spring constant k may be calculated.

The mass ρ is a mass at a predetermined point, but more specifically, it may be a mass in a region around the predetermined point. The area around the point is, for example, 5 mm or less between the point and the center. Note that the mass ρ is not limited to the above.

The resistance μ and the spring constant k are a resistance and a spring constant, respectively, which are generated when the cerebral aneurysm 10 vibrates due to heart pulsation, but are not limited thereto. Further, the resistance μ may be a value derived from friction caused by contact between the cerebral aneurysm 10 and the brain. As the external force P, a potential value based on the electrocardiogram waveform of the subject U is used, but the value is not limited thereto. Note that in the present embodiment, the generation unit 120 acquires the potential based on the electrocardiogram waveform from the moving image capturing device 400 via the moving image information processing device 300 and the acquiring unit 110.

Furthermore, the generation unit 120 calculates the mass ρ, the resistance μ, and the spring constant k using parameter estimation. For example, a mathematical analysis method can be used for parameter estimation.

Using the mass ρ calculated as above, the generation unit 120 generates estimated information. Here, the estimated information is, for example, image data in which information regarding the calculated mass ρ is graphed.

As described above, by using equation (1) or parameter estimation, more accurate information regarding the blood vessel wall can be generated.

Next, the output unit 130 outputs the estimated information generated by the generation unit 120 (output step S103). In the output step S103, the output unit 130 transmits, for example, the image data generated by the generation unit 120 in the generation step S102 to the display device 200.

The display device 200 acquires the image data output by the output unit 130 and displays an image based on the image data.

Furthermore, the wall thickness estimating device 100 may execute the wall thickness estimating method by reading a computer program recorded on a computer-readable recording medium such as a CD-ROM.

Next, the relationship between the estimated information (more specifically, the calculated mass ρ) and the thickness of the blood vessel wall will be explained using an example of a symptom related to a cerebral aneurysm (hereinafter referred to as a case). Here, in the case, the mass ρ of each of a plurality of predetermined points corresponding to points p1 to p30 shown in FIG. 5 is calculated.

FIG. 7 is a diagram showing an example of estimation information according to the present embodiment and a still image of the cerebral aneurysm 10 and the parent blood vessel 20 of the case. More specifically, (a) of FIG. 7 is an example of estimated information, and is a diagram showing the mass ρ of each of a plurality of predetermined points, and (b) of FIG. 7 is a diagram showing an example of estimated information, and (b) of FIG. FIG. 2 is a diagram showing a still image of a case of cerebral aneurysm 10.

The values on the horizontal axis in FIG. 7(a) correspond to the numbers at the end of points p1 to p30 shown in FIG. 5, and the values on the vertical axis correspond to the masses ρ of points p1 to p30. Note that the mass ρ is a relative mass. In this embodiment, the mass ρ decreases as it goes from point p1 to point p12, the mass ρ is lowest at point p12, and increases as it goes from point p12 to point p30, for example, point p25. The mass ρ is high at ~p30.

The arrows in FIG. 7(b) indicate the direction in which the numbers at the end of points p1 to p30 shown in FIG. 5 increase. In FIG. 7B, the darker the color of the cerebral aneurysm 10, the thinner the blood vessel wall, and the lighter the color, the thicker the blood vessel wall.

Note that in FIG. 7(b), the cerebral aneurysm 10 is expressed in two colors, black and white, but in actual craniotomy, the still image of the cerebral aneurysm 10 may be expressed in color. . Therefore, the dark-colored portion of the cerebral aneurysm 10 in FIG. 7(b) is a portion where the white tone is weak and the red tone is strong in an actual craniotomy. Furthermore, the pale-colored region of the cerebral aneurysm 10 in FIG. 7(b) is a region that has a strong white tone and a weak red tone in an actual craniotomy.

The point p12 with the lowest mass ρ corresponds to the area surrounded by the broken line in FIG. 7(b), and the points p25 to p30 with the highest mass ρ correspond to the area surrounded by the dashed-dotted line in FIG. 7(b). do. As shown in FIG. 7(b), the area surrounded by the broken line is the area where the cerebral aneurysm 10 is dark in color and the blood vessel wall is thin, and the area surrounded by the dot-dashed line is the area where the cerebral aneurysm 10 is in the dark color and the blood vessel wall is thin. This is an area where the blood vessel wall is thin and thick. In other words, the point p12 where the mass ρ is lowest corresponds to a region where the blood vessel wall is thin, and the points p25 to p30 where the mass ρ is high correspond to a region where the blood vessel wall is thick. In other words, it is clear that the inventors' hypothesis that the larger the mass ρ is, the thicker the blood vessel wall is, and the smaller the mass ρ is, the thinner the blood vessel wall is is correct.

