FFRCT computing devices, electronic devices, and storage media based on STL mesh and SPH simulation

Abstract

This invention discloses an FFRCT calculation device based on STL mesh and SPH simulation, comprising: a data acquisition module for reading STL files of coronary artery walls and correcting the inlet and outlet meshes of the coronary arteries; a target selection module for generating a three-dimensional point cloud array containing the STL mesh, then calculating the directed distance field from the point cloud to the STL mesh, and filtering blood particles and vascular particles from the point cloud based on the directed distance; a calculation module for calculating the vessel centerline; a simulation module for numerically simulating the blood flow process using the SPH method under inlet and outlet pressures and no-slip boundary conditions of the vessel wall; and an output module for sampling along the vessel centerline and obtaining the FFR value through post-processing of the simulation results. This invention forms a technical route that combines high accuracy and high effectiveness from reading the STL mesh of the coronary artery wall to calculating and outputting the FFR value.

Description

Technical Field

[0001] This invention belongs to the field of biomechanics, and particularly relates to an FFRCT computing device, electronic device and storage medium based on STL mesh and SPH simulation. Background Technology

[0002] FFRCT is a non-invasive examination combining coronary CT angiography and computed tomography fluid dynamics (CTFL) simulation. It is used to assess the impact of coronary artery stenosis on blood flow and to help determine whether a patient needs stenting or bypass surgery. The accuracy and effectiveness of obtaining the fractional flow reserve (FFR) from FFRCT are two key criteria for evaluating FFRCT technology. While mature and efficient techniques exist for converting coronary CT angiography into vascular surface meshes (such as STL meshes), the process from vascular surface mesh to calculating the output FFR value involves a large amount of complex data processing and fluid dynamics calculations. Therefore, it is crucial to clarify a technical approach that achieves both high accuracy and high effectiveness. Summary of the Invention

[0003] Objective of the Invention: The objective of this invention is to provide an FFRCT calculation device, electronic device, and storage medium based on STL mesh and SPH simulation. This establishes a technical route that combines high accuracy and high efficiency from reading the STL mesh of the coronary artery wall to calculating and outputting the FFR value.

[0004] Technical solution: The present invention provides an FFRCT computational device based on STL mesh and SPH simulation, comprising:

[0005] The data acquisition module is used to read the STL file of the coronary artery wall and correct the inlet and outlet mesh of the coronary artery.

[0006] The target filtering module is used to generate a 3D point cloud array containing an STL mesh, and then calculate the directed distance field from the point cloud to the STL mesh. Based on the directed distance, blood particles and blood vessel particles can be filtered out from the point cloud.

[0007] The calculation module is used to calculate the centerline of blood vessels;

[0008] The simulation module is used to numerically simulate blood flow processes using the SPH method under conditions of inlet and outlet pressures and no slippage boundary of the blood vessel wall;

[0009] The output module is used to sample along the centerline of the blood vessel and obtain the FFR value through post-processing simulation results.

[0010] Furthermore, the data acquisition module specifically includes: using the 3D visualization software Paraview to read the STL file of the coronary artery wall, cutting off each mesh near the inlet or outlet with a plane and enclosing it within a sphere, then locating the mesh vertices in the sphere through code, projecting them onto the corresponding plane, and marking the generated new triangular units as inlet and outlet units.

[0011] Furthermore, the target screening module specifically includes: generating a three-dimensional point cloud array containing an STL mesh, then calculating the directed distance field from the point cloud to the STL mesh, and filtering out blood particles and blood vessel particles from the point cloud based on the directed distance. The SPH particle relaxation algorithm is used on the blood vessel particles to fine-tune them to a position that fits the blood vessel wall.

[0012] Furthermore, the calculation module specifically includes: establishing a binary three-dimensional data volume for all generated blood particles according to their position coordinates, with the voxel corresponding to the blood particle marked as 1 and the other voxels marked as 0; obtaining the blood skeleton voxel located on the center line through the skeleton extraction algorithm; and then converting the skeleton voxel position into actual coordinates to obtain the blood vessel center line.

[0013] Furthermore, the simulation module specifically includes: setting the blood pressure at the inlet and outlet, as well as the blood density and viscosity, according to medical standards; simulating blood flow over a given time using the SPH method under conditions of no slippage at the vessel wall boundary, to obtain the time-varying pressure and velocity of blood particles. Excluding the initial stage, the pressure of the blood particles is output according to a given time step.

