Hemodynamics evaluation method and equipment for spontaneous portal shunt, medium and program product

Abstract

The invention provides a hemodynamic evaluation method and equipment for spontaneous portal shunt, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring tested image data; predicting different embolism degrees according to the tested image data; and inputting the tested image data and different embolism degrees into the safe embolism prediction model constructed in the invention to obtain a change relationship between different embolism degrees and wedging pressure values, and determining an SPSS safe embolism value. An accurate, simple and non-invasive method is provided for guiding safe embolism of SPSS, a theoretical basis is provided for the method, it is guaranteed that SPSS relapse cannot be caused after an operation, wedging pressure can be made to be within the physiological range of the human body, and clinical judgment of the optimal embolism degree is assisted.

Description

Technical Field

[0001] This invention relates to the field of intelligent healthcare, and more specifically, to a method, device, medium, and procedure for hemodynamic assessment of spontaneous portosystemic shunt. Background Technology

[0002] Spontaneous portosystemic shunt (SPSS) refers to an abnormal vascular connection that spontaneously forms between the portal venous system and the systemic circulation, allowing blood to bypass the liver and enter the systemic circulation directly. SPSS is one of the compensatory mechanisms for portal hypertension in chronic liver disease and / or cirrhosis. As a type of portosystemic shunt, SPSS is diverse and complex in its course, and its treatment remains a challenge in clinical practice. Current clinical techniques typically employ endovascular embolization, but there are still many uncertainties and risks regarding related complications. For congenital portosystemic shunts, staged embolization can also be used to reduce complications such as hypertension in various vascular branches caused by excessive embolization of SPSS.

[0003] However, there is a lack of theoretical basis for the degree and number of embolizations during the operation. For example, some patients experienced SPSS recurrence due to insufficient embolization, while others experienced significant increases in the pressure of various vascular branches after the operation due to excessive embolization, resulting in ruptured varicose veins, bleeding, and ascites. In some cases, transjugular intrahepatic portosystemic shunt was even required to reduce portal vein pressure, which brought great trouble to clinical treatment. Summary of the Invention

[0004] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention provides a method, device, medium, and procedure for hemodynamic assessment of spontaneous portosystemic shunt (SPSS). The method of this invention provides an accurate, simple, and non-invasive approach to guide safe embolization of SPSS and provides a theoretical basis for it, ensuring that postoperative SPSS recurrence is not caused and that the wedging pressure remains within the physiological range of the human body. This assists in clinical judgment of the optimal degree of embolization and reduces complications caused by increased pressure in various vascular branches.

[0005] The first aspect of this application discloses a method for constructing a predictive model for spontaneous portal shunting safety embolism, the method comprising:

[0006] S101, Obtain vascular anatomy models of training set samples with spontaneous portosystemic shunting; the vascular anatomy models include the location and degree of embolism of the corresponding embolized vessel segments;

[0007] S102, Obtain physiological parameters of the embolized vascular segment, including physiological flow velocity and pressure;

[0008] S103, simulate the insertion of embolization material into the embolized vessel segment, and calculate the hemodynamic simulation results after the insertion of embolization material based on physiological parameters and clinical characteristics of the embolized vessel segment;

[0009] S104, Calculate the hemodynamic parameters of each discrete point in the vascular anatomy model based on the hemodynamic simulation results;

[0010] S105 calculates the wedge pressure values ​​for different degrees of embolism based on hemodynamic parameters, and constructs a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure values ​​based on the changes in wedge pressure values.

[0011] In some embodiments, hemodynamic parameters include any one or more of the following: pressure, velocity, flow rate, wall shear stress WSS, time-averaged wall shear stress TAWSS, oscillating shear index OSI, relative residence time RRT, and endothelial cell activation potential ECAP.

[0012] Optionally, after S104, the method further includes calculating the average value of hemodynamic parameters at each discrete point; calculating the wedge pressure value obtained from different virtual surgical procedures based on the average value; and constructing a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure value based on the change in wedge pressure value.

