KR102211154B1 - Method and apparatus of diagnosis cardiac diseases based on simulation of cardiac motion - Google Patents

Abstract

We propose a method and apparatus for diagnosing heart disease based on cardiac exercise modeling.
Boundary condition by applying the physical characteristics of cardiac motion to the 3D cardiac shape model, and fusing a plurality of echocardiography images according to time and the 3D cardiac shape model to which the physical characteristics are applied, acquired to acquire a dynamic image After deriving a (boundary condition), a method for diagnosing a heart disease based on cardiac motion modeling that models a user's cardiac motion using a boundary condition may be provided.

Description

A method and apparatus for diagnosing heart disease based on cardiac motion modeling {METHOD AND APPARATUS OF DIAGNOSIS CARDIAC DISEASES BASED ON SIMULATION OF CARDIAC MOTION}

The following embodiments relate to a method and apparatus for diagnosing a heart disease based on cardiac motion modeling.

Various modeling devices and methods are used to diagnose heart disease. Among these, the most commonly used method is that the doctor observes the movement of the heart through echocardiography and calculates a wall motion score for the movement of each segment of the heart and uses it as an index of cardiac movement. . Since the wall motion score is measured by a doctor's subjective judgment, it is difficult to become an objective indicator.

According to an embodiment, a method of modeling cardiac motion includes generating a three-dimensional heart shape model; Applying physical characteristics of cardiac motion to the cardiac shape model; Acquiring a plurality of echocardiographic images according to time changes in order to acquire a dynamic image; Deriving a boundary condition by fusing the 3D heart shape model to which the physical characteristics are applied and the acquired plurality of echocardiographic images; And modeling the user's cardiac motion by using the boundary condition.

The method may further include modeling cardiac motion using the modeling result.

The 3D heart shape model may be generated based on a 2D image or a 3D image photographed by the user.

A 2D image or a 3D image photographed by the user may include a CT image.

The applying of the physical characteristics of the cardio exercise may include selecting a parameter that can reflect the physical characteristics of the cardio exercise; And applying the physical characteristics of the cardiac motion to the cardiac shape model by using the selected parameter.

The physical characteristics of the cardiac exercise may include at least one of a fiber orientation of the cardiac muscle, a passive stress of the cardiac muscle, and an active stress of the cardiac muscle.

The deriving of the boundary condition may include fusing the 3D heart shape model and the echocardiographic images in consideration of a position change of a feature point over time in the plurality of echocardiographic images. I can.

The deriving of the boundary condition may include searching for a displacement boundary condition among the boundary conditions by using the fused 3D heart shape model and echocardiographic images.

The boundary condition may include a displacement boundary condition and a load boundary condition.

Diagnosing a heart disease using the modeling result, wherein diagnosing the heart disease may include: optimizing the selected parameter using the modeling result; Obtaining a distribution of motion information of a myocardium using the optimized parameter; And diagnosing the heart disease by using the distribution of the motion information of the myocardium.

Optimizing the selected parameter may include: evaluating the motion information of the myocardium obtained as a result of the modeling; And optimizing the selected parameter to reflect the physical characteristics of the user's cardio exercise using the evaluation result.

The myocardial motion information may include a strain distribution of the myocardium and a stress of the myocardium.

Evaluating the motion information of the myocardium may include evaluating the motion information of the myocardium using a reference strain.

The reference strain may include heart motion information obtained from the plurality of echocardiographic images.

The optimizing may include analyzing an effect of the parameter on the reference strain; And optimizing the parameter using the analysis result.

According to an embodiment, an apparatus for modeling cardiac motion includes: an application unit for applying physical characteristics of cardiac motion to a 3D cardiac shape model; An acquisition unit for acquiring a plurality of echocardiographic images according to a time change to acquire a dynamic image; A derivation unit for deriving a boundary condition by fusing the 3D heart shape model to which the physical characteristics are applied and the plurality of acquired echocardiographic images; And a modeling unit for modeling the user's cardiac motion using the boundary condition.

The application unit may select a parameter that can reflect the physical characteristics of the cardiac exercise, and apply the physical characteristics of the cardiac exercise to the heart shape model by using the selected parameter.

