A method of predicting blood flow patterns within the ascending aorta after TAVR

Abstract

The present disclosure relates to the field of intelligent medical treatment and biomechanics, and particularly relates to a method for predicting blood flow pattern in ascending aorta after TAVR, comprising the following steps: step 1, establishing a zero-dimensional cardiovascular lumped parameter model and calibrating; step 2, replacing the flow resistance model of the aortic valve module with the flow resistance model of the artificial valve, and then solving the calibrated zero-dimensional cardiovascular lumped parameter model to obtain the postoperative hemodynamic parameter time course; step 3, establishing a finite element model, and through transient dynamics analysis, extracting the motion projection time course of the valve structure at the annular plane in the cardiac cycle; step 4, setting the postoperative aortic valve blood flow time course as the inlet flow boundary condition of the ascending aorta, setting the postoperative aortic pressure time course as the outlet pressure boundary condition of the ascending aorta, and setting the motion projection time course as the inlet motion boundary condition of the ascending aorta, to realize the prediction of the blood flow pattern in the ascending aorta after operation. The present disclosure can quantitatively evaluate the improvement of blood flow in the ascending aorta after operation.

Description

Technical Field

[0001] This disclosure relates to the fields of smart healthcare and biomechanics, and in particular to a method for predicting blood flow patterns in the ascending aorta after TAVR. Background Technology

[0002] Degenerative calcification of the aortic valve and congenital bicuspid aortic valve malformation can cause abnormal blood flow patterns in the ascending aorta. The disordered blood flow will continuously impact and damage the vessel wall, gradually causing the ascending aorta to dilate, significantly increasing the risk of aortic dissection, vascular rupture, and even death. The patient population with this type of heart valve disease is huge, posing a serious threat to human life and health.

[0003] Transcatheter aortic valve replacement (TAVR) is currently one of the core minimally invasive treatments for aortic valve disease in clinical practice, offering advantages over traditional open-heart surgery such as less trauma, faster recovery, and wider applicability. However, current clinical TAVR surgery decisions heavily rely on the surgeon's individual clinical experience and the patient's imaging structural indicators. There is a lack of scientific and precise methods for predicting pre- and post-operative blood flow patterns, making it impossible to quantitatively assess the expected treatment outcome and achieve precise personalized surgical planning.

[0004] Furthermore, current technologies for studying aortic blood flow primarily rely on patient imaging data to segment and reconstruct blood vessels, followed by hemodynamic simulation analysis. In existing hemodynamic simulation techniques, the model boundary conditions mostly use fixed, known parameters or statistical averages from the general population, without personalizing them to the individual cardiovascular physiological characteristics of each patient.

[0005] In summary, the existing technology needs further improvement. Summary of the Invention

[0006] The purpose of this invention is to solve the problems of existing technologies, such as the inability to personalize the prediction of ascending aortic blood flow patterns after TAVR, low accuracy in obtaining postoperative blood flow parameters, and lack of physiological basis for simulation boundary conditions.

[0007] To address the aforementioned technical problems, this disclosure provides a method for predicting blood flow patterns in the ascending aorta after TAVR, comprising the following steps: Step 1: Establish a zero-dimensional cardiovascular lumped parameter model. Based on the patient's preoperative medical data, calibrate the zero-dimensional cardiovascular lumped parameter model. The zero-dimensional cardiovascular lumped parameter model includes, in sequence along the blood flow direction, the left atrial module, mitral valve module, left ventricular module, aortic valve module, and systemic circulation module. Step 2: Replace the flow resistance model of the aortic valve module with the flow resistance model of the artificial valve to simulate transcatheter aortic valve replacement surgery, and then solve the calibrated zero-dimensional cardiovascular lumped parameter model to obtain the postoperative hemodynamic parameters time history. The postoperative hemodynamic parameters time history includes the postoperative aortic pressure time history, the postoperative aortic valve blood flow time history, and the postoperative transvalvular pressure gradient time history. Step 3: Based on the patient's preoperative medical imaging data, construct a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque. Perform TAVR virtual surgery based on the three-dimensional geometric model, establish a finite element model, apply the postoperative transvalvular pressure gradient time history as the driving load to the finite element model, and extract the motion projection time history of the valve structure at the annular plane during the cardiac cycle through transient dynamic analysis. Step 4: Set the postoperative aortic valve blood flow time history as the ascending aortic inlet flow boundary condition, the postoperative aortic pressure time history as the ascending aortic outlet pressure boundary condition, and the motion projection time history as the ascending aortic inlet motion boundary condition. Perform computational fluid dynamics simulation to predict the postoperative blood flow pattern in the ascending aorta.