[0013] In some embodiments, hemodynamic simulation results include blood flow velocity and pressure;

[0014] Optionally, when calculating the hemodynamic simulation results, the porosity, permeability, fluid density, and dynamic viscosity of the embolic material should also be considered;

[0015] Optionally, the embolization material may be a porous media model material.

[0016] In some embodiments, between S102 and S103, the method further includes: solving the momentum conservation equation and the mass conservation equation based on the boundary conditions set according to physiological parameters, performing hemodynamic simulation on the vascular anatomy model, and ensuring that the anatomical model is close to the real blood vessel before the embolization material is placed.

[0017] In some embodiments, the vascular anatomy model is obtained by: acquiring preoperative and postoperative image data of the training set samples; extracting the main trunk and branches of the blood vessels to be simulated for hemodynamics based on the image data, and identifying the embolized segment; geometrically segmenting the location of the embolized segment separately, and retaining the initial morphology to obtain the vascular anatomy model;

[0018] Optionally, the degree of embolization is either complete embolization or partial embolization, with the degree of partial embolization being the percentage of the volume of the embolized material relative to the volume of the embolized vessel segment;

[0019] Optionally, the vascular anatomy model may also include information on the location and shape of the embolic material;

[0020] Optionally, the embolization material includes any one or more of the following: coils, vascular plugs, and occluders.

[0021] The second aspect of this application discloses a hemodynamic assessment method for spontaneous portosystemic shunting, the method comprising:

[0022] S201, Acquire the subject's image data;

[0023] S202, predicting different degrees of embolism based on the subject's imaging data;

[0024] S203, input the image data of the test subject and different degrees of embolism into the safe embolism prediction model constructed by any of the methods in the first aspect of this application, obtain the relationship between different degrees of embolism and the wedge pressure value, and determine the SPSS safe embolism value.

[0025] In some embodiments, the method further includes developing different virtual surgical plans based on different degrees of embolism, inputting the subject's imaging data and different virtual surgical plans into the safe embolism prediction model constructed by the method of the first aspect of this application, obtaining the relationship between different virtual surgical plans and the wedging pressure value, and determining the SPSS safe embolism value.

[0026] Optionally, the method also includes: using SPSS safe embolization values ​​as a reference to guide clinical SPSS treatment.

[0027] A third aspect of this application discloses a computer device, comprising: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the above-described method.

[0028] The fourth aspect of this application discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0029] The fifth aspect of this application discloses a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0030] This application has the following beneficial effects: 1. This application innovatively discloses a method for constructing a safe embolism prediction model for spontaneous portosystemic shunt. It constructs a vascular anatomy model using training samples from patients with SPSS, determines different virtual surgical procedures based on the degree of embolism, and then simulates the insertion of a porous media embolic material that closely resembles a real embolic object into the embolic vessel segment of the anatomical model, ensuring the realism of subsequent parameter calculations. Then, it calculates the hemodynamic simulation results after embolic material insertion and calculates the hemodynamic parameters at discrete points in the embolic material region. Based on the hemodynamic parameters, it calculates the change in wedging pressure for different degrees of embolism (corresponding to different virtual surgical procedures), constructing a safe embolism prediction model that can realistically reflect the changes in wedging pressure under different virtual surgical procedures.

[0031] Preferably, in order to ensure that the anatomical model before the embolization material is inserted is close to the real blood vessel, this model also sets boundary conditions based on physiological parameters to solve the momentum conservation equation and the mass conservation equation, and performs hemodynamic simulation on the vascular anatomical model.