The derivation unit may include a fusion unit for fusing the 3D heart shape model and the plurality of echocardiographic images in consideration of a position change of a feature point in the plurality of echocardiographic images over time.

Further comprising a diagnostic unit for diagnosing a heart disease using the result of the modeling, the diagnosis unit, the optimization unit for optimizing the selected parameter using the result of the modeling; And an acquisition unit that obtains a distribution of the motion information of the myocardium using the optimized parameter, and diagnoses the heart disease by using the distribution of the motion information of the myocardium.

The diagnosis unit further includes an evaluation unit for evaluating the motion information of the myocardium obtained as a result of the modeling, and using the evaluation result, the parameter may be optimized to reflect the physical characteristics of the user's cardiac motion. have.

1 is a diagram for explaining an overall operational concept of a method for diagnosing a heart disease based on cardiac exercise modeling according to an exemplary embodiment.
2 is a flow chart of a method for modeling cardiac exercise according to an embodiment.
3 is a diagram for explaining characteristics of cardiac exercise to be modeled in an embodiment.
4 is a diagram illustrating a 3D heart shape model generated in a method of modeling cardiac motion according to an exemplary embodiment.
5 is a diagram illustrating image fusion between heterogeneous images used in a method of modeling cardiac motion according to an exemplary embodiment.
6 is a graph showing a load boundary condition according to a change in a force applied to the myocardium over time in a method of modeling cardiac motion according to an exemplary embodiment.
FIG. 7 is a diagram illustrating a method of evaluating motion information of a myocardium acquired as a result of modeling in a method of modeling cardiac motion according to an exemplary embodiment.
8 is a flow chart showing a method of modeling cardiac exercise in FIG. 2.
9 is a block diagram of an apparatus for modeling cardiac motion according to an embodiment.
10 is a block diagram of an apparatus for modeling cardiac motion according to another embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the present invention is not limited or limited by the exemplary embodiments. In addition, the same reference numerals shown in each drawing denote the same member.

FIG. 1 is a diagram illustrating an overall operation flow according to a method for diagnosing a heart disease based on cardiac exercise modeling according to an exemplary embodiment.

In the method for diagnosing heart disease according to an embodiment, motion information of the heart muscle (hereinafter, ``myocardium'') at all locations of the heart is acquired by cardiac motion modeling using a user's 3D heart shape model, and It is used for diagnosis of disease.

The myocardial motion information includes, for example, a strain of the myocardium, that is, a strain of the myocardium, or a distribution situation of the strain of the myocardium, and a stress of the myocardium.

Referring to FIG. 1, an apparatus for modeling cardiac motion according to an embodiment (hereinafter, a “modeling apparatus”) is an image of a user's heart (for example, magnetic resonance imaging (MRI) or computed tomography). A three-dimensional heart shape may be generated based on (Computed Tomography; CT) image (110). In this case, the 3D heart shape model generated based on the image of the user's heart may be specialized for the user. A cardiac shape model specialized for a user may be used to model a cardiac motion of the user.

The three-dimensional heart shape model generated in the generation process 110 may reflect physical characteristics that are different from other organs and to be considered in cardiac motion. The physical characteristics to be considered in cardio exercise can be called'physical characteristics of cardiac exercise'.

Unlike other organs, the heart is made up of a muscle called myocardium, and physical properties may vary depending on the fiber orientation of the fibers constituting the heart muscle. The characteristics of the heart that can be distinguished from other organs will be described in detail with reference to FIG. 3.

The physical properties of cardio exercise can be parameterized. Accordingly, in an embodiment, a parameter capable of reflecting the physical characteristics of cardiac motion is applied to the 3D heart shape modeled in the modeling process 110 so that the motion characteristics of the heart can be reflected in the cardiac shape model. In this case, the process of adjusting each parameter to suit the user may be performed in an optimization process 150 to be described later.

When a 3D heart shape model is generated for the user in the generation process 110, the modeling device may model the user's cardio movement using the 3D heart shape model (120 ).

In the modeling process 120, the modeling apparatus may derive a boundary condition through fusion between different kinds of images of the user's heart, and perform modeling using the boundary condition. Here, the heterogeneous images mean different types of images capable of grasping the three-dimensional structure of the user's heart and the movement of the heart. The heterogeneous image may be, for example, a CT image and an echocardiographic image.