[0008] In some embodiments, in step 1, the zero-dimensional cardiovascular lumped parameter model is constructed based on the principle of similarity between fluid networks and electrical networks. The left atrial module, mitral valve module, left ventricular module, aortic valve module, and systemic circulation module are coupled with the continuity condition of blood flow pressure and flow rate to form a complete cardiovascular circulatory system, wherein: The left atrial module adopts a constant pressure source model, the mitral valve module and the aortic valve module both adopt a Bernoulli nonlinear resistance model, the left ventricular module adopts a single fiber mechanical model, and the systemic circulation module adopts a series structure of multiple three-element Winkserle models.

[0009] In some embodiments, in step 1, the patient's preoperative medical data includes any combination of the following: aortic pressure over time, left ventricular pressure over time, left ventricular end-diastolic diameter, left ventricular interventricular septum thickness, left ventricular posterior wall thickness, left ventricular ejection fraction, blood flow velocity at the aortic valve over time, mean transvalvular pressure gradient, and effective aortic valve orifice area.

[0010] In some embodiments, step 1, calibrating the zero-dimensional cardiovascular lumped parameter model, includes calibrating the zero-dimensional cardiovascular lumped parameter model using a differential evolution algorithm.

[0011] In some embodiments, step 1, calibrating the zero-dimensional cardiovascular lumped parameter model includes: randomly generating an initial parameter population within a physiologically reasonable range; solving the model's ordinary differential equations using the fourth-order Runge-Kutta method to calculate the cardiac cycle hemodynamic simulation parameters; constructing a multi-objective error function to quantify the deviation between the simulation data and clinically measured data; and iteratively optimizing through mutation, crossover, and selection until the error converges, thus completing the personalized calibration of the zero-dimensional cardiovascular lumped parameter model.

[0012] In some embodiments, in step 3, the patient's preoperative medical imaging data includes any combination of preoperative aortic root CT images and aortic valve ultrasound images.

[0013] In some embodiments, in step 3, constructing a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque includes: obtaining a geometric model of the aortic root vessel wall and a geometric model of calcified plaque by segmenting and three-dimensional reconstruction of the aortic root CT image based on aortic root CT image and aortic valve ultrasound image; constructing a geometric model of autologous valve by aortic valve ultrasound image; and fusing the geometric model of the aortic root vessel wall, the geometric model of calcified plaque, and the geometric model of autologous valve through three-dimensional registration and Boolean operation to obtain a three-dimensional geometric model.

[0014] In some embodiments, in step 3, the transient dynamic analysis includes: assigning personalized material properties to the three-dimensional geometric model of the blood vessel wall, valve, and calcified plaque, simulating the artificial valve stent implantation and opening process to complete a virtual surgery; applying the time-varying transvalvular pressure difference as a surface force load to the artificial valve structure, solving the displacement time history of each node in the annular plane during the cardiac cycle, and fitting the spatial projection time history of valve motion.

[0015] In some embodiments, step 4, the computational fluid dynamics simulation includes: solving the incompressible Navier-Stokes equations using the finite element method, and simulating at least 3 Five cardiac cycles until the blood flow field reaches a periodic steady state.

[0016] In some embodiments, in step 4, the prediction parameters for the postoperative blood flow pattern in the ascending aorta include any combination of the ascending aortic blood flow velocity field, the vessel wall shear stress field, the blood flow pressure field, and the blood flow turbulence intensity.

[0017] By adopting the above technical solution, this disclosure has at least the following beneficial effects: 1. Provides precise postoperative hemodynamic risk assessment indicators. This invention can accurately output multi-dimensional dynamic parameters such as the ascending aortic blood flow velocity field, vessel wall shear stress field, blood flow pressure field, and blood flow turbulence intensity through computational fluid dynamics simulation, enabling quantitative assessment of the improvement in blood flow patterns in the ascending aorta after TAVR.