[0032] 2. Based on the aforementioned safe embolization prediction model, this application also proposes a method to determine the safe SPSS embolization value through imaging data when dealing with different patients. This addresses the current technical situation where some patients experience SPSS recurrence due to insufficient embolization, while others experience significant increases in pressure in various vascular branches postoperatively due to excessive embolization, leading to varicose vein rupture, bleeding, and ascites, and even requiring transjugular intrahepatic portosystemic shunt to reduce portal vein pressure. This provides a theoretical basis for guiding safe embolization of SPSS, ensuring that postoperative SPSS recurrence is not caused and that the wedging pressure remains within the physiological range of the human body, thus assisting in clinical judgment of the optimal degree of embolization. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic flowchart of the method for constructing a predictive model for spontaneous door shunting safety embolism provided in the first aspect of the present invention;

[0035] Figure 2 This is a schematic diagram of the hemodynamic assessment method for spontaneous portosystemic shunting provided in the second aspect of the present invention;

[0036] Figure 3 This is a schematic diagram of the system for constructing a predictive model for spontaneous door shunting safety embolism provided in an embodiment of the present invention;

[0037] Figure 4 This is a schematic diagram of a hemodynamic assessment system for spontaneous portosystemic shunting provided in an embodiment of the present invention;

[0038] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of the architecture of an exemplary computing device provided in an embodiment of the present invention;

[0040] Figure 7 This is a schematic diagram of the storage medium provided in an embodiment of the present invention. Detailed Implementation

[0041] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0042] In some of the processes described in the specification, claims, and accompanying drawings of this invention, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Figure 1 This is a schematic flowchart of a method for constructing a predictive model for spontaneous portal shunting safety embolism, provided by an embodiment of the present invention. Specifically, the method includes the following steps:

[0045] S101, Obtain vascular anatomy models of training set samples with spontaneous portosystemic shunting; the vascular anatomy models include the location and degree of embolism of the corresponding embolized vessel segments;

[0046] In some embodiments, the vascular anatomy model is obtained by: acquiring preoperative and postoperative image data of the training set samples; extracting the main trunk and branches of the blood vessels to be simulated for hemodynamics based on the image data, and identifying the embolized segment; geometrically segmenting the location of the embolized segment separately, and retaining the initial morphology to obtain the vascular anatomy model;

[0047] Optionally, the degree of embolization is either complete embolization or partial embolization, with the degree of partial embolization being the percentage of the volume of the embolized material relative to the volume of the embolized vessel segment;

[0048] Optionally, the vascular anatomy model may also include information on the location and shape of the embolic material;

[0049] Optionally, the embolization material includes any one or more of the following: coils, vascular plugs, and occluders.

[0050] S102, Obtain physiological parameters of the embolized vascular segment, including physiological flow velocity and pressure;

[0051] In some embodiments, between S102 and S103, the method further includes: solving the momentum conservation equation and the mass conservation equation based on the boundary conditions set according to physiological parameters, performing hemodynamic simulation on the vascular anatomy model, and ensuring that the anatomical model is close to the real blood vessel before the embolization material is placed.

[0052] S103, simulate the insertion of embolization material into the embolized vessel segment, and calculate the hemodynamic simulation results after the insertion of embolization material based on physiological parameters and clinical characteristics of the embolized vessel segment;

[0053] In some embodiments, hemodynamic simulation results include blood flow velocity and pressure;

[0054] Optionally, when calculating the hemodynamic simulation results, the porosity, permeability, fluid density, and dynamic viscosity of the embolic material should also be considered;

[0055] Optionally, the embolization material may be a porous media model material.

[0056] S104, Calculate the hemodynamic parameters of each discrete point in the vascular anatomy model based on the hemodynamic simulation results;

[0057] In some embodiments, hemodynamic parameters include any one or more of the following: pressure, velocity, flow rate, wall shear stress WSS, time-averaged wall shear stress TAWSS, oscillating shear index OSI, relative residence time RRT, and endothelial cell activation potential ECAP.

[0058] Optionally, after S104, the method further includes calculating the average value of hemodynamic parameters at each discrete point; calculating the wedge pressure value obtained from different virtual surgical procedures based on the average value; and constructing a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure value based on the change in wedge pressure value.

[0059] S105 calculates the wedge pressure values ​​for different degrees of embolism based on hemodynamic parameters, and constructs a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure values ​​based on the changes in wedge pressure values.

