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

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...

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Autores principales: Wang, Sirui, Wu, Dandan, Li, Gaoyang, Zhang, Zhiyuan, Xiao, Weizhong, Li, Ruichen, Qiao, Aike, Jin, Long, Liu, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872942/
https://www.ncbi.nlm.nih.gov/pubmed/36703930
http://dx.doi.org/10.3389/fphys.2022.1094743
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author Wang, Sirui
Wu, Dandan
Li, Gaoyang
Zhang, Zhiyuan
Xiao, Weizhong
Li, Ruichen
Qiao, Aike
Jin, Long
Liu, Hao
author_facet Wang, Sirui
Wu, Dandan
Li, Gaoyang
Zhang, Zhiyuan
Xiao, Weizhong
Li, Ruichen
Qiao, Aike
Jin, Long
Liu, Hao
author_sort Wang, Sirui
collection PubMed
description 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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spelling pubmed-98729422023-01-25 Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments Wang, Sirui Wu, Dandan Li, Gaoyang Zhang, Zhiyuan Xiao, Weizhong Li, Ruichen Qiao, Aike Jin, Long Liu, Hao Front Physiol Physiology 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. Frontiers Media S.A. 2023-01-10 /pmc/articles/PMC9872942/ /pubmed/36703930 http://dx.doi.org/10.3389/fphys.2022.1094743 Text en Copyright © 2023 Wang, Wu, Li, Zhang, Xiao, Li, Qiao, Jin and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Wang, Sirui
Wu, Dandan
Li, Gaoyang
Zhang, Zhiyuan
Xiao, Weizhong
Li, Ruichen
Qiao, Aike
Jin, Long
Liu, Hao
Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title_full Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title_fullStr Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title_full_unstemmed Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title_short Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
title_sort deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872942/
https://www.ncbi.nlm.nih.gov/pubmed/36703930
http://dx.doi.org/10.3389/fphys.2022.1094743
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