[0014] Furthermore, the output module specifically includes: sampling along the centerline of the blood vessel and obtaining the FFR value through post-processing simulation results, including: sampling the calculation points of the FFR value along the centerline of the blood vessel at a given step size, calculating the FFR value at the sampling point using the SPH smoothing function based on the pressure of blood particles, and performing time averaging.

[0015] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0016] The present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method of the present invention.

[0017] The present invention also discloses a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method of the present invention.

[0018] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: it breaks through the limitations of traditional CFD methods in blood flow simulation due to grid dependence and physiological modeling, and proposes a coronary artery blood flow function assessment device based on STL grid input and the SPH (Smooth Particle Hydrodynamics) meshless method. Compared with existing technologies, this method significantly improves the realism of modeling in terms of geometric adaptability, non-Newtonian blood modeling, vascular wall elastic response, and fluid-structure interaction simulation; it is more stable and accurate in handling complex vascular structures (such as stenosis, bifurcation, and calcification); and it improves computational efficiency through parallel computing optimization to meet the needs of rapid blood flow function reconstruction. The system can output multi-physics field results such as pressure, velocity, and shear stress, providing high-quality label support for functional index (such as FFRCT) calculation and AI model training, and has good engineering practicality and clinical application value. Attached Figure Description

[0019] Figure 1 This is a simplified schematic diagram illustrating the application environment of the technical solution of this application as an embodiment of the present application.

[0020] Figure 2 A flowchart illustrating an embodiment of this application provides a method for calculating FFRCT based on STL mesh and SPH simulation.

[0021] Figure 3 This is a structural block diagram of an FFRCT computing device based on STL mesh and SPH simulation provided in an embodiment of this application.

[0022] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0023] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided to make the description of this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The drawings are merely illustrative of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0024] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more exemplary embodiments. Numerous specific details are provided in the following description to give a full understanding of exemplary embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced with one or more of the specific details omitted, or other methods, components, steps, etc., can be employed. In other instances, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0025] Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] Figure 1 This is a simplified schematic diagram illustrating the application environment of the technical solution provided in one embodiment of this application.

[0027] A schematic diagram of the real-time environment of this application is shown in one embodiment, as follows: Figure 1 As shown, the environment of this embodiment includes a network 110 and a computer device 120. The network 110 can be a communication medium of various connection types capable of providing communication links between servers 120, such as a wired communication link or a wireless communication link. The computer device 120 can be a server, a server cluster, or other device with computing capabilities, and is not specifically limited here.

[0028] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0029] Figure 2 This is a flowchart illustrating an embodiment of the FFRCT calculation method based on STL mesh and SPH simulation provided in this application. (Refer to...) Figure 2 As shown, the FFRCT calculation method based on STL mesh and SPH simulation includes at least steps 210 to 250, which are described in detail below:

[0030] Step 210: Coronary artery wall STL mesh inlet and outlet correction and marking module.

[0031] Specifically, using visualization software, each grid near an inlet or outlet is first cut off with a plane perpendicular to the blood vessel at that location. Then, each grid near the cut-off inlet or outlet is enclosed by a sphere with its center on each plane. The radius of the sphere is adjusted so that the enclosed grids fall exactly within their respective spheres.

[0032] The code re-reads the STL file of the coronary artery wall, establishes vertex and triangular element indices for the STL mesh, and marks all triangular elements as walls. Iterates through vertex coordinates; when a vertex lies on the sphere labeled i, it projects the vertex coordinates onto the plane labeled i and marks that vertex as the corrected vertex. All triangular elements containing the corrected vertex are marked as inlet or outlet elements with the corresponding labels.

[0033] Step 220: Blood Particle and Blood Vessel Particle Generation Module.

[0034] Specifically, first, based on the span of the STL mesh in the three dimensions of length, width, and height, adjust the point cloud spacing accordingly. A 3D point cloud array is generated based on Cartesian coordinate grid points. Then, the directed distance field from the point cloud to the STL grid is calculated (points with a directed distance less than 0 are located inside the STL grid, and points with a directed distance greater than 0 are located outside the STL grid). Based on the directed distance of each point, blood particles and blood vessel particles are selected from the point cloud. When the directed distance is less than 0, the point is marked as a blood particle; when the directed distance is greater than 0 and less than 0, the point is marked as a blood particle. When the point is labeled as a blood vessel particle, δ is the point cloud spacing set in the three-dimensional Cartesian coordinate grid, that is, the unit distance of placing points in space along the X, Y, and Z directions. It is used to generate three-dimensional point clouds at equal intervals inside and outside the volume surrounded by the STL grid. These point clouds are then labeled as "blood particles" or "blood vessel particles" according to their position relative to the STL model (determined by directed distance).

