Liver blood vessel one-dimensional blood flow simulation method considering energy loss at blood vessel bifurcation

Abstract

The invention discloses a hepatic blood vessel one-dimensional blood flow simulation method considering energy loss at a blood vessel bifurcation, and relates to the technical field of hemodynamic simulation, and the method comprises the following steps: 1, based on real human body hepatic blood vessel data, reconstructing a blood vessel model through a medical image processing technology, and extracting a blood vessel center line; acquiring the length of a blood vessel section, the cross sectional area of a starting end and a bifurcation angle of a blood vessel bifurcation, and constructing a one-dimensional geometric model of the blood vessel; 2, according to the one-dimensional geometric model, a one-dimensional blood vessel network model capable of being read by Nektar + + software is constructed through Visual Studio Code and other code editing software, simulation parameters are set, and a simulation parameter setting file is generated; and 3, implanting an algorithm for calculating the loss coefficient of the blood vessel bifurcation into Nektar + + software to process a functional module for calculating the blood vessel bifurcation.

Description

Technical Field

[0001] The present invention relates to the technical field of hemodynamic simulation, and in particular to a one-dimensional blood flow simulation method for hepatic blood vessels taking into account energy loss at a blood vessel bifurcation. Background Art

[0002] In the medical field, hemodynamic research is of great significance to the diagnosis and treatment of diseases, and blood flow simulation technology has become an important research tool. Although three-dimensional blood flow simulation can accurately present the blood flow morphology in blood vessels, it faces the problem of grid division, huge consumption of computing resources, long calculation time, and the results depend on the accuracy of the grid, which affects reliability. One-dimensional blood flow simulation has low computing resource requirements, fast calculation speed and can provide simulation results with a certain degree of accuracy. However, existing one-dimensional blood flow simulation often ignores energy loss when dealing with blood flow at vascular bifurcations. There are a large number of branching vessels with large bifurcation angles in the hepatic vascular network. The increase in bifurcation angle and blood flow rate will significantly increase the energy loss at the bifurcation and accumulate in the complex network. The existing one-dimensional blood flow simulation technology cannot accurately reflect the true hemodynamic characteristics when applied to hepatic blood vessels. The present invention is a one-dimensional blood flow simulation method for hepatic blood vessels that takes into account the energy loss at the vascular bifurcation. It aims to solve the problem that the existing technology does not fully consider the energy loss at the bifurcation in the hepatic blood flow simulation, resulting in inaccurate simulation, and provide more accurate and reliable technical support for hepatic vascular related research and disease diagnosis and treatment. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention solves the technical problems by adopting a technical solution: a one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations, comprising the following steps:

[0004] Step 1: Based on real human liver blood vessels or other blood vessel data, medical image processing technology is used to reconstruct the blood vessel model and extract the blood vessel centerline. The blood vessel segment length, starting end cross-sectional area, and bifurcation angle at the bifurcation are obtained to construct a one-dimensional geometric model of the blood vessel.

[0005] Step 2: Based on the one-dimensional geometric model, use code editing software such as Visual Studio Code to construct a one-dimensional vascular network model that can be read by Nektar++ software, set simulation parameters, and generate a simulation parameter setting file;

[0006] Step 3: The algorithm for calculating the loss coefficient at the vascular bifurcation is implanted into the functional module for processing vascular bifurcation calculations in the Nektar++ software. The loss coefficient calculation algorithm includes the following steps:

[0007] Step A1: Based on the vascular cross-sectional area and blood flow velocity parameters calculated by Nektar++, as well as the externally input vascular bifurcation angle parameters, the blood flow ratio and cross-sectional area ratio of parent vessel i and child vessel j are calculated. The blood flow ratio calculation formula is:

[0008]

[0009] The cross-sectional area ratio is calculated as:

[0010]

[0011] Among them, q i is the flow rate of parent vessel i, q j is the flow rate of sub-vessel j, A i is the cross-sectional area of parent vessel i, A j is the cross-sectional area of daughter vessel j;

