A method for simulating the stress distribution of a heart valve and perivalvular tissue by a guide wire

Abstract

The present application relates to the technical field of medical devices, and discloses a method for simulating stress distribution of a guide wire on a heart valve and perivalvular tissue, comprising: obtaining image data of the heart valve and perivalvular tissue of a patient before surgery, and obtaining strain of each structure; constructing a material constitutive equation of the heart valve and perivalvular tissue to determine stress-strain relationship of each structure under different stress conditions; determining first stress distribution of the heart valve and perivalvular tissue according to the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions; obtaining second stress distribution of the guide wire when the guide wire is in different morphological positions in the heart valve; and determining third stress distribution of the guide wire on the heart valve and perivalvular tissue when the guide wire is in different morphological positions in the heart valve according to the first stress distribution and the second stress distribution, so as to accurately perform intraoperative navigation of the guide wire.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method, apparatus, equipment and medium for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue. Background Technology

[0002] Interventional cardiac valve surgery, as a novel technique for diagnosing and treating valvular heart disease, has gradually become the preferred treatment option in clinical practice due to its advantages such as minimal invasiveness and low risk. However, limited by the surgeon's potential lack of experience and operational errors, as well as the lack of direct visualization of the individualized anatomical structure of the heart valves and perivalvular tissues in patients, surgical complications such as vascular wall injury, valve displacement, and conduction block remain frequent, and in severe cases, they can endanger the patient's life. Therefore, intraoperative navigation and risk warning are extremely important.

[0003] In cardiac valve interventional surgery, precise control of the guidewire and real-time force analysis are crucial for reducing complications. However, most cardiac valve interventional surgeries, such as transcatheter aortic valve implantation (TAVI), rely solely on the surgeon's experience and two-dimensional image guidance during the procedure, lacking real-time and intuitive guidewire force feedback, resulting in poor guidewire navigation. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and medium for simulating the stress distribution of a guidewire on the heart valve and perivalvular tissue. This can solve the technical problem that during interventional heart valve surgery, the reliance on the doctor's experience and two-dimensional image guidance, coupled with the lack of real-time and intuitive guidewire force feedback, results in poor guidewire navigation.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue, comprising the following steps:

[0006] Obtain preoperative imaging data of the patient's heart valves and perivalvular tissues;

[0007] Based on the imaging data, the strain of each structure in the heart valve and perivalvular tissue is obtained, and the material constitutive equation of the heart valve and perivalvular tissue is constructed using the strain of all structures to determine the stress-strain relationship of each structure under different stress conditions.

[0008] Based on the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions, the first stress distribution of the heart valve and perivalvular tissue is determined.

[0009] Based on the three-dimensional model of the heart valve and perivalvular tissue, as well as the three-dimensional model of the guidewire, the placement of the guidewire in the heart valve was simulated to obtain the second stress distribution of the guidewire when it was in different morphological positions in the heart valve.

[0010] Based on the first and second stress distributions, the third stress distribution of the guidewire on the heart valve and perivalvular tissue is determined when the guidewire is in different morphological positions within the heart valve.

[0011] Optionally, the step of obtaining the strain of each structure in the heart valve and perivalvular tissue based on image data, and constructing the material constitutive equation of the heart valve and perivalvular tissue using the strain of all structures, includes:

[0012] Three-dimensional models of the patient's heart valves and perivalvular tissues at different times were established based on imaging data, and the pressure and strain of each structure in the heart valves and perivalvular tissues at different times were obtained through the three-dimensional models.

[0013] Based on imaging data, the volume and pressure of each structure in the heart valve and perivalvular tissue at different times were obtained;

[0014] Pressure, strain, and volume of each structure in the heart valve and perivalvular tissue were extracted at equal intervals, and pressure-volume dataset and pressure-strain dataset were constructed using the Latin hypercube sampling method.

[0015] Based on the pressure-volume dataset, the pressure-strain dataset, and the pre-defined exponential relationship between volume and pressure of the heart valve and perivalvular tissue, the material constitutive equation is constructed using the Holzapfel–Ogden model.