[0060] The second aspect of this application discloses a hemodynamic assessment method for spontaneous portosystemic shunting, such as... Figure 2 As shown, the method includes:

[0061] S201, Acquire the subject's image data;

[0062] S202, predicting different degrees of embolism based on the subject's imaging data;

[0063] S203, input the image data of the test subject and different degrees of embolism into the safe embolism prediction model constructed by any of the methods in the first aspect of this application, obtain the relationship between different degrees of embolism and the wedge pressure value, and determine the SPSS safe embolism value.

[0064] In some embodiments, the method further includes developing different virtual surgical plans based on different degrees of embolism, inputting the subject's imaging data and different virtual surgical plans into the safe embolism prediction model constructed by the method of the first aspect of this application, obtaining the relationship between different virtual surgical plans and the wedging pressure value, and determining the SPSS safe embolism value.

[0065] Optionally, the method also includes: using SPSS safe embolization values ​​as a reference to guide clinical SPSS treatment.

[0066] In some embodiments, the SPSS safety embolism value can be a specific threshold or a range, and the specific form is not specifically limited in this embodiment.

[0067] Figure 5 This is a schematic diagram of a computer device provided in an embodiment of the present invention, such as... Figure 5 As shown, device 2000 may include: one or more processors 2010 and one or more memories 2020; wherein the memories store computer-readable code that, when run by one or more processors, can execute the methods described above.

[0068] The processor in this embodiment can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, operations, and logic block diagrams disclosed in this embodiment. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 or ARM architecture.

[0069] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0070] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 6 The architecture of the computing device 3000 shown is used for implementation. For example... Figure 6 As shown, the computing device 3000 may include a bus 3010, one or more CPUs 3020, a read-only memory (ROM) 3030, a random access memory (RAM) 3040, a communication port 3050 connected to a network, an input / output component 3060, a hard disk 3070, etc. The storage devices in the computing device 3000, such as the ROM 3030 or the hard disk 3070, may store various data or files used for processing and / or communication of the methods provided in this disclosure, as well as program instructions executed by the CPU. The computing device 3000 may also include a user interface 3080. Of course, Figure 6 The architecture shown is merely exemplary and can be omitted as needed when implementing different devices. Figure 6 One or more components in the computing device shown.

[0071] This invention also includes a computer-readable storage medium, such as... Figure 7 The diagram illustrates a storage medium 4000 provided in an embodiment of the present invention. The computer storage medium 4020 stores computer-readable instructions 4010. When the computer-readable instructions 4010 are executed by a processor, the method described above according to embodiments of the present disclosure can be performed. The computer-readable storage medium in the embodiments of the present disclosure may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM), and Direct Memory Bus Random Access Memory (DR RAM). It should be noted that the memory used in the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0072] This disclosure also provides a computer program product or system, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0073] In some embodiments, this embodiment also discloses a system for constructing a predictive model for spontaneous door shunting safety embolism, such as... Figure 3 As shown, the system includes:

[0074] The anatomical model acquisition module 301 is used or configured to acquire vascular anatomy models of training set samples with spontaneous portosystemic shunting; the vascular anatomy model includes the location and degree of embolism of the corresponding embolized vessel segment;

[0075] Physiological parameter acquisition module 302 is used or configured to acquire physiological parameters of the embolized vascular segment, including physiological flow velocity and pressure.

[0076] The hemodynamic simulation result calculation module 303 is used or configured to simulate the placement of embolization material in the embolized vessel segment and calculate the hemodynamic simulation results after the placement of the embolization material based on physiological parameters and clinical characteristics of the embolized vessel segment.

[0077] The hemodynamic parameter calculation module 304 is used or configured to calculate the hemodynamic parameters of each discrete point of the vascular anatomy model based on the results of hemodynamic simulation.

[0078] The safety embolism prediction model training module 305 is used or configured to calculate the wedge pressure values ​​for different degrees of embolism based on hemodynamic parameters, and to construct a safety embolism prediction model that reflects the degree of embolism and the change in wedge pressure values ​​based on the changes in wedge pressure values.