That is, by using the estimated information (more specifically, the calculated mass ρ), highly accurate information about the thickness of the blood vessel wall can be generated.

Such information is useful information for, for example, distinguishing between cerebral aneurysms that are likely to grow and rupture and cerebral aneurysms that are difficult to grow and rupture, and to appropriately determine whether or not treatment is necessary.

In other words, the wall thickness estimation method according to the present embodiment generates highly accurate information about the blood vessel wall using a minimally invasive method, thereby providing useful information for taking specific treatments for blood vessel diseases. I can make suggestions. Furthermore, the wall thickness estimation method according to the present embodiment can be used not only for estimating the thickness of blood vessel walls, but also for estimating the thickness of organ walls.

In other words, by generating highly accurate information about organ walls using minimally invasive techniques that do not require laparotomy, open heart surgery, or craniotomy, we can obtain useful information for taking specific treatments for organ diseases. I can make suggestions.

Furthermore, the estimated information is not limited to the above. For example, the generation unit 120 may generate other examples of estimation information using the calculated mass ρ (for example, (a) in FIG. 7). FIG. 8 is a schematic diagram showing the masses ρ of the cerebral aneurysm 10 and the parent blood vessel 20, which is another example of the estimation information according to the present embodiment. Note that the schematic diagram shown in FIG. 8 may be generated using the minimum value, maximum value, standard deviation, average value, and median value of each of the mass ρ, the resistance μ, and the spring constant k.

In FIG. 8, the darker the color of the cerebral aneurysm 10 and the parent blood vessel 20, the lower the mass ρ, and the lighter the color, the higher the mass ρ. In addition, in FIG. 8, the cerebral aneurysm 10 and the parent blood vessel 20 are expressed in two colors, black and white, but when the output unit 130 actually outputs them, a schematic representation of the cerebral aneurysm 10 and the parent blood vessel 20 is used. The figures may be rendered in color.

Therefore, the dark-colored areas of the cerebral aneurysm 10 and the mother blood vessel 20 in FIG. 8 are areas where the white tone is weak and the red tone is strong when the output unit 130 actually outputs them. In addition, the pale-colored parts of the cerebral aneurysm 10 and the parent blood vessel 20 in FIG. 8 are parts where the white color tone is strong and the red color tone is weak when the output unit 130 actually outputs them.

As mentioned above, the inventors' hypothesis is correct, and the estimated information shown in FIG. It is thickly illustrated.

Estimated information expressed as such a schematic diagram is useful information for distinguishing between cerebral aneurysms that are likely to grow and rupture and those that are difficult to grow and rupture, and for appropriately determining whether or not treatment is necessary. be.

Further, although a two-dimensional schematic diagram is shown in FIG. 8, a three-dimensional color map may be used as the estimation information.

[Effects etc.]
As explained above, the wall thickness estimation method includes an acquisition step S101, a generation step S102, and an output step S103. In the acquisition step S101, numerical information regarding temporal changes in the positions of a plurality of predetermined points on an organ wall or a blood vessel wall is obtained based on a moving image including the organ wall or blood vessel wall obtained using four-dimensional angiography. Obtain the behavior information that is. The generation step S102 generates estimation information that is information visualizing the mass of each of a plurality of predetermined points for estimating the thickness of an organ wall or a blood vessel wall, based on the behavior information acquired in the acquisition step S101. do. The output step S103 outputs the estimated information generated in the generation step S102.

Further, the computer program according to the present embodiment causes a computer to execute the wall thickness estimation method described above.

Accordingly, in the wall thickness estimation method, for example, a moving image including a blood vessel wall is generated using an X-ray CT device or an MRI device and a four-dimensional angiography method. For example, compared to techniques such as craniotomy, moving images including blood vessel walls can be obtained using a minimally invasive technique. The wall thickness estimation method can generate estimation information in which the mass of each of a plurality of predetermined points for estimating the thickness of a blood vessel wall is visualized using behavior information regarding the moving image. It was shown that the thickness of the blood vessel wall estimated based on the estimated information corresponds to the thickness of the blood vessel wall obtained by craniotomy.

In other words, the wall thickness estimation method can generate highly accurate information about the wall thickness near each of a plurality of predetermined points on the blood vessel wall. In this embodiment, for example, the thickness of the aneurysm wall 11 of the cerebral aneurysm 10 is estimated. Such information is useful information for, for example, distinguishing between cerebral aneurysms that are likely to grow and rupture and cerebral aneurysms that are difficult to grow and rupture, and to appropriately determine whether or not treatment is necessary.