[0016] Optionally, the Holzapfel–Ogden model is:

[0017] ;

[0018] In the formula, , For matrix response parameters, , For parameters of myocardial fiber materials, , The proportion of myocardial fiber materials, , Indicates shear effect, , For the right Cauchy-Green tensor, For deformation gradient, and These are the invariants along the cardiomyocyte direction in the reference configuration. These are coupling invariants.

[0019] Optionally, the step of simulating the placement of a guidewire in the heart valve based on a three-dimensional model of the heart valve and perivalvular tissue and a three-dimensional model of the guidewire, to obtain the second stress distribution of the guidewire when it is in different morphological positions within the heart valve, includes:

[0020] Using 3D printing technology, a three-dimensional model of the patient's heart valve and perivalvular tissue was created, and guidewire models corresponding to multiple guidewires with different shapes and positions were also created.

[0021] Multiple guidewire models with different shapes and positions were placed into the three-dimensional model of the patient's heart valve and perivalvular tissue to simulate the heart valve and perivalvular tissue after the guidewire was placed during the operation.

[0022] Using the finite element method, based on the simulated heart valve and perivalvular tissue after guidewire insertion, the second stress distribution of the guidewire in different morphological positions within the heart valve was obtained.

[0023] Embodiments of the present invention also provide a device for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue, comprising:

[0024] The cardiac imaging acquisition module is used to acquire imaging data of the patient's heart valves and perivalvular tissues before surgery;

[0025] The stress-strain acquisition module is used to acquire the strain of each structure in the heart valve and perivalvular tissue based on image data, and to construct the material constitutive equation of the heart valve and perivalvular tissue based on the strain of all structures, so as to determine the stress-strain relationship of each structure under different stress conditions.

[0026] The first stress detection module is used to determine the first stress distribution of the heart valve and perivalvular tissue based on the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions.

[0027] The second stress detection module is used to simulate the placement of a guidewire in the heart valve based on the three-dimensional model of the heart valve and perivalvular tissue and the three-dimensional model of the guidewire, so as to obtain the second stress distribution of the guidewire when it is in different morphological positions in the heart valve.

[0028] The third stress detection module is used to determine the third stress distribution of the guidewire on the heart valve and perivalvular tissue when the guidewire is in different morphological positions in the heart valve, based on the first stress distribution and the second stress distribution.

[0029] Embodiments of the present invention also provide a computer device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for simulating the stress distribution of the guidewire on the heart valve and perivalvular tissue.

[0030] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for simulating the stress distribution of the guidewire on the heart valve and perivalvular tissue.

[0031] The method for simulating the stress distribution of the heart valve and perivalvular tissue using a guidewire provided by this invention has the following beneficial effects:

[0032] By using preoperative imaging data of the heart valve and perivalvular tissue, the strain of each structure in the heart valve and perivalvular tissue can be obtained. Based on this strain, a material constitutive equation for the heart valve and perivalvular tissue can be constructed to obtain the stress-strain relationship of each structure in the heart valve and perivalvular tissue under different stress conditions. This yields the first stress distribution of the heart valve and perivalvular tissue. After inserting a guidewire into a simulated heart valve, the second stress distribution of the guidewire in different morphological positions within the heart valve can be determined. Furthermore, based on the first and second stress distributions, the third stress distribution of the guidewire on the heart valve and perivalvular tissue in different morphological positions within the heart valve can be determined.

[0033] In other words, this invention simulates the distribution of the third stress on the heart valve and perivalvular tissue when the guidewire is in each morphological position during the procedure. The mapping relationship between the guidewire morphological position and the third stress distribution can help determine the real-time stress distribution on the heart valve and perivalvular tissue during heart valve intervention surgery based on the real-time morphological position of the guidewire, thereby obtaining the real-time stress state of the guidewire. This can be used for guidewire navigation during the procedure, enabling precise control of the guidewire during heart valve intervention surgery and thus improving the success rate of the surgery. Attached Figure Description

[0034] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0035] Figure 1 This is a flowchart of a method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue according to an embodiment of the present invention. Figure 1 ;

[0036] Figure 2This is a prediction diagram of material constitutive equation parameters provided according to an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of a method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue according to an embodiment of the present invention. Figure 2 . Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the embodiments of the present invention to facilitate a better understanding of the invention. However, the technical solutions claimed in the present invention can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0039] One embodiment of the present invention relates to a method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue. The implementation details of the method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue in this embodiment are described below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0040] The specific procedure for simulating the stress distribution of the heart valve and perivalvular tissue using the guidewire in this embodiment can be described as follows: Figure 1 As shown, it includes:

[0041] Step 101: Obtain imaging data of the patient's heart valves and perivalvular tissues before surgery.