[0018] 2. Accurate calibration of patient-specific zero-dimensional cardiovascular models was achieved. By fusing preoperative ultrasound and imaging multi-source data through differential evolution algorithms, a single-fiber model was used to describe left ventricular biomechanics, and a three-element Windkessel series circuit was used to describe systemic circulation. This enabled the model to accurately reproduce individualized hemodynamic characteristics such as preoperative aortic pressure, left ventricular pressure, ejection fraction, and transvalvular pressure gradient, providing a reliable individualized benchmark for post-TAVR prediction.

[0019] 3. It enables non-invasive preoperative prediction of hemodynamics after TAVR. Before the procedure, the hemodynamic response after artificial valve implantation can be simulated simply by modifying the aortic valve module in the zero-dimensional model. The waveforms of postoperative aortic pressure, left ventricular pressure, transvalvular pressure gradient, and blood flow can be quickly output, providing quantitative basis for clinical evaluation of surgical efficacy, selection of stent type, and formulation of implantation strategy.

[0020] 4. A multi-scale coupling mechanism between zero-dimensional hemodynamics and three-dimensional structural mechanics was established. The postoperative transvalvular pressure gradient time history predicted by the zero-dimensional model was applied as a surface force load to the finite element model of the TAVR virtual surgery, driving the artificial valve to move realistically during the cardiac cycle and obtaining the motion projection at the annular plane. This solved the problem of the lack of physiological basis for valve motion boundary conditions in traditional simulations and significantly improved the accuracy of postoperative blood flow pattern prediction. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for predicting blood flow patterns in the ascending aorta after TAVR, according to an embodiment of this disclosure; Figure 2 This is a circuit diagram of a zero-dimensional cardiovascular lumped parameter model according to an embodiment of the present disclosure. Detailed Implementation

[0023] The embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. The detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of this disclosure by way of example, but should not be used to limit the scope of this disclosure. This disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0024] These embodiments are provided to make the disclosure thorough and complete, and to fully express the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps, material composition, numerical expressions, and values ​​set forth in these embodiments should be interpreted as exemplary only and not as limiting.

[0025] It should be noted that, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientation or positional relationship, are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0026] Furthermore, the terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different parts. "Vertical" is not strictly vertical, but within the permissible margin of error. "Parallel" is not strictly parallel, but within the permissible margin of error. Terms such as "including" or "contains" mean that the element preceding the word encompasses the element listed after the word, and do not exclude the possibility of encompassing other elements as well.

[0027] It should also be noted that, in the description of this disclosure, unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this disclosure depending on the specific circumstances. When a particular device is described as being located between a first device and a second device, an intermediary device may or may not be present between the particular device and the first or second device.

[0028] All terms used in this disclosure have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein.

[0029] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0030] As mentioned in the background section, transcatheter aortic valve replacement (TAVR) is currently one of the core minimally invasive treatments for aortic valve disease, offering advantages over traditional open-chest surgery such as less trauma, faster recovery, and wider applicability. However, current clinical TAVR surgery decisions heavily rely on the surgeon's individual clinical experience and the patient's imaging structural indicators, lacking scientific and precise methods for predicting preoperative and postoperative blood flow patterns. This makes it impossible to quantitatively assess the expected treatment effect and achieve precise personalized surgical planning. Based on the above, this disclosure provides a method for predicting the blood flow pattern in the ascending aorta after TAVR. Through computational fluid dynamics simulation, it can accurately output multi-dimensional dynamic parameters such as the ascending aortic blood flow velocity field, vessel wall shear stress field, blood flow pressure field, and blood flow turbulence intensity, enabling quantitative assessment of the improvement in the ascending aortic blood flow pattern after TAVR.

[0031] This disclosure provides a method for predicting blood flow patterns in the ascending aorta after TAVR, including the following steps: Step 1: Establish a zero-dimensional cardiovascular lumped parameter model. Based on the patient's preoperative medical data, calibrate the zero-dimensional cardiovascular lumped parameter model. The zero-dimensional cardiovascular lumped parameter model includes, in sequence along the blood flow direction, the left atrial module, mitral valve module, left ventricular module, aortic valve module, and systemic circulation module. Step 2: Replace the flow resistance model of the aortic valve module with the flow resistance model of the artificial valve to simulate transcatheter aortic valve replacement surgery, and then solve the calibrated zero-dimensional cardiovascular lumped parameter model to obtain the postoperative hemodynamic parameters time history. The postoperative hemodynamic parameters time history includes the postoperative aortic pressure time history, the postoperative aortic valve blood flow time history, and the postoperative transvalvular pressure gradient time history. Step 3: Based on the patient's preoperative medical imaging data, construct a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque. Perform TAVR virtual surgery based on the three-dimensional geometric model, establish a finite element model, apply the postoperative transvalvular pressure gradient time history as the driving load to the finite element model, and extract the motion projection time history of the valve structure at the annular plane during the cardiac cycle through transient dynamic analysis. Step 4: Set the postoperative aortic valve blood flow time history as the ascending aortic inlet flow boundary condition, the postoperative aortic pressure time history as the ascending aortic outlet pressure boundary condition, and the motion projection time history as the ascending aortic inlet motion boundary condition. Perform computational fluid dynamics simulation to predict the postoperative blood flow pattern in the ascending aorta.