[0079] In some embodiments, between the physiological parameter acquisition module and the hemodynamic simulation result calculation module, the system further includes an anatomical model optimization simulation module, which is used or configured to solve the momentum conservation equation and the mass conservation equation based on the boundary conditions set according to the physiological parameters, and to perform hemodynamic simulation on the vascular anatomical model to ensure that the anatomical model is close to the real blood vessel before the embolization material is placed.

[0080] In some embodiments, this embodiment also discloses a hemodynamic assessment system for spontaneous portosystemic shunting, such as... Figure 4 As shown, the system includes:

[0081] Image data acquisition module 401 acquires image data of the subject;

[0082] Embolism degree prediction module 402 predicts different degrees of embolism based on the subject's imaging data;

[0083] The safety embolism value prediction module 403 inputs the subject's image data and different degrees of embolism into the safety embolism prediction model constructed by any of the methods in the first aspect of this application, obtains the relationship between different degrees of embolism and the wedge pressure value, and determines the SPSS safety embolism value. Specific implementation examples:

[0085] S1. Collect preoperative and postoperative medical imaging data of patients with spontaneous portosystemic shunt to obtain characteristics such as the location and degree of embolism of the embolized vessel segment after surgery.

[0086] S2. Mark the location and geometry of the embolized vessel segment and establish a 3D vascular anatomy model based on medical imaging data;

[0087] S3. Collect personalized physiological parameters based on clinical information. Personalized physiological parameters include physiological flow rate data and pressure data.

[0088] S4. Develop a virtual surgical plan based on the degree of embolism, and perform hemodynamic simulation and calculate mechanical parameters on the vascular anatomy model based on personalized physiological parameters;

[0089] S5. Based on mechanical parameters, study the influence of the degree of embolism on the increase of wedge pressure and determine the SPSS safe embolism threshold.

[0090] Optionally, in S1, preoperative and postoperative medical imaging data of patients with spontaneous portosystemic shunt are collected to obtain characteristics such as the location and degree of embolism of the embolic segment after surgery, including:

[0091] Personalized medical imaging data of patients before and after surgery is obtained by using a combination of one or more medical imaging modalities.

[0092] Based on medical imaging data, the location and degree of embolization of the embolized vessel segment after surgery are obtained.

[0093] Optionally, methods for acquiring medical imaging data include:

[0094] It employs any one or more medical imaging modalities from computed tomography, magnetic resonance imaging, angiography, and ultrasound imaging.

[0095] Optionally, characteristics such as the location and degree of embolization of the embolized vessel segment after surgery include:

[0096] Information on the degree of embolism and the location and shape of the embolic material. The degree of embolism is expressed as the percentage of the volume of the embolic material relative to the volume of the embolic vessel segment. According to the degree of embolism, the embolism type can be divided into complete embolism and partial embolism. The location and shape information of the embolic material is based on the patient's medical imaging data after SPSS embolization.

[0097] Optionally, in S2, the location and geometry of the embolic vessel segment are marked, and a 3D vascular anatomy model is established based on medical imaging data, including:

[0098] Based on medical imaging data, a thresholding technique is used to obtain vascular masks from the medical imaging data. A vascular mask is a segmentation technique based on medical images (such as CT or MRI) used to extract vascular structures from image data. It uses algorithms to identify and label vascular regions, generating a binary image (i.e., a mask) where vascular regions are marked in white against a black background. This technique can accurately separate blood vessels from other tissues, providing a foundation for subsequent analysis and modeling.

[0099] Based on the vascular mask, generate the vascular path to be segmented;

[0100] The blood vessels are initially segmented according to the vascular pathway, preserving the main trunks and branches of the blood vessels to be used for hemodynamic simulation;

[0101] The location of the embolized vascular segment is geometrically segmented separately, preserving its initial shape, to obtain the 3D reconstructed geometry of the blood vessel;

[0102] The 3D reconstructed geometry is denoised to obtain a smooth vascular model, and the smooth vascular model is then meshed to obtain a 3D vascular anatomy model.