[0012] Step A2: Based on the above calculated proportional parameters, use the formula:

[0013]

[0014] and,

[0015] Calculate the loss coefficient K between parent vessel i and child vessel j at the bifurcation i,j ,in is the angle between the axis of parent vessel i and the axis of child vessel j;

[0016] Step A3: According to the blood flow rate u of the parent vessel i , blood density ρ and the calculated loss coefficient K i，j , through the formula:

[0017]

[0018] Calculate the energy loss p at the vascular bifurcation loss , and perform numerical stability checks on the calculation results;

[0019] Step A4: Get the p loss By adding the momentum conservation law code part to the vascular bifurcation processing, a one-dimensional solver for liver blood flow that takes into account the energy loss at the vascular bifurcation is obtained. During the implantation process, the compatibility and correctness of the code are ensured to avoid introducing new errors.

[0020] Preferably, in the step of constructing a one-dimensional geometric model of the blood vessel, the medical image data is preprocessed, and the preprocessing operation includes image denoising, using Gaussian filtering, median filtering and wavelet denoising methods, and contrast enhancement using histogram equalization and contrast-limited adaptive histogram equalization algorithms to improve the accuracy of blood vessel model reconstruction.

[0021] Preferably, in the step of embedding the loss coefficient calculation algorithm into the Nektar++ software, the calculated loss coefficient K i，j Perform rationality check, including comparison with preset experience value range and theoretical calculation range. i，j If it is not within the preset reasonable range, the accuracy of the input parameters will be checked retrospectively and recalculated, and the relevant data during the verification process will be recorded for subsequent analysis.

[0022] Preferably, after obtaining a one-dimensional solver for liver blood flow that takes into account energy loss at vascular bifurcations, the solver is used to perform blood flow simulation calculations. The numerical calculation methods used in the calculation process include but are not limited to one or more combinations of the finite difference method, the finite volume method, and the finite element method. The convergence of the numerical calculation is monitored during the calculation process, and the calculation parameters or methods are adjusted when the convergence conditions are not met.

[0023] Preferably, when performing blood flow simulation calculations, the inlet boundary condition is set to a pulsating flow with a time period of T0, wherein T0 is the cardiac cycle duration that conforms to the physiological conditions of the human body and is set to 0.75s, and the outlet boundary condition is a ternary windkessel boundary condition. The DisContinuous Galerkin projection method, the Runge-Kutta time integration method, and the Upwind format are used for simulation calculations. The simulation time is set to T1, which is set to 10S, and the calculation result of T2 seconds is selected as the final result, wherein T2 is a duration greater than 3 seconds and less than 10 seconds, and the usage of computing resources is monitored during the simulation calculation to ensure computing efficiency and stability.

[0024] Preferably, the method further includes the step of post-processing the simulation results, wherein the post-processing includes data visualization processing, using ParaView and VisIt to draw blood flow velocity distribution, pressure distribution, and flow change curves, as well as evaluating the accuracy of the simulation results, and comparing and analyzing the simulation results with the actual measurement data. The comparative analysis method includes root mean square error calculation, correlation coefficient calculation and statistical analysis means.

[0025] Preferably, the method also includes the step of optimizing the entire simulation process, and the optimization directions include algorithm efficiency optimization and computing resource allocation optimization, wherein the algorithm efficiency optimization is achieved by improving the calculation steps and reducing redundant calculation methods, and the computing resource allocation optimization dynamically adjusts the allocation of memory and CPU resources according to the requirements of the computing tasks.

[0026] The beneficial effects of the present invention are as follows:

[0027] 1. Based on the characteristics of the hepatic vascular network, this invention calculates the cross-sectional area, flow velocity, bifurcation angle, and other information of the parent and daughter vessels at a vascular bifurcation in detail, deriving a loss coefficient in real time and accurately embedding it into a momentum conservation model at the bifurcation. This innovative approach overcomes the shortcomings of existing one-dimensional blood flow simulation technology in addressing energy loss at hepatic vascular bifurcations, enabling the simulation model to accurately reflect the energy loss of blood flow within the hepatic vessels at bifurcations, thereby significantly improving the accuracy of the simulation of hepatic vascular hemodynamics.