[0032] Compared with existing technologies, this disclosure proposes a method for predicting blood flow patterns in the ascending aorta after TAVR. This method can accurately output multi-dimensional dynamic parameters such as the ascending aortic blood flow velocity field, vessel wall shear stress field, blood flow pressure field, and blood flow turbulence intensity. It can intuitively and quantitatively compare the differences in blood flow patterns before and after surgery, accurately assess the stress state of the vessel wall and the degree of blood flow turbulence, and provide core quantitative indicators for the risk prediction of long-term complications such as postoperative aortic dilatation and dissection, thus assisting in precise clinical diagnosis and treatment.

[0033] In the above method, in step 1, the zero-dimensional cardiovascular lumped parameter model is constructed based on the principle of similarity between fluid networks and electrical networks. The left atrial module, mitral valve module, left ventricular module, aortic valve module, and systemic circulation module are coupled with the continuity condition of blood flow pressure and flow rate to form a complete cardiovascular circulatory system, wherein: The left atrial module adopts a constant pressure source model, the mitral valve module and the aortic valve module both adopt a Bernoulli nonlinear resistance model, the left ventricular module adopts a single fiber mechanical model, and the systemic circulation module adopts a series structure of multiple three-element Winkserle models.

[0034] In the above method, in step 1, the patient's preoperative medical data includes any combination of the following: aortic pressure over time, left ventricular pressure over time, left ventricular end-diastolic diameter, left ventricular interventricular septum thickness, left ventricular posterior wall thickness, left ventricular ejection fraction, blood flow velocity at the aortic valve over time, mean transvalvular pressure gradient, and effective aortic valve orifice area.

[0035] In the above method, step 1, calibrating the zero-dimensional cardiovascular lumped parameter model includes: calibrating the zero-dimensional cardiovascular lumped parameter model using a differential evolution algorithm.

[0036] In the above method, step 1, calibrating the zero-dimensional cardiovascular lumped parameter model includes: randomly generating an initial parameter population within a physiologically reasonable range; using the fourth-order Runge-Kutta method to solve the model's ordinary differential equations to calculate the cardiac cycle hemodynamic simulation parameters; constructing a multi-objective error function to quantify the deviation between the simulation data and the clinically measured data; and iteratively optimizing through mutation, crossover, and selection until the error converges to complete the personalized calibration of the zero-dimensional cardiovascular lumped parameter model.

[0037] In the above method, in step 3, the patient's preoperative medical imaging data includes any combination of the patient's preoperative aortic root CT images and aortic valve ultrasound images.

[0038] In the above method, step 3, constructing a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque, includes: based on aortic root CT images and aortic valve ultrasound images, obtaining the aortic root vessel wall geometric model and calcified plaque geometric model through aortic root CT image segmentation and three-dimensional reconstruction; constructing the autologous valve geometric model through aortic valve ultrasound images; and fusing the aortic root vessel wall geometric model, calcified plaque geometric model, and autologous valve geometric model through three-dimensional registration and Boolean operation to obtain a three-dimensional geometric model.

[0039] In the above method, in step 3, the transient dynamic analysis includes: assigning personalized material properties to the three-dimensional geometric model of the blood vessel wall, valve, and calcified plaque, simulating the artificial valve stent implantation and opening process to complete a virtual surgery; applying the time-varying transvalvular pressure difference as a surface force load to the artificial valve structure, solving the displacement time history of each node in the valve annulus plane during the cardiac cycle, and fitting the valve motion spatial projection time history.