[0042] The imaging data includes preoperative medical imaging data such as computed tomography angiography (CTA), magnetic resonance imaging (MRI), and ultrasound of the patient's heart valves and perivalvular tissues.

[0043] Step 102: Obtain the strain of each structure in the heart valve and perivalvular tissue based on the image data, and construct the material constitutive equation of the heart valve and perivalvular tissue using the strain of all structures to determine the stress-strain relationship of each structure under different stress conditions.

[0044] Specifically, a three-dimensional model of the patient's heart valve and perivalvular tissue at different times is established based on imaging data. The pressure and strain of each structure in the heart valve and perivalvular tissue at different times are obtained through the three-dimensional model. The volume and pressure of each structure in the patient's heart valve and perivalvular tissue at different times are obtained based on imaging data. The pressure, strain, and volume of each structure in the heart valve and perivalvular tissue are extracted at equal intervals, and the pressure-volume dataset and pressure-strain dataset are constructed using the Latin hypercube sampling method. Based on the pressure-volume dataset, the pressure-strain dataset, and the pre-defined exponential relationship between the volume and pressure of the heart valve and perivalvular tissue, the material constitutive equation is constructed using the Holzapfel–Ogden (HO) model, which is used for research on hyperelastic right heart mechanics.

[0045] The HO model is represented as follows:

[0046] ;

[0047] In the formula, , For matrix response parameters, , For parameters of myocardial fiber materials, , The proportion of myocardial fiber materials, , Indicates shear effect, , It is the right Cauchy-Green tensor. It is the deformation gradient. and It is an invariant along the direction of cardiomyocytes in the reference configuration. As coupling invariants, further determination is needed.

[0048] Due to the 8 material parameters of the HO model (i.e. , , , , , , , The parameters are strongly correlated, therefore parameter dimensionality reduction can be performed using the following formula:

[0049] ;

[0050] Because different patients have different myocardial structures with different material parameters, in the formula, , , , The correction factor for the actual patient's heart model needs to be calculated iteratively to fit the data. , , , , , , , This serves as an empirical reference value for a healthy heart model.

[0051] Therefore, in this embodiment, it is necessary to determine the correction factor in the HO model. , , , This leads to the construction of the material constitutive equations for the heart valve and perivalvular tissue.

[0052] In the specific implementation, an XGBoost machine learning model is constructed, such as... Figure 2 As shown, input , , , , , Given an initial , , , The value range is [0.1, 5]. 4 Output through iterative training , , , This enables rapid prediction of individualized material constitutive parameters for different locations in a patient's heart model, such as the ventricle and aorta.

[0053] Step 103: Based on the strain of each structure in the heart valve and perivalvular tissue, and the stress-strain relationship of each structure under different stress conditions, determine the first stress distribution of the heart valve and perivalvular tissue.

[0054] Step 104: Based on the three-dimensional model of the heart valve and perivalvular tissue and the three-dimensional model of the guidewire, simulate the placement of the guidewire in the heart valve to obtain the second stress distribution of the guidewire when it is in different morphological positions in the heart valve.

[0055] Specifically, 3D printing technology is used to create a three-dimensional model of the patient's heart valve and perivalvular tissue, and guidewire models corresponding to multiple guidewires with different shapes and positions are also created. The guidewire models corresponding to the multiple guidewires with different shapes and positions are then placed into the three-dimensional model of the patient's heart valve and perivalvular tissue to simulate the heart valve and perivalvular tissue after the guidewire is placed during the operation. Finite element analysis is used to obtain the second stress distribution of the guidewire when it is in different shapes and positions in the heart valve, based on the simulated heart valve and perivalvular tissue after the guidewire is placed.

[0056] Step 105: Based on the first stress distribution and the second stress distribution, determine the third stress distribution of the guidewire on the heart valve and perivalvular tissue when the guidewire is in different morphological positions within the heart valve.