[0028] 2. With more accurate hemodynamic simulations, researchers can delve deeper into the blood flow patterns of hepatic vessels under normal physiological conditions and the mechanisms of hemodynamic changes in disease. This will help reveal the intrinsic connection between the pathogenesis of liver diseases (such as cirrhosis and liver cancer) and hemodynamics, providing an important theoretical basis for early diagnosis and precise treatment of diseases.

[0029] 3. This invention takes into account energy loss at vascular bifurcations without significantly increasing the computational burden. Compared to three-dimensional blood flow simulation technologies, this method avoids reliance on large-scale computing resources, effectively reducing R&D costs and hardware requirements, enabling rapid hepatic vascular blood flow simulation studies on standard computing equipment. Blood flow simulation calculations can be completed in a relatively short time, providing clinicians with timely blood flow simulation results. In clinical scenarios such as preoperative planning and condition assessment, physicians can quickly obtain hepatic vascular blood flow information to assist in determining surgical feasibility, assessing surgical risks, and predicting treatment outcomes, thereby improving the efficiency and accuracy of clinical decision-making. Specifically designed to address the complex structure of the hepatic vascular network and the numerous branches with large bifurcations, this method effectively addresses the unique challenges of hepatic vascular blood flow simulation. In liver disease research and clinical applications, it provides a reliable technical means for the diagnosis, treatment, and prognosis of hepatic vascular-related diseases. The principles and algorithms employed are universal and can be applied not only to the hepatic vascular network but also, after appropriate adjustment and optimization, to one-dimensional blood flow simulation in other parts of the human body. This provides a unified technical framework for comprehensive research on the hemodynamics of the human vascular system and helps promote the development of the entire field of hemodynamics. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1This is a model diagram of the loss coefficient at the blood vessel bifurcation of the present invention;

[0031] Figure 2 This is a comparison chart of the pressure results of the present invention;

[0032] Figure 3 This is a comparison chart of flow rate results of the present invention;

[0033] Figure 4 It is a comparison chart of the visualization results of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for purposes of illustration and description and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments are chosen and described to better illustrate the principles of the invention and its practical application, and to enable those skilled in the art to understand the invention and design various embodiments with various modifications suitable for specific applications.

[0035] Example:

[0036] A one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations comprises the following steps:

[0037] Step 1: Based on real human liver blood vessels or other blood vessel data, medical image processing technology is used to reconstruct the blood vessel model and extract the blood vessel centerline. The blood vessel segment length, starting end cross-sectional area, and bifurcation angle at the bifurcation are obtained to construct a one-dimensional geometric model of the blood vessel.

[0038] Step 2: Based on the one-dimensional geometric model, use code editing software such as Visual Studio Code to construct a one-dimensional vascular network model that can be read by Nektar++ software, set simulation parameters, and generate a simulation parameter setting file;

[0039] Step 3: The algorithm for calculating the loss coefficient at the vascular bifurcation is implanted into the functional module for processing vascular bifurcation calculations in the Nektar++ software. The loss coefficient calculation algorithm includes the following steps:

[0040] Step A1: Based on the vascular cross-sectional area and blood flow velocity parameters calculated by Nektar++, as well as the externally input vascular bifurcation angle parameters, the blood flow ratio and cross-sectional area ratio of parent vessel i and child vessel j are calculated. The blood flow ratio calculation formula is:

[0041]

[0042] The cross-sectional area ratio is calculated as:

[0043]

[0044] Among them, q i is the flow rate of parent vessel i, q j is the flow rate of sub-vessel j, A i is the cross-sectional area of parent vessel i, A j is the cross-sectional area of daughter vessel j;

[0045] Step A2: Based on the above calculated proportional parameters, use the formula:

[0046]

[0047] and,

[0048] Calculate the loss coefficient K between parent vessel i and child vessel j at the bifurcation i,j ,in is the angle between the axis of parent vessel i and the axis of child vessel j;

[0049] Step A3: According to the blood flow rate u of the parent vessel i i , blood density ρ and the calculated loss coefficient K i，j , through the formula:

[0050]

[0051] Calculate the energy loss p at the vascular bifurcation loss , and perform numerical stability checks on the calculation results;

[0052] Step A4: Get the p loss By adding the momentum conservation law code part to the vascular bifurcation processing, a one-dimensional solver for liver blood flow that takes into account the energy loss at the vascular bifurcation is obtained. During the implantation process, the compatibility and correctness of the code are ensured to avoid introducing new errors.