The boundary condition relates to a method of processing boundary regions or boundary points of two different images that are fused to each other. Fusion between heterogeneous images and derivation of boundary conditions through them will be described with reference to FIG. 5.

The modeling apparatus may evaluate the motion information of the myocardium obtained as a result of modeling the cardiac motion (130).

In the evaluation process 130, the modeling device may verify and evaluate the motion information of the myocardium obtained through the modeling process 120. The evaluation process 130 is to evaluate whether the parameter value reflecting the physical characteristics of the heart properly reflects the motion information of the user's myocardium.

The modeling device determines whether the evaluation result converges to a constant value (140).

If it is determined in the determination process 140 that the evaluation result converges to a certain value, the modeling apparatus may diagnose a heart disease of the user using the motion information of the myocardium obtained through the modeling process 120 (160 ).

If it is determined in the determination process 140 that the evaluation result does not converge to a certain value, the modeling device may optimize a parameter that reflects individual physical characteristics by using the verification and evaluation results in the evaluation process 130. There is (150). The optimization process 150 may be viewed as a process of optimizing a parameter selected in the modeling process 110 for a user by using, for example, heart motion information obtained from an echocardiographic image.

In one embodiment, by repeatedly performing the above-described modeling process 120 to the optimization process 150, motion information (for example, strain distribution) for the entire user's heart is obtained, Can be used for diagnosis.

2 is a flow chart of a method for modeling cardiac exercise according to an embodiment.

Referring to FIG. 2, the modeling apparatus according to an embodiment may generate a user-specific 3D heart shape model based on a user's CT image (210). The 3D heart shape model may be generated based on a 2D image or a 3D image captured by the user. A 2D image or a 3D image photographing a user may include a CT image. A method of generating a 3D heart shape model by the modeling apparatus will be described in detail with reference to FIG. 4.

The modeling apparatus may apply physical characteristics of cardiac motion to the cardiac shape model generated in operation 210 (220 ). In step 220, the modeling apparatus may select a parameter that can reflect the physical characteristics of the cardiac exercise, and apply the physical characteristics of the cardiac exercise to the heart shape model by using the selected parameter. In this case, the physical characteristics of the cardiac exercise may include at least one of a fiber orientation of a cardiac muscle, a passive stress of a cardiac muscle, and an active stress of a cardiac muscle.

The modeling apparatus may acquire a plurality of echocardiographic images according to time changes in order to acquire a dynamic image (230 ).

The modeling apparatus may derive a boundary condition by fusing the 3D heart shape model to which the physical characteristics generated in step 210 is applied and a plurality of echocardiographic images acquired in step 230 (240). . Here, the boundary condition may be applied to the 3D heart shape model generated in 210 to model the user's cardiac motion.

A method of fusing the user's heterogeneous images, for example, a 3D heart shape model and a plurality of echocardiographic images acquired in step 230, and a method of deriving a boundary condition, are detailed with reference to FIGS. 5 and 6. Explained as.

The modeling apparatus may model the user's cardiac motion by using the boundary condition derived in step 240 (250 ).

The modeling device may diagnose a heart disease using the modeling result obtained in step 250 (260 ). The modeling device may obtain a distribution of the user's myocardial strain through the modeling result, and may perform cardiac diagnosis using this. A specific method of diagnosing a heart disease by the modeling device will be described in detail with reference to FIG. 8.

3 is a diagram for explaining characteristics of cardiac exercise to be modeled in an embodiment.

Referring to FIG. 3, physical characteristics to be considered in cardiac exercise are shown. The physical characteristics to be considered in cardio exercise can be called'physical characteristics of cardiac exercise'.

Unlike other organs, the heart is composed of a muscle called myocardium, and physical properties may vary according to the fiber orientation of the fibers 310 constituting the myocardium. In particular, since the heart needs to grasp the movement of the myocardium or the direction of the fibers 310 in the process of blood flowing through the left, right atrium and ventricle, items to be considered different from general organs may occur.