Note that the wall thickness estimation method is not limited to blood vessel walls, and can also be used to estimate the thickness of organ walls.

In other words, the wall thickness estimation method according to the present embodiment generates highly accurate information regarding organ walls or blood vessel walls using a minimally invasive method, thereby enabling specific treatment for organ or blood vessel diseases. can suggest useful information.

Further, for each of a plurality of predetermined points, the position is x, the mass is ρ, the resistance is μ, the spring constant is k, the external force is P, and the rational number is m. At this time, the generation step S102 calculates the mass ρ, the resistance μ, and the spring constant k assuming that the equation (1) is satisfied.

Thereby, more accurate information regarding the organ wall or blood vessel wall can be generated.

Furthermore, the generation step S102 calculates the mass ρ, the resistance μ, and the spring constant k using parameter estimation.

Thereby, more accurate information regarding the organ wall or blood vessel wall can be generated.

Furthermore, in the wall thickness estimation method, the blood vessel wall thickness is the thickness of the aneurysm wall in an aneurysm or varicose aneurysm.

Thereby, the wall thickness estimation method can estimate the thickness of the aneurysm wall of an aneurysm or varicose aneurysm as the thickness of the blood vessel wall.

Furthermore, in the wall thickness estimation method, the blood vessel wall thickness is the thickness of the aneurysm wall in a cerebral aneurysm.

Thereby, the wall thickness estimation method can estimate the thickness of the aneurysm wall of a cerebral aneurysm as the thickness of the blood vessel wall.

Furthermore, in the wall thickness estimation method, the blood vessel wall thickness is the thickness of the blood vessel wall in an artery or a vein.

Thereby, the wall thickness estimation method can estimate the thickness of the blood vessel wall of an artery or vein as the blood vessel wall thickness.

The wall thickness estimating device 100 also includes an acquisition section 110, a generation section 120, and an output section 130. The acquisition unit acquires numerical information regarding temporal changes in the positions of each of a plurality of predetermined points on the organ wall or blood vessel wall, based on a moving image including the organ wall or blood vessel wall obtained using four-dimensional angiography. Obtain certain behavioral information. The generation unit 120 generates estimation information, which is information visualizing the mass of each of a plurality of predetermined points for estimating the thickness of an organ wall or a blood vessel wall, based on the behavior information acquired by the acquisition unit 110. do. The output unit 130 outputs the estimated information generated by the generation unit.

Thereby, in the wall thickness estimating device 100, a moving image including the blood vessel wall is generated using an X-ray CT device or an MRI device and a four-dimensional angiography method, for example. For example, compared to techniques such as craniotomy, moving images including blood vessel walls can be obtained using a minimally invasive technique. The wall thickness estimating device 100 can generate estimation information in which the mass of each of a plurality of predetermined points for estimating the thickness of a blood vessel wall is visualized by using the behavior information regarding the moving image. It was shown that the thickness of the blood vessel wall estimated based on the estimated information corresponds to the thickness of the blood vessel wall obtained by craniotomy.

That is, the wall thickness estimating device 100 can generate highly accurate information about the wall thickness near each of a plurality of predetermined points on the blood vessel wall. In this embodiment, for example, the thickness of the aneurysm wall 11 of the cerebral aneurysm 10 is estimated. Such information is useful information for, for example, distinguishing between cerebral aneurysms that are likely to grow and rupture and cerebral aneurysms that are difficult to grow and rupture, and to appropriately determine whether or not treatment is necessary.

Note that the wall thickness estimating device 100 can be used not only for estimating the thickness of blood vessel walls but also for estimating the thickness of organ walls.

In other words, the wall thickness estimating device 100 according to the present embodiment generates highly accurate information regarding organ walls or blood vessel walls using a minimally invasive method, thereby making it possible to take specific measures against diseases of organs or blood vessels. We can suggest useful information for you.

The wall thickness estimation system 1000 also includes the wall thickness estimation device 100 described above, a video information processing device 300 that acquires a video, generates behavior information, and outputs it to the acquisition unit 110, and an output unit 130 that outputs and a display device 200 that displays the estimated information.