[0040] In the above method, step 4, the computational fluid dynamics simulation includes: solving the incompressible Navier-Stokes equations using the finite element method, and simulating at least 3 Five cardiac cycles until the blood flow field reaches a periodic steady state.

[0041] In the above method, in step 4, the prediction parameters for the postoperative blood flow pattern in the ascending aorta include any combination of the ascending aortic blood flow velocity field, the vessel wall shear stress field, the blood flow pressure field, and the blood flow turbulence intensity.

[0042] The working process and working principle of the technical solution of the present invention will be further explained below, such as... Figure 1 As shown.

[0043] The 0D cardiovascular lumped parameter model (CLM) is constructed using an electrical network analogy method. Based on the similarity principle between fluid networks and electrical networks, it maps hemodynamic parameters to circuit parameters. For example... Figure 2 As shown, the zero-dimensional cardiovascular lumped parameter model consists of five functional modules, arranged sequentially along the blood flow direction: left atrial module Ppul, mitral valve module MV, left ventricular module LV, aortic valve module AV, and systemic circulation module. These modules are coupled through the continuity of blood flow pressure and flow rate, forming a complete circulatory system. The left atrial module Ppul uses a constant pressure source model, setting the left atrial pressure PLA to a constant value. The initial setting of PLA is determined based on the mean left atrial pressure measured by preoperative echocardiography. This constant pressure source serves as the entry boundary condition for the lumped parameter model, providing stable pressure for subsequent modules. The mitral valve module MV uses a Bernoulli-type nonlinear resistance model. The mitral valve flow rate relationship is as follows: , in, : Mitral valve resistance coefficient. When the mitral valve is closed, the blood flow is directly set to 0.

[0044] The left ventricular (LV) module uses a single-fiber model, assuming the ventricle is a thick-walled sphere with muscle fibers uniformly distributed circumferentially, and stress and strain uniform within the wall. The relationship between ventricular pressure and fiber stress is as follows:

[0045] Cauchy stress in the fiber direction Radial stress : Ventricular wall volume The active stress of the fiber is:

[0046] : Fiber radial active stress under reference state Shrinkage parameter : Sarcomere length influence function Temporal activation function Muscle contraction force-velocity relationship The passive stress of the fiber is:

[0047]

[0048] Fiber draw ratio:

[0049]

[0050] : Ventricular cavity volume under no-load conditions The aortic valve module (AV) also uses a Bernoulli-type nonlinear resistance model, and the pressure-flow relationship is as follows:

[0051] in, The forward resistance coefficient is determined based on the effective orifice area of ​​the patient's aortic valve. When the aortic valve is closed, the blood flow is directly set to 0.

[0052] The systemic circulation module consists of three three-element Windkessel models (three-element Windkessel artery elasticity models) connected in series and a distal resistor Rdist. Each three-element Windkessel model comprises an inductor Lart, a resistor Rprox, and a capacitor Vc. Its circuit equations are as follows:

[0053]

[0054]

[0055] The calibration of parameters within a zero-dimensional cardiovascular lumped parameter model requires obtaining preoperative medical ultrasound data and preoperative medical imaging data of the target patient, specifically including: the relationship between aortic pressure and time, the relationship between left ventricular pressure and time, the left ventricular end-diastolic diameter, the thickness of the left ventricular interventricular septum, the thickness of the left ventricular posterior wall, the left ventricular ejection fraction, the relationship between blood flow velocity at the aortic valve and time, the mean transvalvular pressure gradient, and the effective valve orifice area of ​​the aortic valve.

[0056] The aforementioned clinical data are mapped to the parameters to be calibrated in a lumped parameter model, and the differential evolution algorithm is used to globally optimize the parameters to be calibrated in the lumped parameter model. The specific execution steps are as follows: Initialization of the population (randomly generating an initial population within a preset physiologically reasonable range, with upper and lower bounds for each parameter set based on physiological statistical values ​​of normal populations), ODE (Ordinary Differential Equation) system solution: The fourth-order Runge-Kutta method is used to solve the ODE system of the lumped parameter model to calculate the hemodynamic parameters for one cardiac cycle, error function calculation: a multi-objective error function is constructed to quantify the deviation between the simulation results and clinical data, differential evolution iteration: the mutation, crossover, and selection operations of the standard DE algorithm (Differential Evolutionary Algorithm) are executed until the convergence condition is met. When the hemodynamic parameters output by the lumped parameter model match the patient's clinical data, the lumped parameter model can then represent the cardiovascular hemodynamic characteristics of the target patient in a personalized way.