[0035] The blood vessel particles determine the geometry of the flow field. The blood vessel particles directly selected by the above method are distributed according to a Cartesian grid coordinate system and do not conform to the blood vessel wall, which will affect the accuracy of subsequent SPH numerical simulations. Therefore, the SPH particle relaxation algorithm is used to fine-tune the blood vessel particles to a position conforming to the blood vessel wall. The SPH particle relaxation algorithm is as follows:

[0036] Apply a random perturbation to each blood vessel particle a ,in It is a random variable that follows a uniform distribution in [0,1]. Construct the Level-Set function for the STL grid.

[0037] Enter the loop and repeat for 1000 steps:

[0038] Calculate the pressure on vascular particle a Calculate the time step of the relaxation process. Update the position of vascular particles ; (Forces acting on vascular particles) This represents the total pressure exerted on the particle in the current step, which mainly comes from the interaction of neighboring particles; Used as a reference background pressure to stabilize the simulation system; and These represent the volumes of particle a and its neighboring particle b, respectively, and are weighting coefficients used in force calculations. It is the gradient of the kernel function with respect to particle a, used to describe the direction and intensity of the interaction between particles; the time step Δt is determined by the particle's mass m, the kernel function radius h, and the magnitude of the force it experiences, and is used to control the update rate of the particle's position. and These represent the current position and the updated position of the particle, respectively; while the point cloud spacing δ is the granularity of the 3D mesh sampling, determining the particle generation density and the minimum spacing between particles, directly affecting computational accuracy and efficiency.

[0039] calculate Directed distance to STL mesh .like ,but Located outside the wall boundary, the blood vessel particle 'a' needs to be pulled back inside the wall. The Level-Set function is used to calculate the distance on the STL mesh. The outward normal of the recent triangular unit Move the vascular particle a to a new location .

[0040] Return to the beginning of the loop.

[0041] Step 230, Calculation module for the centerline of blood vessels.

[0042] Specifically, a binary 3D data volume is created based on the position coordinates of all blood particles generated in step 220. The voxels corresponding to the blood particles are marked as 1, and the remaining voxels are marked as 0. Using a skeleton extraction algorithm, the blood skeleton voxels located at the centerline are obtained. Then, the positions of the skeleton voxels are converted back to actual coordinates, thus obtaining the blood vessel centerline. The skeleton extraction algorithm is as follows:

[0043] Set the border vector.

[0044] Entering the loop:

[0045] Set the tag to 0.

[0046] Perform the following probes sequentially in the six directions: up, down, left, right, front, and back:

[0047] Clear the border vector. Traverse the binary data volume. If the current voxel is 1, and its neighboring voxels in the current probe direction are 0, search all 26 neighboring voxels of the current voxel to obtain the number of neighboring voxels marked as 1, nNeighbor. If nNeighbor > 1, search all neighboring voxels of the current voxel marked as 1, calculate the Euler characteristic of the current voxel, and if the Euler characteristic is greater than 1, add the coordinates of the current voxel to the border vector. Continue until the traversal of the binary data volume is complete. Then traverse the border vector. For the current voxel, search all its neighboring voxels marked as 1 to check if the current voxel is a simple point, i.e., a point whose existence or absence does not change the topological properties of the geometry. If the current voxel is a simple point, change the label of its corresponding binary data volume voxel to 0. When none of the voxels in the border vector are simple points, update the label tag = tag + 1.

[0048] Until the exploration in all six directions is completed.

[0049] If tag=6, exit the loop; otherwise, return to the beginning of the loop.

[0050] Traverse the binary data volume; at this point, all voxels marked as 1 in the data volume are the skeleton points.

[0051] Step 240, Blood Flow SPH Numerical Simulation Module.

[0052] Based on medical standards, the blood pressure at the inlet and outlet, as well as blood density and viscosity, are set. Under conditions of no slippage at the vessel wall boundary, the SPH method is used to simulate blood flow over a given time period, obtaining the time-varying pressure and velocity of blood particles. Excluding the initial phase, the pressure of blood particles is output according to a given time step.