[0053] In the step of constructing a one-dimensional geometric model of the blood vessels, the medical image data is preprocessed. The preprocessing operation includes image denoising using Gaussian filtering, median filtering, and wavelet denoising methods, and contrast enhancement using histogram equalization and contrast-limited adaptive histogram equalization algorithms to improve the accuracy of vascular model reconstruction.

[0054] In the step of embedding the loss coefficient calculation algorithm into Nektar++ software, the calculated loss coefficient K i，j Perform rationality check, including comparison with preset experience value range and theoretical calculation range. i，jIf it is not within the preset reasonable range, the accuracy of the input parameters will be checked retrospectively and recalculated, and the relevant data during the verification process will be recorded for subsequent analysis.

[0055] After obtaining a one-dimensional solver for liver blood flow that takes into account energy loss at vascular bifurcations, the solver is used to perform blood flow simulation calculations. The numerical calculation methods used in the calculation process include but are not limited to one or more combinations of the finite difference method, the finite volume method, and the finite element method. The convergence of the numerical calculation is monitored during the calculation process, and the calculation parameters or methods are adjusted when the convergence conditions are not met.

[0056] When performing blood flow simulation calculations, the inlet boundary condition is set to a pulsating flow with a time period of T0, where T0 is the cardiac cycle duration that conforms to the physiological conditions of the human body and is set to 0.75s. The outlet boundary condition is a ternary windkessel boundary condition. The DisContinuous Galerkin projection method, the Runge-Kutta time integration method, and the Upwind format are used for simulation calculations. The simulation time is set to T1, which is set to 10S. The calculation result of T2 seconds is selected as the final result, where T2 is a duration greater than 3 seconds and less than 10 seconds. During the simulation calculation process, the usage of computing resources is monitored to ensure computing efficiency and stability.

[0057] The method also includes a step of post-processing the simulation results, which includes data visualization, using ParaView and VisIt to draw blood flow velocity distribution, pressure distribution, and flow change curves, as well as evaluating the accuracy of the simulation results, and comparing and analyzing the simulation results with actual measurement data. The comparative analysis method includes root mean square error calculation, correlation coefficient calculation and statistical analysis means.

[0058] The method also includes the step of optimizing the entire simulation process, and the optimization directions include algorithm efficiency optimization and computing resource allocation optimization. The algorithm efficiency optimization is achieved by improving the calculation steps and reducing redundant calculation methods. The computing resource allocation optimization dynamically adjusts the allocation of memory and CPU resources according to the requirements of the computing task.

[0059] Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without making creative work should fall within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described and explained in the present invention are implemented in accordance with conventional means in the field unless otherwise specified or limited.