Therefore, in one embodiment, the heart is divided into three layers so that the orientation of the fibers 310 can be taken into account, and a three-dimensional mesh is formed for each layer to form a three-dimensional heart shape. Can be modeled. A three-dimensional heart shape model generated according to an embodiment may be referred to FIG. 4.

In addition, the physical characteristics of cardiac exercise include passive stress, a force that pushes or pulls inside the heart muscle when an external force acts on the heart muscle, and active stress, a force that acts inside the heart muscle without external force. For example.

These physical properties of cardio exercise can be parameterized. Accordingly, in an embodiment, a parameter capable of reflecting the physical characteristics of cardiac motion is applied to the 3D heart shape modeled in the modeling process 110 so that the motion characteristics of the heart can be reflected in the heart shape model.

4 is a diagram illustrating a 3D heart shape model generated in a method of modeling cardiac motion according to an exemplary embodiment.

Referring to FIG. 4, the modeling apparatus according to an embodiment uses a 3D volume-type medical image 410 generated using a plurality of 2D images of a target organ, a heart. A shape model 430 may be generated. In this case, the medical image 410 in the form of a 3D volume may be generated from, for example, magnetic resonance imaging (MRI) and computed tomography (CT) images.

The heart shape model may be generated as a user-specific 3D shape model by using a heart region masked in the medical image 410 of the user. In this case, the modeling device may reflect the characteristics of the user's heart shape by using a volume mesh model of a pre-configured template organ. The template organ is a three-dimensional model of the general shape of the target organ.

The modeling apparatus according to an embodiment constructs a surface mesh from the medical image 410 in the form of a 3D volume, and then puts the heart into an inner layer so that the directionality of the myocardial fibers can be considered as described above. layer) 431, the middle layer 433, and the outer layer (epi layer) 435 can be composed of a three-dimensional shape model having three layers (3-layer). In this case, in the heart shape model 430 composed of a 3D mesh including three layers, movements of each layer may be different.

5 is a diagram illustrating image fusion between heterogeneous images used in a method of modeling cardiac motion according to an exemplary embodiment.

Referring to FIG. 5, a CT image 510 and an echocardiographic image 530 of a user's heart are shown. The CT image 510 can grasp a three-dimensional shape of the user's heart, but cannot grasp the movement of the heart. In addition, although the echocardiographic image 530 can grasp the movement of the heart, it cannot grasp the three-dimensional shape of the heart. Accordingly, in an embodiment, by fusion of different heterogeneous images to derive a boundary condition, features of the shape of the heart and features of the movement can be reflected during modeling.

The modeling apparatus extracts feature points from each of the CT image 510 and the echocardiography image 530, and matches the feature points of the CT image 510 with the feature points of the echocardiography image 530 By doing so, the CT image 510 and the echocardiographic image 530 may be fused. The modeling apparatus may perform fusion of the 3D heart shape model obtained from the CT image 510 and the echocardiographic image in consideration of the position change of the feature points in the echocardiography image 530 over time. The modeling apparatus may search for a displacement boundary condition using a fused 3D heart shape model and an echocardiographic image.

Here, the boundary condition is for processing boundary areas or boundary points of two different images fused to each other. Boundary conditions can be divided into displacement boundary conditions and load boundary conditions. The displacement boundary condition may indicate how each feature point of the heart in an echocardiographic image moves when it slides over the diaphragm. In other words, the displacement boundary condition may indicate how each position of the myocardium changes physically. The load boundary condition is the force (or pressure) applied to each part of the myocardium inside the heart. The load boundary condition may be expressed as a change in force applied to the myocardium over time when the heart is periodically exercising as shown in the graph of FIG. 6. The displacement boundary condition can also be called'Essential Boundary Condition', and the load boundary condition can also be called'Natural Boundary Condition'.

6 is a graph showing a load boundary condition according to a change in a force applied to the myocardium over time in a method of modeling cardiac motion according to an exemplary embodiment.

FIG. 7 is a diagram illustrating a method of evaluating motion information of a myocardium acquired as a result of modeling in a method of modeling cardiac motion according to an exemplary embodiment.

