Numerical Simulation of the Effect of Pulmonary Vascular Resistance on the Hemodynamics of Reoperation After Failure of One and a Half Ventricle Repair

Fu, Yan; Qiao, Aike; Yang, Yao; Fan, Xiaoxuan

doi:10.3389/fphys.2020.00207

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…and to discretize computational domain; 2) computation of flow fields in terms of pressures and velocities by solving the Navier-Stokes equations under certain boundary conditions ( Yin et al, 2018 ; Driessen et al, 2019 ; Han et al, 2021 ; Hou et al, 2022 ; Rizzini et al, 2022 ); and 3) post-processing to visualize flow fields while calculating hemodynamic parameters such as wall shear stresses. Thus, the CFD-based simulations are of high computational cost due to the requirements of mighty computing resources, large-scale computing time, and highly skilled experts ( Yamaguchi et al, 2016 ; Fu et al, 2020 ). Moreover, the simulation is generally performed in a patient-specific manner by using the image-based geometric model for each individual under specific boundary conditions, which needs to be conducted for all patients and is usually highly time-consuming ( Fu et al, 2010 ; Conti et al, 2016 ; Bluestein, 2017 ; Polanczyk et al, 2018 ; Albadawi et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

Wang

et al. 2023

Front. Physiol.

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Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

Wang

et al. 2023

Front. Physiol.

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“…Some complications, such as intimal hyperplasia and curvature distortion, may occur within a month after the surgery due to increasing pressure of the pulmonary artery from its connection to the systemic circulation 13,14 . Moreover, if the treatment is not planned correctly, many functional anomalies such as severe pulmonary regurgitation and right ventricular dilation will be likely 15,16 . Therefore, understanding the hemodynamics of the palliated TOF is necessary for planning an appropriate treatment process.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the numerical simulation approach was employed to simulate the blood flows in 3D domains for various configurations to investigate the pressure drop for the shunt, 17 the effect of the size of the shunt, 26 the effect of different angles, 27 the combination of different size and angles 18 or configurations 20,28 between the shunt and the pulmonary artery. The lumped‐parameter model has also been used to investigate blood distribution in various numerical studies such as simulating cavopulmonary anastomosis, 29,30 one‐and‐a‐half ventricle repair (1.5VR) 16 in order to generate the boundary conditions for a 3D model. Multiscale models showed a significant capability to capture the effects of the closed loop of entire circulatory system and the details of the blood flow in the region of interest 19,23‐25,31‐33 .…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling of a modified Blalock‐Taussig surgery in a patient‐specific tetralogy of Fallot

Keramati

Houts

Chen

et al. 2021

Numer Methods Biomed Eng

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Tetralogy of Fallot (TOF) is a congenital heart anomaly that causes a drastic reduction in the oxygen level. In this study, we coupled a lumped‐parameter model with a patient‐specific three‐dimensional (3D) model which included a modified Blalock‐Taussig (MBT) shunt. By forming a closed loop, we investigated the effects of certain parameters on the flow rates and the pressures at different locations of the developed network. A local sensitivity analysis on an initial zero‐dimensional (0D) closed‐loop model was conducted. The 0D lumped parameter (LP) model was then refined based on the results of the multiscale 0D‐3D model and the local sensitivity analysis was repeated for the refined 0D model. It was shown that the maximum pressure of the pulmonary bed had the highest sensitivity of 94% to the diameter of MBT shunt. We observed that the existence of the flow in the shunt during the diastole caused an elevated wall shear stress (WSS) in the pulmonary artery. In this work, we calculated the flow velocity and pressure field in a 3D patient‐specific aorta with an MBT shunt, and then we used the results to increase the accuracy of our LP model to simulate numerous 0D simulations in a significantly shorter time, which is potentially applicable for medical decision‐making.

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“…A large number of previous studies have used computational fluid dynamics (CFD) to obtain cardiovascular hemodynamics [15][16][17][18][19] . Based on the patient's cardiovascular geometry, provided by medical imaging data (e.g., MRI, CT, etc.)…”

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confidence: 99%

Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

Wang

Zhang

et al. 2021

Commun Biol

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The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

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Numerical Simulation of the Effect of Pulmonary Vascular Resistance on the Hemodynamics of Reoperation After Failure of One and a Half Ventricle Repair

Cited by 4 publications

References 34 publications

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments

Multiscale modeling of a modified Blalock‐Taussig surgery in a patient‐specific tetralogy of Fallot

Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

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