[0057] After successful model parameter calibration, the aortic valve module (AV) was modified to simulate transcatheter aortic valve replacement (TAVR). Specific modifications included replacing the original Bernoulli-type nonlinear resistance model of the diseased aortic valve with a flow resistance model of the prosthetic valve.

[0058] The modified lumped parameter model is used to resolve the ODE system and output postoperative hemodynamic parameters, including: postoperative aortic pressure waveform, left ventricular pressure waveform, postoperative aortic valve blood flow waveform, and postoperative transvalvular pressure gradient waveform.

[0059] Preoperative medical imaging data of the target patient is acquired, and a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaques is constructed based on this data. Specifically, this includes: Preoperative CT images of the aortic root wall and aortic valve were obtained by scanning the target patient using CT equipment and ultrasound images of the target patient. The preoperative CT images were imported into medical image processing software, and geometric models of the aortic root wall and calcified plaques were generated through segmentation and reconstruction. A geometric model of the autologous valve was constructed based on the preoperative aortic valve ultrasound images. The three models were then fused using 3D registration and Boolean operations to construct a three-dimensional geometric model including the aortic root wall, the autologous valve, and the calcified plaques.

[0060] Obtain the stent type and implantation strategy to be used in the treatment plan, and construct the stent geometric model.

[0061] Finite element analysis was performed, and based on a three-dimensional geometric model, initial material property parameters were assigned to the aortic root vessel wall, autologous valve, and calcified plaque. The stent implantation process was simulated to obtain the results of the TAVR virtual surgery.

[0062] The postoperative transvalvular pressure gradient curve predicted in the lumped parameter model was applied to the TAVR virtual surgery results. In the finite element model, the time-varying transvalvular pressure gradient predicted by the zero-dimensional model was applied as a driving load to the artificial valve to simulate the effect of the pressure difference across the valve on valve motion. Transient dynamic analysis was performed to extract the displacement time history of each node at the annular plane and calculate the projection of the valve's cardiac cycle motion onto the annular plane. This projection characterizes the overall motion amplitude of the valve structure at the annular plane during the cardiac cycle and serves as the inlet motion boundary condition for subsequent CFD (Computational Fluid Dynamics) simulations. The lumped parameter model and TAVR virtual surgery results were fused using multi-scale data, and the following time-varying boundary conditions were extracted for CFD simulation: the relationship between aortic valve blood flow and time, directly output from the calibrated lumped parameter model, served as the inlet flow boundary condition for CFD simulation; the relationship between aortic pressure and time, output from the lumped parameter model, served as the outlet pressure boundary condition for CFD simulation; and the relationship between the projection space of the valve's cardiac cycle motion onto the annular plane and time, extracted from the TAVR virtual surgery results, served as the inlet motion boundary condition for CFD simulation. The incompressible Navier-Stokes equations were solved using the finite element method, simulating at least 3 Five cardiac cycles were performed until the blood flow field reached a periodic steady state. After the CFD simulation was completed, the following postoperative hemodynamic parameters were extracted and output for clinical evaluation: blood flow velocity field, wall shear stress, pressure field, and turbulence intensity. By comparing these parameters with the preoperative state, the improvement effect of TAVR surgery on the hemodynamics of the target patient could be quantitatively evaluated, and personalized prediction of the patient's postoperative blood flow pattern could be completed.

[0063] The embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0064] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. In particular, as long as there is no structural conflict, the technical features mentioned in the various embodiments can be combined in any manner.