Referring to FIG. 7, the modeling apparatus according to an embodiment may obtain a strain of a two-dimensional cross section from an echocardiographic image using a speckle tracking echo (STE) technique. The Speckle Tracking Echo (STE) technique tracks the movement of speckles in an echocardiographic image, allowing you to understand how the structures of the heart deform during the exercise cycle of the heart.

At this time, the strain in the longitudinal direction (L) 710 in the strain of the two-dimensional cross-section is applied to the strains in different directions (radial (R), circumferencial). Compared to this, relatively accurate values can be obtained.

Accordingly, in an embodiment, in the process of evaluating the motion information of the myocardium, the motion information of the myocardium obtained through the modeling process (motion information of the myocardium in the longitudinal direction) and the longitudinal strain (L) 710 are compared. You can do the evaluation. In addition, the modeling apparatus may perform an evaluation by comparing the motion information of the whole heart and motion information of the myocardium, which is determined from the feature points of the heart (or myocardium).

In this way, a strain in the longitudinal direction used for verification and evaluation may be called a'reference strain'.

8 is a flow chart showing a method of modeling cardiac exercise in FIG. 2.

Referring to FIG. 8, the modeling apparatus according to an embodiment may evaluate motion information of the myocardium obtained as a result of the modeling in step 240 (810 ). The motion information of the myocardium may include a strain distribution of the myocardium and a stress of the myocardium.

In step 810, the modeling device may evaluate the motion information of the myocardium using a reference strain. The reference strain may include, for example, heart motion information (eg, a longitudinal strain) obtained from a two-dimensional echocardiographic image.

The modeling apparatus may optimize the selected parameter to reflect the physical characteristics of the user's cardio exercise by using the evaluation result of step 810 (820 ). Here, the parameter is a parameter that can reflect the physical characteristics of the aforementioned cardiac exercise. In step 820, the modeling apparatus may analyze the effect of the parameter on the reference strain, and optimize the parameter using the analysis result.

In an embodiment, using the result of the modeling in step 240, a parameter to which a physical characteristic of a cardiac exercise is applied may be optimized to reflect the physical characteristic of a user's cardiac exercise when a 3D heart shape model is generated.

The modeling apparatus may obtain a distribution of motion information of the myocardium for the entire heart by using the parameter optimized in step 820 (830 ).

The modeling device may diagnose a heart disease using the distribution of the motion information of the myocardium acquired in step 830 (step 830).

9 is a block diagram of an apparatus for modeling cardiac motion according to an embodiment.

Referring to FIG. 9, an apparatus for modeling cardiac motion according to an embodiment (hereinafter, a modeling apparatus) 900 includes an application unit 910, an acquisition unit 920, a derivation unit 930, and a modeling unit 940. And a diagnosis unit 950.

The application unit 910 may apply physical characteristics of cardiac motion to a 3D cardiac shape model specialized for a user. Here,'physical characteristics of cardiac exercise' refers to physical characteristics to be considered in cardiac exercise, and fiber orientation of the heart muscle, passive stress of the heart muscle, and active stress of the heart muscle. ) And the like may be given as an example. The physical properties of cardio exercise can be parameterized.

The application unit 910 may select a parameter that can reflect the physical characteristics of the cardiac exercise, and may apply the physical characteristics of the cardiac exercise to the heart shape model by using the selected parameter.

The acquisition unit 920 may acquire a plurality of echocardiographic images according to time changes in order to acquire a dynamic image. The guidance unit 930 includes a 3D heart shape model to which the physical characteristics of cardiac motion are applied and the acquired plurality of A boundary condition can be derived by fusing the two echocardiographic images. The boundary condition relates to a method of processing boundary regions or boundary points of two different images that are fused to each other. Boundary conditions can be divided into displacement boundary conditions and load boundary conditions. The displacement boundary condition is, for example, a boundary condition for displacement that indicates how each feature point of the heart in an echocardiographic image moves when it slides on the diaphragm, that is, how each position of the myocardium changes physically. And the load boundary condition is a boundary condition for the force applied to each part of the myocardium inside the heart.

The modeling unit 940 may model a user's cardiac motion by using the boundary condition derived from the deriving unit 930.