[0057] In practice, a patient model based on material constitutive model is constructed using 3D printing technology, and a guidewire is inserted to have different morphological positions in the patient model. Then, a guidewire beam element model is constructed, and the stress distribution of the guidewire at different morphological positions (i.e., the second stress distribution) and the stress of the guidewire on the model (i.e., the third stress distribution) are obtained based on experimental methods and finite element analysis.

[0058] First, an experimental setup was constructed, including a 3D-printed three-dimensional model of the patient's heart valve and perivalvular tissue, a guidewire instrument for cardiac valve interventional surgery, an in vivo environment simulation system, and a parameter detection device. The individualized patient model (i.e., the 3D model of the heart valve and perivalvular tissue) was 3D printed using materials such as hydrogel, possessing the patient's true material constitutive properties. The in vivo environment simulation system simulates the actual boundary conditions within the patient's cardiovascular system. The parameter detection device includes flow sensors, pressure sensors, strain sensors, and temperature sensors, which can monitor changes in parameters such as pressure, strain, and temperature in real time during the experiment. During the experiment, the guidewire instrument for cardiac valve interventional surgery was manipulated to achieve different morphological and positional distributions within the patient model, while simultaneously acquiring real-time changes in the pushing force measured on the instrument. The parameter detection device acquired the required parameter changes, and the corresponding stress distribution was calculated. In the finite element analysis, the guidewire was set as a beam element model. Based on the guidewire material properties, constraints and boundary conditions were set in finite element analysis software such as ABAQUS, with each constraint corresponding to a different morphological and positional guidewire position in the experiment. The stress distribution of the guidewire and the stress exerted by the guidewire on the model were calculated.

[0059] In this embodiment, by using the patient's preoperative imaging data of the heart valve and perivalvular tissue, the strain of each structure in the heart valve and perivalvular tissue can be obtained. Based on this strain, the material constitutive equation of the heart valve and perivalvular tissue can be constructed to obtain the stress-strain relationship of each structure in the heart valve and perivalvular tissue under different stress conditions, thereby obtaining the first stress distribution of the heart valve and perivalvular tissue. After inserting the guidewire into the simulated heart valve, the second stress distribution of the guidewire when it is in different morphological positions in the heart valve can be determined. Then, based on the first stress distribution and the second stress distribution, the third stress distribution of the guidewire on the heart valve and perivalvular tissue when it is in different morphological positions in the heart valve can be determined.

[0060] In other words, this embodiment simulates the distribution of the third stress on the heart valve and perivalvular tissue when the guidewire is in each position during the procedure. The mapping relationship between the guidewire position and the third stress distribution can help determine the real-time stress distribution on the heart valve and perivalvular tissue during heart valve intervention surgery based on the real-time position of the guidewire, thus obtaining the real-time stress state of the guidewire. This can be used for guidewire navigation during the procedure, enabling precise control of the guidewire during heart valve intervention surgery and improving the success rate of the surgery.

[0061] In particular, when using guidewire navigation based on the distribution of the third stress, the distribution of the third stress on the heart valve and perivalvular tissue can be determined by combining the guidewire's position in different shapes within the heart valve and the patient's real-time position of the guidewire during the procedure. This allows for the intraoperative navigation of the guidewire based on the real-time stress distribution on the heart valve and perivalvular tissue.

[0062] Specifically, a stress distribution prediction model for the heart valve and perivalvular tissue is established based on the first, second, and third stress distributions. Using image data of the patient's heart valve and perivalvular tissue at each moment after guidewire placement during the procedure, the morphological position of the guidewire at each moment is obtained to determine the second stress distribution of the guidewire at each moment. The first stress distribution and the second stress distribution of the guidewire at each moment are input into the stress distribution prediction model to obtain the third stress distribution of the guidewire on the heart valve and perivalvular tissue at each moment, which serves as the real-time stress distribution. Based on the real-time stress distribution, with the goal of reducing the stress distribution of the guidewire on the heart valve and perivalvular tissue during the procedure, a motion path is planned for the guidewire, completing the intraoperative navigation of the guidewire.