Claims

1. A one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations, characterized in that: The following steps are involved: Step 1: Based on real human liver vascular data, medical image processing technology is used to reconstruct the vascular model and extract the vascular centerline. The vascular segment length, starting end cross-sectional area, and bifurcation angle at the vascular bifurcation are obtained to construct a one-dimensional geometric model of the blood vessels. Step 2: Based on the one-dimensional geometric model, use code editing software such as Visual Studio Code to construct a one-dimensional vascular network model that can be read by Nektar++ software, set simulation parameters, and generate a simulation parameter setting file; Step 3: The algorithm for calculating the loss coefficient at the vascular bifurcation is implanted into the functional module for processing vascular bifurcation calculations in the Nektar++ software. The loss coefficient calculation algorithm includes the following steps: Step A1: Based on the vascular cross-sectional area and blood flow velocity parameters calculated by Nektar++, as well as the externally input vascular bifurcation angle parameters, the blood flow ratio and cross-sectional area ratio of parent vessel i and child vessel j are calculated. The blood flow ratio calculation formula is: The cross-sectional area ratio is calculated as: Among them, q i is the flow rate of parent vessel i, q j is the flow rate of sub-vessel j, A i is the cross-sectional area of parent vessel i, A j is the cross-sectional area of daughter vessel j; Step A2: Based on the above calculated proportional parameters, use the formula: and, Calculate the loss coefficient K between parent vessel i and child vessel j at the bifurcation i,j ,in is the angle between the axis of parent vessel i and the axis of child vessel j; Step A3: According to the blood flow rate u of the parent vessel i , blood density ρ and the calculated loss coefficient K i，j , through the formula: Calculate the energy loss p at the vascular bifurcation loss , and perform numerical stability checks on the calculation results; Step A4: Get the p loss By adding the momentum conservation law code part to the vascular bifurcation processing, a one-dimensional liver blood flow solver that takes into account the energy loss at the vascular bifurcation is obtained.

2. The one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations according to claim 1, characterized in that: In the step of constructing a one-dimensional geometric model of the blood vessel, the medical image data is preprocessed. The preprocessing operation includes image denoising using Gaussian filtering, median filtering and wavelet denoising methods, and contrast enhancement using histogram equalization and contrast-limited adaptive histogram equalization algorithms.

3. The one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations according to claim 1, characterized in that: In the step of embedding the loss coefficient calculation algorithm into Nektar++ software, the calculated loss coefficient K i，j Perform rationality check, including comparison with preset experience value range and theoretical calculation range. i，j If it is not within the preset reasonable range, the accuracy of the input parameters will be checked back and recalculated.

4. The method for simulating one-dimensional blood flow in hepatic vessels taking into account energy loss at vascular bifurcations according to claim 1, characterized in that: After obtaining a one-dimensional solver for liver blood flow that takes into account energy loss at vascular bifurcations, the solver is used to perform blood flow simulation calculations. The numerical calculation methods used in the calculation process include but are not limited to one or more combinations of the finite difference method, the finite volume method, and the finite element method. The convergence of the numerical calculation is monitored during the calculation process, and the calculation parameters or methods are adjusted when the convergence conditions are not met.

5. The one-dimensional blood flow simulation method for hepatic vessels taking into account energy loss at vascular bifurcations according to claim 4, characterized in that: When performing blood flow simulation calculations, the inlet boundary condition is set to a pulsating flow with a time period of T0, where T0 is the cardiac cycle duration that conforms to the physiological conditions of the human body and is set to 0.75s. The outlet boundary condition is a ternary windkessel boundary condition. The DisContinuous Galerkin projection method, the Runge-Kutta time integration method, and the Upwind format are used for simulation calculations. The simulation time is set to T1, which is set to 10S. The calculation result of T2 seconds is selected as the final result, where T2 is a duration greater than 3 seconds and less than 10 seconds.

6. The method for simulating one-dimensional blood flow in hepatic vessels taking into account energy loss at vascular bifurcations according to claim 1, characterized in that: The method also includes a step of post-processing the simulation results, which includes data visualization, using ParaView and VisIt to draw blood flow velocity distribution, pressure distribution, and flow change curves, as well as evaluating the accuracy of the simulation results, and comparing and analyzing the simulation results with actual measurement data. The comparative analysis method includes root mean square error calculation, correlation coefficient calculation and statistical analysis means.

7. The method for simulating one-dimensional blood flow in hepatic vessels taking into account energy loss at vascular bifurcations according to claim 1, characterized in that: The method also includes the step of optimizing the entire simulation process, and the optimization directions include algorithm efficiency optimization and computing resource allocation optimization. The algorithm efficiency optimization is achieved by improving the calculation steps and reducing redundant calculation methods. The computing resource allocation optimization dynamically adjusts the allocation of memory and CPU resources according to the requirements of the computing task.

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