The diagnosis unit 95 may diagnose a heart disease using the result of modeling by the modeling unit 940. For a detailed method of diagnosing a heart disease by the diagnosis unit 940 using the modeling result, refer to the description of FIG. 8 described above.

10 is a block diagram of an apparatus for modeling cardiac motion according to another embodiment.

Referring to FIG. 10, a modeling apparatus 1000 according to an embodiment includes a generator 1010, an application unit 1020, an acquisition unit 1030, a derivation unit 1040, a modeling unit 1050, and a diagnosis unit. (1060).

The generator 1010 may generate a user-specific 3D heart shape model based on the user's CT image.

The application unit 1020 may apply the physical characteristics of the cardiac exercise to the user-specific heart shape model generated by the generation unit 1010.

The acquisition unit 1030 may acquire a plurality of echocardiographic images according to time changes in order to acquire a dynamic image.

The derivation unit 1040 may derive a boundary condition by fusing a 3D cardiac shape model to which a physical characteristic of cardiac motion is applied and a plurality of acquired echocardiographic images.

The derivation unit 1040 may include a fusion unit 1045 for fusing the user's 3D heart shape model and the user's echocardiographic image. The fusion unit 1045 may fuse the 3D heart shape model and the echocardiographic image in consideration of a position change of a feature point in the user's echocardiographic image over time.

The modeling unit 1050 may model a user's cardiac motion using the boundary condition derived from the deriving unit 1040.

The diagnosis unit 1060 may diagnose a heart disease using a result of modeling by the modeling unit 1050. The diagnosis unit 1060 may include an evaluation unit 1061, an optimization unit 1062, and an acquisition unit 1063.

The evaluation unit 1061 may evaluate the motion information of the myocardium acquired as a result of modeling by the modeling unit 1040. The diagnosis unit 1060 may optimize a parameter to reflect the physical characteristics of the user's cardiac exercise using the evaluation result of the evaluation unit 1061.

The optimizer 1062 may optimize a parameter to reflect the physical characteristics of the user's cardiac exercise using the result of modeling by the modeling unit 1050. The optimizer 1062 may optimize the parameters to reflect the physical characteristics of the user's cardio exercise by analyzing the sensitivity of the main parameters according to the modeling result and updating the parameters.

The acquisition unit 1063 may acquire a distribution of motion information of the myocardium for the entire heart of the user by using the main parameters optimized by the optimizer 1062. The diagnosis unit 1050 may diagnose a heart disease by using the distribution of motion information of the myocardium.

The apparatus described above may be implemented as a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the devices and components described in the embodiments include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It can be implemented using one or more general purpose computers or special purpose computers, such as a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of software. For the convenience of understanding, although it is sometimes described that one processing device is used, one of ordinary skill in the art, the processing device is a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it may include. For example, the processing device may include a plurality of processors or one processor and one controller. In addition, other processing configurations are possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of these, configuring the processing unit to behave as desired or processed independently or collectively. You can command the device. Software and/or data may be interpreted by a processing device or to provide instructions or data to a processing device, of any type of machine, component, physical device, virtual equipment, computer storage medium or device. , Or may be permanently or temporarily embodyed in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. -A hardware device specially configured to store and execute program instructions such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operation of the embodiment, and vice versa.

As described above, although the embodiments have been described by the limited embodiments and the drawings, various modifications and variations are possible from the above description to those of ordinary skill in the art. For example, the described techniques are performed in a different order from the described method, and/or components such as a system, structure, device, circuit, etc. described are combined or combined in a form different from the described method, or other components Alternatively, even if substituted or substituted by an equivalent, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and claims and equivalents fall within the scope of the claims to be described later.