[0053] Specifically, the blood flow SPH governing equation is:

[0054]

[0055] ρ i dρ represents the density of particle i, used to describe the degree of local blood compression. i / dt: The rate of change of the density of particle i over time, reflecting a local compressibility or expansion tendency; v i : The velocity vector of particle i; v * : Inlet / outlet reference speed, used as a speed reference when setting boundary conditions; V j The volume of particle j is equal to its mass divided by its density; ∇ i W ij The SPH kernel function W is the gradient of particle i with respect to particle i, describing the spatial influence between particle i and particle j; di i / dt: The acceleration of particle i (i.e., the rate of change of velocity with time), determined by both the pressure gradient and viscosity; P * : Symmetrical average pressure of particles i and j, representing the average hydrostatic pressure between them; μ: Dynamic viscosity coefficient, used to simulate the viscous behavior of blood; v ij The velocity difference between particles i and j reflects local shear flow or velocity differences; r ij : The displacement vector (position difference) between particles i and j; |r ij |^2: The square of the distance between particles, used to normalize the strength of the interaction.

[0056] To achieve the inlet and outlet pressure boundary conditions ( and At the inlet and outlet, 3-4 layers of particles are arranged perpendicular to the flow direction to form the inlet / outlet zones, with the main flow zone between the inlet and outlet zones. When a fluid particle crosses the boundary in the inlet zone and enters the main flow zone, it is placed at a periodic position relative to the inlet zone with the same velocity and pressure. New fluid particles. When a fluid particle crosses the flow field boundary in the outlet region, the particle is directly deleted.

[0057] The momentum equations for particles at the inlet and outlet boundaries are corrected as follows:

[0058]

[0059] In actual simulations, to prevent the positions of particles at the inlet section from coinciding with the original boundary particles within the section, which would lead to computational instability, the following adaptive free boundary detection algorithm is adopted to accurately locate the inlet and outlet boundary particles.

[0060] Define a previous timestep label `pre_tag` and a current label `tag` for all blood particles. Before the numerical simulation begins, set the `pre_tag` of all blood particles to 1. During the blood flow numerical simulation, iterate through all blood particles `i` and calculate... Let tag_i = 0, when If `pre_tag_i` = 1, then set `tag_i` = 1; otherwise, search for all other blood particles `j` within the smooth neighborhood of blood particle `i`. If `pre_tag_j` = 1, then set `tag_i` = 1. After traversing all blood particles `i`, set `pre_tag` = `tag`. Then, in the next time step, the blood particle with label `tag` = 1 is the boundary particle.

[0061] Step 250: Coronary artery FFR value calculation module.

[0062] Sampling points for FFR value calculation along the centerline of the blood vessel at given step intervals. Based on the pressure of the blood particles, the FFR value at the sampling point is calculated using the SPH smoothing function:

[0063]

[0064] Calculate the time average of the FFR values: .

[0065] The FFRCT calculation method based on STL mesh and SPH simulation provided in this application reads the STL file of the coronary artery wall, corrects the inlet and outlet mesh of the coronary artery, generates blood particles and vessel particles, calculates the vessel centerline, and numerically simulates the blood flow process using the SPH method under the conditions of inlet and outlet pressure and no slippage boundary of the vessel wall. Sampling is performed on the vessel centerline, and the simulation results are post-processed to finally obtain the FFR value. This forms a complete set of data analysis, calculation, and processing procedures from analyzing the coronary artery surface mesh to obtaining the coronary blood flow reserve fraction, enabling non-invasive examination of coronary symptoms and providing powerful assistance to physicians in determining treatment plans.

[0066] The following describes an embodiment of the apparatus of this application, which can be used to execute the FFRCT calculation method based on STL mesh and SPH simulation in the above embodiments of this application. Figure 3 A schematic block diagram of the FFRCT computing device based on STL mesh and SPH simulation provided in an embodiment of this application is shown. Figure 3 As shown, an embodiment of this application provides an FFRCT computing device based on STL mesh and SPH simulation, comprising:

[0067] The coronary artery wall STL mesh inlet and outlet correction and marking module 310 reads the coronary artery wall STL mesh, corrects and marks the inlet and outlet triangular units;

[0068] The blood particle and blood vessel particle generation module 320 uses the directed distance field from the point cloud to the STL mesh of the coronary artery wall to generate blood particles and blood vessel particles, and fine-tunes the position of the blood vessel particles to make them fit the body distribution.

[0069] The blood vessel centerline calculation module 330 obtains a binary data volume from the blood particles generated by the module 330, obtains the blood skeleton through the skeleton extraction algorithm, returns the skeleton position to the actual coordinates, and obtains the blood vessel centerline.

[0070] The blood flow SPH numerical simulation module 340 uses the SPH method to numerically simulate the blood flow process under the conditions of inlet and outlet pressures and no slippage boundary of the blood vessel wall, and obtains the blood particle pressure field that changes with time.