[0103] Alternatively, the embolization material for the embolized vascular segment can be a solid embolizing agent such as a coil, a vascular plug, or an occluder.

[0104] Optionally, in S3, personalized physiological parameters are collected based on clinical information. These personalized physiological parameters include physiological flow rate data and pressure data, including:

[0105] Physiological flow velocity data were obtained by using ultrasound velocimetry and phase-contrast magnetic resonance imaging data; a pressure guidewire was inserted into the blood vessel and connected to a guidewire pressure measuring device, and the pressure status of each blood vessel branch was obtained by reading data from real-time monitoring instruments.

[0106] Optionally, in S4, a virtual surgical plan is developed based on the degree of embolism. Based on personalized physiological parameters, hemodynamic simulations are performed on the vascular anatomy model, and mechanical parameters are calculated, including:

[0107] Based on personalized physiological parameter boundary conditions, the blood flow velocity and pressure are obtained by solving the continuity equation (based on the law of conservation of mass, describing the change of mass of fluid during flow) and the Navier-Stokes equation (describing the conservation of momentum of viscous fluid, including inertial force, pressure gradient, viscous force and external force) on the 3D vascular anatomy model.

[0108] A porous media model is used to approximate embolic materials. Based on personalized physiological parameters and clinical characteristics of embolic vessel segments in existing clinical cases, the momentum conservation equation and mass conservation equation are solved to perform hemodynamic simulation on a 3D vascular anatomy model. Currently, there is no fully automated technology, but the hemodynamic simulation process is relatively well-defined, and this field is generally directly described as hemodynamic simulation. More specifically, a porous media model is used to approximate embolic materials. Based on porosity, permeability, fluid density, and dynamic viscosity, and according to the governing equations, including the mass conservation equation and momentum conservation equation, the blood flow velocity and pressure in the porous media region are calculated.

[0109] Hemodynamic parameters at discrete points in a 3D vascular anatomy model are calculated based on hemodynamic simulation results. These calculated mechanical parameters are then applied to the corresponding locations at each discrete point in the 3D vascular anatomy model. The mechanical parameters include: pressure, velocity, flow rate, wall shear stress (WSS), time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), and endothelial cell activation potential (ECAP). These mechanical parameters, as independent parameters, are used to assess the stress on the vessel wall. They, along with flow rate and pressure, are used to evaluate the appropriateness of the chosen embolism degree. These parameters are related to complications. Generally, higher pressure corresponds to greater embolism. The appropriateness of the embolism degree is primarily determined using these hemodynamic parameters.

[0110] The method for calculating dynamic parameters directly uses existing calculation formulas, as follows:

[0111]

[0112]

[0113]

[0114]

[0115] The mechanical parameters of the vessel wall in the 3D vascular anatomy model are visualized and their average values ​​are calculated. In this embodiment, the average value is calculated as the average value of the mechanical parameters of the discrete points that make up the region of interest.

[0116] Optionally, in S5, the influence of the degree of embolism on the increase in wedging pressure is studied based on mechanical parameters to determine the SPSS safe embolism threshold, including:

[0117] The changes in wedge pressure values ​​obtained from preoperative simulations of different virtual surgical procedures were statistically analyzed. The critical embolization level of SPSS when the postoperative wedge pressure increased by 50% or exceeded 30 mmHg was screened out, and this was used as a reference to guide clinical SPSS endovascular embolization. For example, if the critical embolization level is 50%, then embolization between 0-50% is considered safe. 0-50% is a safe embolization range, and as long as it does not exceed this threshold, it is acceptable. This is thus used as a reference to guide clinical SPSS treatment.

[0118] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0119] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0120] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] The exemplary embodiments of this disclosure described in detail above are merely illustrative and not restrictive. Those skilled in the art will understand that various modifications and combinations can be made to these embodiments or their features without departing from the principles and spirit of this disclosure, and such modifications should fall within the scope of this disclosure.