Accordingly, in the wall thickness estimation system 1000, for example, a moving image including the blood vessel wall is generated using an X-ray CT device or an MRI device and a four-dimensional angiography method. For example, compared to techniques such as craniotomy, moving images including blood vessel walls can be obtained using a minimally invasive technique. The wall thickness estimation system 1000 can generate estimation information in which the mass of each of a plurality of predetermined points for estimating the thickness of the blood vessel wall is visualized using the behavior information regarding the moving image. It was shown that the thickness of the blood vessel wall estimated based on the estimation information corresponds to the thickness of the blood vessel wall obtained by craniotomy.

That is, the wall thickness estimation system 1000 can generate highly accurate information about the wall thickness near each of a plurality of predetermined points on the blood vessel wall. In this embodiment, for example, the thickness of the aneurysm wall 11 of the cerebral aneurysm 10 is estimated. Such information is useful information for, for example, distinguishing between cerebral aneurysms that are likely to grow and rupture and cerebral aneurysms that are difficult to grow and rupture, and to appropriately determine whether or not treatment is necessary.

Note that the wall thickness estimation system 1000 can be used not only for estimating the thickness of blood vessel walls but also for estimating the thickness of organ walls.

In other words, the wall thickness estimation system 1000 according to the present embodiment generates highly accurate information regarding organ walls or blood vessel walls using a minimally invasive method, thereby making it possible to take specific measures against diseases of organs or blood vessels. We can suggest useful information for you.

Furthermore, by visualizing and displaying the estimated information, for example, a doctor or the like can obtain highly accurate information about the thickness of an organ wall or a blood vessel wall.

(Other embodiments)
Although the wall thickness estimation method and the like according to the embodiment have been described above, the present invention is not limited to the above embodiment.

The analysis using parameter estimation used in this embodiment is included in AI (Artificial Intelligence). That is, the analysis for calculating the mass ρ, the resistance μ, and the spring constant k in the generation step S102 is not limited to parameter estimation, and other methods included in AI technology may be used.

In the embodiment described above, a method of obtaining behavioral information using an actual case and four-dimensional angiography has been shown. However, the method of obtaining behavior information is not limited to this. For example, behavior information may be obtained by two other example methods shown below.

In the other method of Example 1, behavior information is obtained by using an artificial aneurysm, an artificial heart connected to the artificial aneurysm, and an imaging device.

The artificial aneurysm includes an artificial blood vessel and an artificial aneurysm. Artificial blood vessels and artificial aneurysms are made to imitate human blood vessels and aneurysms that occur in human blood vessels. The artificial lump may be made of, for example, a rubber material, such as silicone rubber or fluororubber.

Further, the artificial lump may be made of silicone resin, for example. The artificial aneurysm is not limited to the above, as long as it is made of a flexible material.

The artificial aneurysm is created using image data obtained by the X-ray CT device or MRI device described above. This image data includes data on human blood vessels and aneurysms that have occurred in the blood vessels.

The artificial aneurysm is created based on DICOM (Digital Imaging and Communications in Medicine) data regarding the image data obtained above.

An artificial heart is a device that performs the pumping function of the human heart. The artificial heart and the artificial aneurysm are connected, and the pump function of the artificial heart is activated, causing the artificial aneurysm to pulsate. Using the movement of the artificial aneurysm and the imaging device, behavioral information can be obtained.

The imaging device is, for example, a camera device that can capture still images and moving images. Furthermore, the imaging device may be a device that can obtain all information such as three-dimensional coordinates of the surface of the observation target, displacement in three-dimensional space, velocity in three-dimensional space, and acceleration in three-dimensional space. . Such an imaging device captures images for 1 second, 5 seconds, or 10 seconds to obtain the three-dimensional coordinates of the surface of the observation target, displacement in three-dimensional space, velocity in three-dimensional space, and acceleration in three-dimensional space. You can get all the information.

As described above, in the method of Other Example 1, the imaging device captures an image of the pulsating artificial aneurysm, thereby obtaining the three-dimensional coordinates of the surface of the artificial aneurysm, the displacement in three-dimensional space, and the velocity in three-dimensional space. and acceleration in three-dimensional space. Behavior information may be obtained based on information on any or all of these three-dimensional coordinates, displacement in three-dimensional space, velocity in three-dimensional space, and acceleration in three-dimensional space.

In the method of Example 1, since it is a less invasive technique than the craniotomy described above, behavioral information can be obtained more easily.

Furthermore, in the method of Example 2, behavior information can be obtained by using a model animal in which a blood vessel has developed an aneurysm and the above-mentioned imaging device.