[0071] The coronary artery FFR value calculation module 350 samples along the centerline of the blood vessel to obtain the calculation location of the FFR value. It calculates the FFR value at the sampling point location through a smoothing function and the pressure field of blood particles, and performs time averaging.

[0072] The specific implementation process of the functions and roles of each module / unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0073] It is understood that these modules can be implemented through hardware, software, or a combination of both. When implemented in hardware, these modules can be implemented as one or more hardware modules, such as one or more application-specific integrated circuits (ASICs). When implemented in software, these modules can be implemented as one or more computer programs that execute on one or more processors.

[0074] The following is for reference. Figure 4 This application describes an electronic device 400 provided in one embodiment. Figure 4 The electronic device 400 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0075] like Figure 4 As shown, the electronic device 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM). The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output interface 405 (I / O interface) is also connected to the bus 404.

[0076] The following components are connected to the input / output interface 405: an input section 606 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a local area network card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0077] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of this application.

[0078] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0079] In an exemplary embodiment of this application, a computer-readable storage medium is also provided, on which computer-readable instructions are stored, which, when executed by a computer's processor, cause the computer to perform the methods described in the above method embodiments.

[0080] According to one embodiment of this application, a program product for implementing the methods in the above-described method embodiments is also provided. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of this invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0081] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0082] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0083] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0084] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0085] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0086] Furthermore, although the steps of the method in this application are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0087] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0088] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention filed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not claimed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the appended claims.

Claims

1. An FFRCT computing device based on STL mesh and SPH simulation, characterized in that, include: The data acquisition module is used to read the STL file of the coronary artery wall and correct the inlet and outlet mesh of the coronary artery. The target filtering module is used to generate a 3D point cloud array containing an STL mesh, and then calculate the directed distance field from the point cloud to the STL mesh. Based on the directed distance, blood particles and blood vessel particles can be filtered out from the point cloud. The calculation module is used to calculate the centerline of blood vessels; The simulation module is used to numerically simulate blood flow processes using the SPH method under conditions of inlet and outlet pressures and no slippage boundary of the blood vessel wall; The output module is used to sample along the centerline of the blood vessel and obtain the FFR value through post-processing simulation results.

2. The FFRCT computing device based on STL mesh and SPH simulation according to claim 1, characterized in that, The data acquisition module specifically includes: using the 3D visualization software Paraview to read the STL file of the coronary artery wall, cutting off each mesh near the inlet or outlet with a plane and enclosing it within a sphere, then locating the mesh vertices in the sphere through code, projecting them onto the corresponding plane, and marking the generated new triangular units as inlet and outlet units.

3. The FFRCT computing device based on STL mesh and SPH simulation according to claim 1, characterized in that, The target screening module specifically includes: generating a three-dimensional point cloud array containing an STL mesh, then calculating the directed distance field from the point cloud to the STL mesh, and filtering out blood particles and blood vessel particles from the point cloud based on the directed distance. The SPH particle relaxation algorithm is used on the blood vessel particles to fine-tune them to fit the position of the blood vessel wall.

4. The FFRCT computing device based on STL mesh and SPH simulation according to claim 1, characterized in that, The calculation module specifically includes: establishing a binary three-dimensional data volume for all generated blood particles according to their position coordinates, marking the voxels corresponding to the blood particles as 1 and the remaining voxels as 0, obtaining the blood skeleton voxels located on the center line through the skeleton extraction algorithm, and then converting the skeleton voxel positions into actual coordinates, that is, obtaining the blood vessel center line.

5. The FFRCT computing device based on STL mesh and SPH simulation according to claim 1, characterized in that, The simulation module specifically includes: setting the blood pressure at the inlet and outlet, as well as the blood density and viscosity, according to medical standards; simulating blood flow over a given time using the SPH method under conditions of no slippage at the vessel wall boundary; and obtaining the time-varying pressure and velocity of blood particles. Excluding the initial phase, the pressure of the blood particles is output according to a given time step.

6. The FFRCT computing device based on STL mesh and SPH simulation according to claim 1, characterized in that, The output module specifically includes: sampling along the centerline of the blood vessel and obtaining the FFR value through post-processing simulation results, including: sampling the calculation points of the FFR value along the centerline of the blood vessel at a given step size, calculating the FFR value at the sampling point using the SPH smoothing function based on the pressure of blood particles, and performing time averaging.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method of claim 1.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

9. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method of claim 1.

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