Claims

1. A method for constructing a predictive model for spontaneous valve shunting safety embolism, characterized in that, The method includes: S101, Obtain vascular anatomy models of training set samples with spontaneous portosystemic shunting; the vascular anatomy models include the location and degree of embolism of the corresponding embolized vessel segments; S102, Obtain physiological parameters of the embolized vascular segment, including physiological flow velocity and pressure; S103, embolization material is simulated to be inserted into the embolized vessel segment, and the hemodynamic simulation results after the embolization material is inserted are calculated based on physiological parameters and clinical characteristics of the embolized vessel segment; S104, Calculate the hemodynamic parameters of each discrete point in the vascular anatomy model based on the hemodynamic simulation results; S105 calculates the wedge pressure values ​​for different degrees of embolism based on hemodynamic parameters, and constructs a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure values ​​based on the changes in wedge pressure values.

2. The method for constructing a predictive model for spontaneous gate shunting safety embolism according to claim 1, characterized in that, The hemodynamic parameters include any one or more of the following: pressure, velocity, flow rate, wall shear stress, time-averaged wall shear stress, oscillatory shear index, relative residence time, and endothelial cell activation potential. Optionally, after S104, the method further includes calculating the average value of the hemodynamic parameters of each discrete point; calculating the wedge pressure value obtained from different virtual surgical procedures based on the average value; and constructing a safe embolism prediction model that reflects the degree of embolism and the change in wedge pressure value based on the change in wedge pressure value.

3. The method for constructing a predictive model for spontaneous gate shunting safety embolism according to claim 1, characterized in that, The hemodynamic simulation results include blood flow velocity and pressure; Optionally, when calculating the hemodynamic simulation results, the porosity, permeability, fluid density, and dynamic viscosity of the embolic material also need to be considered; Optionally, the embolization material may be a porous media model material.

4. The method for constructing a predictive model for spontaneous gate shunting safety embolism according to claim 1, characterized in that, Between S102 and S103, the method further includes: solving the momentum conservation equation and the mass conservation equation based on the boundary conditions set according to physiological parameters, performing hemodynamic simulation on the vascular anatomy model, and ensuring that the anatomical model is close to the real blood vessel before the embolization material is placed.

5. The method for constructing a predictive model for spontaneous gate shunting safety embolism according to claim 1, characterized in that, The vascular anatomy model was obtained by the following method: acquiring preoperative and postoperative image data of the training set samples; extracting the main trunk and branches of the blood vessels to be simulated for hemodynamics based on the image data, and determining the embolized segment; geometrically segmenting the location of the embolized segment separately, and retaining the initial morphology to obtain the vascular anatomy model. Optionally, the degree of embolization is either complete embolization or partial embolization, and the degree of partial embolization is the percentage of the volume of the embolization material relative to the volume of the embolized vessel segment; Optionally, the vascular anatomy model may also include information on the location and shape of the embolic material; Optionally, the embolization material includes any one or more of the following: spring coils, vascular plugs, and occluders.

6. A hemodynamic assessment method for spontaneous portosystemic shunting, characterized in that, The method includes: S201, Acquire the subject's image data; S202, predict different degrees of embolism based on the imaging data of the test subjects; S203, input the image data of the test subject and different degrees of embolism into the safe embolism prediction model constructed by the method of any one of claims 1-5, obtain the relationship between different degrees of embolism and the wedge pressure value, and determine the SPSS safe embolism value.

7. The hemodynamic assessment method for spontaneous portosystemic shunting according to claim 6, characterized in that, The method further includes developing different virtual surgical plans based on different degrees of embolism, inputting the subject's imaging data and different virtual surgical plans into the safe embolism prediction model constructed by the method of any one of claims 1-5, obtaining the relationship between different virtual surgical plans and wedging pressure values, and determining the SPSS safe embolism value; Optionally, the method further includes: using SPSS safe embolization values ​​as a reference to guide clinical SPSS treatment.

8. A computer device, characterized in that, The device includes: a memory and a processor; the memory is used to store a computer program; the processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1-7.

Images