[0063] In practical implementation, the following data can be used as a dataset to construct a deep learning-based guidewire-patient 3D model stress distribution prediction model: the first stress distribution of the heart valve and perivalvular tissue; the second stress distribution of the guidewire when it is in different morphological positions within the heart valve; and the third stress distribution of the guidewire on the heart valve and perivalvular tissue. By acquiring multiple 2D perspective views of the heart valve and perivalvular tissue from various angles after the guidewire is placed in the patient's heart valve, and using image registration methods based on these views, the morphological position of the guidewire within the heart valve can be located. Based on this, a 3D model of the guidewire can be quickly reconstructed and placed into a personalized patient model, enabling intraoperative guidewire tracking. Furthermore, based on the guidewire-patient 3D model stress distribution prediction model, the stress distribution of the guidewire and the personalized patient model can be automatically and quickly obtained.

[0064] The procedure involves real-time input of multimodal image data, including multi-angle two-dimensional perspective views of the patient, and rapid output of a three-dimensional cloud map and two-dimensional perspective view of the stress field, including the patient model and guidewire. The constructed deep learning models include, but are not limited to, UNet and VNet. Image registration methods include, but are not limited to, LCD, 2D-3D MatchNet, ternary loss function, and VGG-Net, among others.

[0065] In one example, when obtaining the morphological position of the guidewire at each moment during the procedure based on the imaging data of the patient's heart valves and perivalvular tissue after guidewire placement, the procedure involves obtaining preoperative two-dimensional images of the heart valves and perivalvular tissue in the coronal, sagittal, and horizontal planes based on preoperative imaging data of the heart valves and perivalvular tissue; obtaining intraoperative two-dimensional images of the heart valves and perivalvular tissue in the coronal, sagittal, and horizontal planes based on the imaging data of the heart valves and perivalvular tissue after guidewire placement; registering the intraoperative two-dimensional images of each angle with the corresponding preoperative two-dimensional images to determine the angle with the highest registration accuracy; and obtaining the morphological position of the guidewire during the procedure based on the intraoperative two-dimensional image corresponding to the angle with the highest registration accuracy.

[0066] In one example, the movement of the guidewire during the procedure can be determined based on its various morphological positions. Then, by combining the movement of the guidewire during the procedure, the second stress distribution of the guidewire at each morphological position, and the third stress distribution of the guidewire on the heart valve and perivalvular tissue, intraoperative guidewire navigation can be performed.

[0067] For example, AR software and wearable devices can be used to visualize the guidewire's position and movement in real time, dynamically track the guidewire's movement and stress field within a 3D model of the patient's heart valve and perivalvular tissue during surgery, and predict the risk of mechanically related complications. The AR immersive visualization interface can be displayed in real time through holographic displays, head-mounted displays, etc., and includes, but is not limited to, 3D images of the patient's aortic valve and perivalvular tissue (including the prosthetic valve), the mechanical field of the heart valve and perivalvular tissue, the guidewire model's position and real-time movement, etc., providing intraoperative complication prediction and early warning information.

[0068] The following specific embodiment illustrates the detailed process of using the guidewire simulation method of the present invention to realize the intraoperative navigation method of the guidewire in cardiac valve interventional surgery, such as... Figure 2 As shown, it includes:

[0069] 1. Obtain preoperative multimodal imaging data of the patient;

[0070] 2. Based on machine learning methods, preoperative multimodal imaging data of patients are processed to obtain the strain variables of structures such as blood vessel wall and myocardium. Individualized constitutive equations of blood vessel and myocardial materials for patients are constructed, and stress distribution is calculated using finite element analysis. Stress distribution dataset of three-dimensional model of patient's heart valve and perivalvular tissue is constructed.

[0071] 3. Construct a patient model based on material constitutive modeling using 3D printing technology, and insert guidewires to achieve different morphological and positional distributions within the patient model. Construct a guidewire beam element model, and obtain the stress distribution of the guidewires at different morphological positions and the stress exerted by the guidewires on the model based on experimental methods and finite element analysis, thereby constructing a deep learning dataset.

[0072] 4. Combining the datasets obtained in the above two steps, construct a deep learning-based guidewire-patient three-dimensional model stress distribution prediction model;

[0073] 5. Acquire multimodal imaging data of the patient during the operation, including multi-angle two-dimensional perspective views;

[0074] 6. The image registration method is used to locate the position of the guidewire in the patient's individualized model, quickly reconstruct the three-dimensional model of the guidewire and place it into the patient's individualized model to achieve intraoperative guidewire tracking;

[0075] 7. Based on the guidewire-patient 3D model stress distribution prediction model, automatically and quickly obtain the stress distribution of the guidewire and patient individualized model;

[0076] 8. AR software and wearable devices are used to visualize the position and movement of the guidewire in real time, dynamically track the movement and stress field of the guidewire in the three-dimensional model of the patient's heart valve and perivalvular tissue during the operation, and predict the risk of mechanical complications.