Specifically, the imaging device images the blood vessels and aneurysms of the model animal, thereby obtaining the three-dimensional coordinates of the surfaces of the model animal's blood vessels and aneurysms, the displacement in three-dimensional space, the velocity in three-dimensional space, and the like. All information about acceleration in three-dimensional space can be obtained. Behavior information may be obtained based on any or all of these pieces of information.

In the other method of Example 2, unlike the case of the human case shown in the embodiment, there is no need for written consent from the human subject of the case. Moreover, since the surfaces of the blood vessels and aneurysms of the model animal can be patterned (for example, marked by spraying) necessary for imaging, time evolution data of precise three-dimensional coordinates can be obtained.

Furthermore, data on blood vessels and aneurysms of model animals can be acquired at equal time intervals (eg, once every two weeks). Therefore, behavior information can be obtained more easily than in the embodiment.

By using the above method, a large amount of behavior information can be easily obtained, and as a result, a large amount of estimated information can be obtained. This is expected to improve the accuracy of information regarding walls.

In this embodiment, a case has been shown in which the thickness of the blood vessel wall is the thickness of the aneurysm wall 11 of the cerebral aneurysm 10, but as described above, it may be the thickness of the wall of a blood vessel including an artery or a vein. For example, when the blood vessel wall is the thickness of a blood vessel including an artery or a vein, the degree of stenosis of the artery or vein can be estimated by using the wall thickness estimation method according to the embodiment.

Note that in each of the above embodiments, each component may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.

Other embodiments may be obtained by making various modifications to each embodiment that a person skilled in the art would think of, or may be realized by arbitrarily combining the components and functions of each embodiment without departing from the spirit of the present invention. The present invention also includes such forms.

The wall thickness estimation method according to the present invention can be used in various applications such as medical equipment and medical methods.

10 Cerebral aneurysm 11 Aneurysm wall 20 Mother blood vessel 100 Wall thickness estimation device 110 Acquisition unit 120 Generation unit 130 Output unit 1000 Wall thickness estimation system 200 Display device 300 Video information processing device 400 Video imaging device U Subject p1, p2, p3 , p4, p5, p6, p7, p8, p9, p10, p11, p12, p13, p14, p15, p16, p17, p18, p19, p20, p21, p22, p23, p24, p25, p26, p27, p28 , p29, p30 Point S101 Acquisition process S102 Generation process S103 Output process

Claims (9)

Behavior that is numerical information regarding the temporal change in the position of each of a plurality of predetermined points on the organ wall or the blood vessel wall, based on a moving image containing the organ wall or the blood vessel wall obtained using four-dimensional angiography. an acquisition step of acquiring information;
Based on the behavior information acquired in the acquisition step, estimation information is generated that is information visualizing the mass of each of the plurality of predetermined points for estimating the thickness of the organ wall or the blood vessel wall. generation process,
and an output step of outputting the estimation information generated by the generation step. When the position of each of the plurality of predetermined points is x, the mass is ρ, the resistance is μ, the spring constant is k, the external force is P, and the rational number is m,
The generation step includes:
The wall thickness estimating method according to claim 1, wherein the mass ρ, the resistance μ, and the spring constant k are calculated as satisfying the following. The wall thickness estimation method according to claim 2, wherein the generating step calculates the mass ρ, the resistance μ, and the spring constant k using parameter estimation. The wall thickness estimation method according to any one of claims 1 to 3, wherein the blood vessel wall thickness is the thickness of an aneurysm wall in an aneurysm or varicose aneurysm. The wall thickness estimation method according to any one of claims 1 to 4, wherein the blood vessel wall thickness is the thickness of an aneurysm wall in a cerebral aneurysm. The wall thickness estimation method according to any one of claims 1 to 5, wherein the blood vessel wall thickness is the thickness of a blood vessel wall in an artery or a vein. A computer program for causing a computer to execute the wall thickness estimation method according to any one of claims 1 to 6. Behavior that is numerical information regarding the temporal change in the position of each of a plurality of predetermined points on the organ wall or the blood vessel wall, based on a moving image containing the organ wall or the blood vessel wall obtained using four-dimensional angiography. an acquisition unit that acquires information;
Based on the behavior information acquired by the acquisition unit, estimation information is generated that is information visualizing the mass of each of the plurality of predetermined points for estimating the thickness of the organ wall or the blood vessel wall. A generation section,
A wall thickness estimating device, comprising: an output section that outputs the estimation information generated by the generation section. The wall thickness estimating device according to claim 8,
a video information processing device that acquires the video image, generates the behavior information, and outputs the behavior information to the acquisition unit;
A wall thickness estimation system, comprising: a display device that displays the estimated information outputted by the output unit.

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