Claims

1. A method for predicting blood flow patterns in the ascending aorta after TAVR, characterized in that, Includes the following steps: Step 1: Establish a zero-dimensional cardiovascular lumped parameter model. Based on the patient's preoperative medical data, calibrate the zero-dimensional cardiovascular lumped parameter model. The zero-dimensional cardiovascular lumped parameter model includes, in sequence along the blood flow direction, a left atrial module, a mitral valve module, a left ventricular module, aortic valve module, and a systemic circulation module. Step 2: Replace the flow resistance model of the aortic valve module with the flow resistance model of the artificial valve to simulate transcatheter aortic valve replacement surgery, and then solve the calibrated zero-dimensional cardiovascular lumped parameter model to obtain the postoperative hemodynamic parameters time history. The postoperative hemodynamic parameters time history includes the postoperative aortic pressure time history, the postoperative aortic valve blood flow time history, and the postoperative transvalvular pressure gradient time history. Step 3: Based on the patient's preoperative medical imaging data, construct a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque. Perform TAVR virtual surgery based on the three-dimensional geometric model, establish a finite element model, and apply the postoperative transvalvular pressure gradient time history as a driving load to the finite element model. Through transient dynamic analysis, extract the motion projection time history of the valve structure at the annular plane during the cardiac cycle. Step 4: Set the postoperative aortic valve blood flow time history as the ascending aortic inlet flow boundary condition, the postoperative aortic pressure time history as the ascending aortic outlet pressure boundary condition, and the motion projection time history as the ascending aortic inlet motion boundary condition. Perform computational fluid dynamics simulation to predict the postoperative blood flow pattern in the ascending aorta.

2. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 1, the zero-dimensional cardiovascular lumped parameter model is constructed based on the principle of similarity between fluid networks and electrical networks. The left atrial module, the mitral valve module, the left ventricular module, the aortic valve module, and the systemic circulation module are coupled through the continuity of blood flow pressure and flow rate to form a complete cardiovascular circulatory system, wherein: The left atrial module adopts a constant pressure source model, the mitral valve module and the aortic valve module both adopt a Bernoulli nonlinear resistance model, the left ventricular module adopts a single fiber mechanical model, and the systemic circulation module adopts a series structure of multiple three-element Winkserle models.

3. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 1, the patient's preoperative medical data includes any combination of the following: aortic pressure over time, left ventricular pressure over time, left ventricular end-diastolic diameter, left ventricular interventricular septum thickness, left ventricular posterior wall thickness, left ventricular ejection fraction, blood flow velocity at the aortic valve over time, mean transvalvular pressure gradient, and effective aortic valve orifice area.

4. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, Step 1, calibrating the zero-dimensional cardiovascular lumped parameter model, includes: The zero-dimensional cardiovascular lumped parameter model was calibrated using the differential evolution algorithm.

5. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 4, characterized in that, Step 1, the calibration of the zero-dimensional cardiovascular lumped parameter model includes: The initial parameter population is randomly generated within a physiologically reasonable range. The fourth-order Runge-Kutta method was used to solve the system of ordinary differential equations of the model, and the simulation parameters of cardiac cycle hemodynamics were calculated. Construct a multi-objective error function to quantify the deviation between simulation data and clinical experimental data; Through iterative optimization of mutation, crossover, and selection until the error converges, the personalized calibration of the zero-dimensional cardiovascular lumped parameter model is completed.

6. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 3, the patient's preoperative medical imaging data includes any combination of the patient's preoperative aortic root CT images and aortic valve ultrasound images.

7. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 6, characterized in that, In step 3, constructing a three-dimensional geometric model including the aortic root vessel wall, autologous valve, and calcified plaque includes: Based on the aortic root CT image and the aortic valve ultrasound image, the geometric model of the aortic root vessel wall and the geometric model of calcified plaques are obtained by segmenting and three-dimensional reconstruction of the aortic root CT image, and the geometric model of the autologous valve is constructed by the aortic valve ultrasound image. The geometric model of the aortic root vessel wall, the geometric model of the calcified plaque, and the geometric model of the autologous valve are fused together through three-dimensional registration and Boolean operation to obtain the three-dimensional geometric model.

8. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 3, the transient dynamic analysis includes: Personalized material properties of blood vessel walls, valves, and calcified plaques are assigned to three-dimensional geometric models to simulate the expansion process of artificial valve stent implantation and complete virtual surgery; The time-varying transvalvular pressure difference is applied as a surface force load to the artificial valve structure. The displacement time history of each node in the annular plane during the cardiac cycle is solved, and the spatial projection time history of valve motion is obtained by fitting.

9. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 4, the computational fluid dynamics simulation includes: Solving the incompressible Navier-Stokes equations using the finite element method, simulating at least 3 Five cardiac cycles until the blood flow field reaches a periodic steady state.

10. The method for predicting blood flow patterns in the ascending aorta after TAVR according to claim 1, characterized in that, In step 4, the prediction parameters for the postoperative ascending aortic blood flow pattern include any combination of the ascending aortic blood flow velocity field, the vessel wall shear stress field, the blood flow pressure field, and the blood flow turbulence intensity.

Images