[0077] This embodiment enables real-time positioning, force analysis, and precise control of the guidewire during the procedure, which is of great clinical significance for reducing TAVR complications, improving the success rate of the procedure, and ultimately improving patient prognosis.

[0078] Experiments and finite element analysis were used to measure and calculate the stress distribution of the guidewire, the force and stress distribution in the contact area between the guidewire and the patient, and to obtain the stress fields of guidewires with different shapes. This will help the subsequent deep learning model to more accurately describe the morphological changes of the guidewire in complex vascular environments, thereby improving the accuracy and reliability of navigation.

[0079] Using deep learning models, real-time image data acquired during surgery can be analyzed quickly and accurately, and key information such as the position, shape, stress of the guidewire, and stress distribution of the patient affected by the guidewire can be extracted. It has high accuracy and generalization ability, and can effectively assist clinicians in performing precise guidewire manipulation.

[0080] Using VR technology to visualize key intraoperative information can intuitively display the three-dimensional model, position, stress field of the guidewire, as well as the structure and stress distribution of surrounding blood vessels and tissues, thereby helping clinicians better understand the surgical environment and improve the efficiency and safety of surgical procedures.

[0081] The steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split into multiple steps. As long as they include the same logical relationship, they are all within the protection scope of this invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the protection scope of this invention.

[0082] Another embodiment of the present invention relates to a device for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue. The implementation details of this device are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. The device for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue in this embodiment includes:

[0083] The cardiac imaging acquisition module is used to acquire imaging data of the patient's heart valves and perivalvular tissues before surgery.

[0084] The constitutive equation construction module is used to obtain the strain of each structure in the heart valve and perivalvular tissue based on imaging data, and to construct the material constitutive equation of the heart valve and perivalvular tissue using the strain of all structures, so as to determine the stress-strain relationship of each structure under different stress conditions.

[0085] The first stress detection module is used to determine the first stress distribution of the heart valve and perivalvular tissue based on the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions.

[0086] The second stress detection module is used to simulate the placement of a guidewire in the heart valve based on the three-dimensional model of the heart valve and perivalvular tissue and the three-dimensional model of the guidewire, so as to obtain the second stress distribution of the guidewire when it is in different morphological positions in the heart valve.

[0087] The third stress detection module is used to determine the third stress distribution of the guidewire on the heart valve and perivalvular tissue when the guidewire is in different morphological positions in the heart valve, based on the first stress distribution and the second stress distribution.

[0088] It is not difficult to see that this embodiment is a device embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above embodiments.

[0089] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by this invention; however, this does not mean that other units are absent from this embodiment.

[0090] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method for simulating the stress distribution of guidewires on heart valves and perivalvular tissues in the above embodiments.