900: modeling device
910: application part
920: acquisition unit
930: lead part
940: modeling unit
950: diagnostic unit

Claims (21)

Generating a three-dimensional heart shape model;
Applying physical characteristics of cardiac motion to the cardiac shape model;
Acquiring a plurality of echocardiographic images according to time changes in order to acquire a dynamic image;
Deriving a boundary condition by fusing the 3D heart shape model to which the physical characteristics are applied and the acquired plurality of echocardiographic images; And
Modeling a user's cardio exercise using the boundary condition
Including,
The step of applying the physical properties of the cardiac exercise
Selecting a parameter that can reflect the physical characteristics of the cardiac exercise; And
Applying physical characteristics of the cardiac motion to the cardiac shape model using the selected parameter
Containing, a method of modeling cardio exercise. delete The method of claim 1,
The three-dimensional heart shape model,
A method of modeling cardiac motion, which is generated based on a 2D image or a 3D image captured by the user. The method of claim 3,
A method of modeling cardiac motion, wherein the 2D image or the 3D image photographed by the user includes a CT image. delete The method of claim 1,
The physical properties of the cardiac exercise,
A method of modeling cardiac motion, comprising at least one of fiber orientation of the heart muscle, passive stress of the heart muscle, and active stress of the heart muscle. The method of claim 1,
The step of deriving the boundary condition,
Fusing the 3D cardiac shape model and the echocardiographic images in consideration of a position change of a feature point in the plurality of echocardiographic images over time
A method for modeling cardio exercise comprising a. The method of claim 7,
The step of deriving the boundary condition,
Searching for a displacement boundary condition among the boundary conditions using the fused 3D heart shape model and echocardiographic images.
A method for modeling cardio exercise comprising a. The method of claim 1,
The boundary condition is,
Including displacement boundary conditions and load boundary conditions,
How to model cardio exercise. The method of claim 1,
Diagnosing a heart disease using the modeling result
Including more,
Diagnosing the heart disease,
Optimizing the selected parameter by using the modeling result;
Obtaining a distribution of motion information of a myocardium using the optimized parameter; And
Diagnosing the heart disease using the distribution of the motion information of the myocardium
A method for modeling cardio exercise comprising a. The method of claim 10,
The step of optimizing the selected parameter,
Evaluating the motion information of the myocardium acquired as a result of the modeling; And
Optimizing the selected parameter to reflect the physical characteristics of the user's cardio exercise using the evaluation result
Containing, a method of modeling cardio exercise. The method of claim 11,
The myocardial motion information,
A method for modeling cardiac exercise, including the myocardial strain distribution and the myocardial stress. The method of claim 11,
The step of evaluating the motion information of the myocardium,
Evaluating the motion information of the myocardium using a reference strain
Containing, a method of modeling cardio exercise. The method of claim 13,
The reference strain,
A method of modeling cardiac motion, comprising heart motion information obtained from the plurality of echocardiographic images. The method of claim 13,
The step of optimizing,
Analyzing the effect of the parameter on the reference strain; And
Optimizing the parameter using the analysis result
Containing, a method of modeling cardio exercise. A computer-readable recording medium on which a program for executing the method of any one of claims 1, 3 to 4, and 6 to 15 is recorded. An application unit for applying physical characteristics of cardiac motion to the 3D cardiac shape model;
An acquisition unit for acquiring a plurality of echocardiographic images according to a time change to acquire a dynamic image;
A derivation unit for deriving a boundary condition by fusing the 3D heart shape model to which the physical characteristics are applied and the plurality of acquired echocardiographic images; And
Modeling unit for modeling the user's cardio exercise using the boundary condition
Including,
The application unit,
An apparatus for modeling cardiac motion, which selects a parameter that can reflect the physical characteristics of the cardiac exercise and applies the physical characteristics of the cardiac exercise to the heart shape model by using the selected parameter. delete The method of claim 17,
The lead-out unit,
A fusion unit that fuses the 3D heart shape model and the plurality of echocardiographic images in consideration of a position change of a feature point in the plurality of echocardiographic images over time
Containing, an apparatus for modeling cardiac motion. The method of claim 17,
Diagnosis unit for diagnosing heart disease using the result of the modeling
Including more,
The diagnostic unit,
An optimization unit that optimizes the selected parameter by using the modeling result; And
An acquisition unit that acquires a distribution of motion information of the myocardium using the optimized parameter
Including,
A device for modeling cardiac movement, diagnosing the heart disease by using the distribution of the movement information of the myocardium. The method of claim 20,
The diagnosis unit
An evaluation unit evaluating the motion information of the myocardium acquired as a result of the modeling
Including more,
Using the evaluation result, the device for modeling cardiac motion, optimizing the parameter to reflect the physical characteristics of the user's cardiac motion.

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