[0091] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0092] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0093] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0094] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for simulating the stress distribution of a guidewire on a heart valve and perivalvular tissue, characterized in that, The method includes: Obtain imaging data of the patient's heart valves and perivalvular tissues before surgery; Based on the imaging data, the strain of each structure in the heart valve and perivalvular tissue is obtained, and the material constitutive equation of the heart valve and perivalvular tissue is constructed using the strain of all structures to determine the stress-strain relationship of each structure under different stress conditions. Based on the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions, the first stress distribution of the heart valve and perivalvular tissue is determined. Based on the three-dimensional model of the heart valve and perivalvular tissue, as well as the three-dimensional model of the guidewire, the placement of the guidewire in the heart valve was simulated to obtain the second stress distribution of the guidewire when it was in different morphological positions in the heart valve. Based on the first and second stress distributions, determine the third stress distribution of the guidewire on the heart valve and perivalvular tissue when the guidewire is in different morphological positions within the heart valve. The step of simulating the placement of a guidewire in a heart valve based on a three-dimensional model of the heart valve and perivalvular tissue, and a three-dimensional model of the guidewire, to obtain the second stress distribution of the guidewire when it is in different morphological positions within the heart valve, includes: Using 3D printing technology, a three-dimensional model of the patient's heart valve and perivalvular tissue was created, along with guidewire models corresponding to multiple guidewires with different shapes and positions. These guidewire models were then inserted into the three-dimensional model of the patient's heart valve and perivalvular tissue to simulate the heart valve and perivalvular tissue after guidewire placement during surgery. Using finite element analysis, based on the simulated heart valve and perivalvular tissue after guidewire placement, the second stress distribution of the guidewire in different shapes and positions within the heart valve was obtained. After determining the distribution of the third stress on the heart valve and perivalvular tissue when the guidewire is in different morphological positions within the heart valve, the method further includes: Based on the first, second, and third stress distributions, a stress distribution prediction model for the heart valve and perivalvular tissue is established. Using image data of the patient's heart valve and perivalvular tissue at each moment after guidewire placement during the procedure, the morphological position of the guidewire at each moment is obtained to determine the second stress distribution of the guidewire at each moment. The first stress distribution and the second stress distribution of the guidewire at each moment are input into the stress distribution prediction model to obtain the third stress distribution of the guidewire on the heart valve and perivalvular tissue at each moment during the procedure, which serves as the real-time stress distribution.

2. The method for simulating the stress distribution of the heart valve and perivalvular tissue by the guidewire according to claim 1, characterized in that, The process of obtaining the strain of each structure in the heart valve and perivalvular tissue based on imaging data, and constructing the material constitutive equation of the heart valve and perivalvular tissue using the strain of all structures, includes: Three-dimensional models of the patient's heart valves and perivalvular tissues at different times were established based on imaging data, and the pressure and strain of each structure in the heart valves and perivalvular tissues at different times were obtained through the three-dimensional models. Based on imaging data, the volume and pressure of each structure in the heart valve and perivalvular tissue at different times were obtained; Pressure, strain, and volume of each structure in the heart valve and perivalvular tissue were extracted at equal intervals, and pressure-volume dataset and pressure-strain dataset were constructed using the Latin hypercube sampling method. Based on the pressure-volume dataset, the pressure-strain dataset, and the pre-defined exponential relationship between volume and pressure of the heart valve and perivalvular tissue, the material constitutive equation is constructed using the Holzapfel–Ogden model.

3. The method for simulating the stress distribution of the heart valve and perivalvular tissue by the guidewire according to claim 2, characterized in that, The Holzapfel–Ogden model is as follows: ； In the formula, , For matrix response parameters, , For parameters of myocardial fiber materials, , The proportion of myocardial fiber materials, , Indicates shear effect, , For the right Cauchy-Green tensor, For deformation gradient, and These are the invariants along the cardiomyocyte direction in the reference configuration. These are coupling invariants.

4. An apparatus for simulating the stress distribution of the guidewire on the heart valve and perivalvular tissue as described in any one of claims 1 to 3, characterized in that, include: The cardiac imaging acquisition module is used to acquire imaging data of the patient's heart valves and perivalvular tissues before surgery; The stress-strain acquisition module is used to acquire the strain of each structure in the heart valve and perivalvular tissue based on image data, and to construct the material constitutive equation of the heart valve and perivalvular tissue based on the strain of all structures, so as to determine the stress-strain relationship of each structure under different stress conditions. The first stress detection module is used to determine the first stress distribution of the heart valve and perivalvular tissue based on the strain of each structure in the heart valve and perivalvular tissue and the stress-strain relationship of each structure under different stress conditions. The second stress detection module is used to simulate the placement of a guidewire in the heart valve based on the three-dimensional model of the heart valve and perivalvular tissue and the three-dimensional model of the guidewire, so as to obtain the second stress distribution of the guidewire when it is in different morphological positions in the heart valve. The third stress detection module is used to determine the third stress distribution of the guidewire on the heart valve and perivalvular tissue when the guidewire is in different morphological positions in the heart valve, based on the first stress distribution and the second stress distribution.

5. A computer device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method for simulating the stress distribution of the guidewire on the heart valve and perivalvular tissue as described in any one of claims 1 to 3.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements a method for simulating the stress distribution of the guidewire on the heart valve and perivalvular tissue as described in any one of claims 1 to 3.

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