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Alamir SH, Tufaro V, Trilli M, Kitslaar P, Mathur A, Baumbach A, Jacob J, Bourantas CV, Torii R. Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning. Front Bioeng Biotechnol 2024; 12:1360330. [PMID: 39188371 PMCID: PMC11345599 DOI: 10.3389/fbioe.2024.1360330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 07/12/2024] [Indexed: 08/28/2024] Open
Abstract
There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.
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Affiliation(s)
- Salwa Husam Alamir
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | - Vincenzo Tufaro
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Matilde Trilli
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | | | - Anthony Mathur
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
| | - Andreas Baumbach
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Joseph Jacob
- Satsuma Lab, Centre for Medical Image Computing, University College London, London, United Kingdom
- UCL Respiratory, University College London, London, United Kingdom
| | - Christos V. Bourantas
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, United Kingdom
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2
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Suk J, de Haan P, Lippe P, Brune C, Wolterink JM. Mesh neural networks for SE(3)-equivariant hemodynamics estimation on the artery wall. Comput Biol Med 2024; 173:108328. [PMID: 38552282 DOI: 10.1016/j.compbiomed.2024.108328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 01/29/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice. Machine-learning methods that estimate blood flow in individual patients could accelerate or replace CFD simulation to overcome these limitations. In this work, we consider the estimation of vector-valued quantities on the wall of three-dimensional geometric artery models. We employ group-equivariant graph convolution in an end-to-end SE(3)-equivariant neural network that operates directly on triangular surface meshes and makes efficient use of training data. We run experiments on a large dataset of synthetic coronary arteries and find that our method estimates directional wall shear stress (WSS) with an approximation error of 7.6% and normalised mean absolute error (NMAE) of 0.4% while up to two orders of magnitude faster than CFD. Furthermore, we show that our method is powerful enough to accurately predict transient, vector-valued WSS over the cardiac cycle while conditioned on a range of different inflow boundary conditions. These results demonstrate the potential of our proposed method as a plugin replacement for CFD in the personalised prediction of hemodynamic vector and scalar fields.
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Affiliation(s)
- Julian Suk
- Department of Applied Mathematics & Technical Medical Center, University of Twente, Enschede, 7522 NB, The Netherlands.
| | - Pim de Haan
- Qualcomm AI Research, Qualcomm Technologies Netherlands B.V., Nijmegen, 6546 AS, The Netherlands; QUVA Lab, University of Amsterdam, Amsterdam, 1012 WX, The Netherlands
| | - Phillip Lippe
- QUVA Lab, University of Amsterdam, Amsterdam, 1012 WX, The Netherlands
| | - Christoph Brune
- Department of Applied Mathematics & Technical Medical Center, University of Twente, Enschede, 7522 NB, The Netherlands
| | - Jelmer M Wolterink
- Department of Applied Mathematics & Technical Medical Center, University of Twente, Enschede, 7522 NB, The Netherlands
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3
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Shi J, Manjunatha K, Behr M, Vogt F, Reese S. A physics-informed deep learning framework for modeling of coronary in-stent restenosis. Biomech Model Mechanobiol 2024; 23:615-629. [PMID: 38236483 DOI: 10.1007/s10237-023-01796-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 11/22/2023] [Indexed: 01/19/2024]
Abstract
Machine learning (ML) techniques have shown great potential in cardiovascular surgery, including real-time stenosis recognition, detection of stented coronary anomalies, and prediction of in-stent restenosis (ISR). However, estimating neointima evolution poses challenges for ML models due to limitations in manual measurements, variations in image quality, low data availability, and the difficulty of acquiring biological quantities. An effective in silico model is necessary to accurately capture the mechanisms leading to neointimal hyperplasia. Physics-informed neural networks (PINNs), a novel deep learning (DL) method, have emerged as a promising approach that integrates physical laws and measurements into modeling. PINNs have demonstrated success in solving partial differential equations (PDEs) and have been applied in various biological systems. This paper aims to develop a robust multiphysics surrogate model for ISR estimation using the physics-informed DL approach, incorporating biological constraints and drug elution effects. The model seeks to enhance prediction accuracy, provide insights into disease progression factors, and promote ISR diagnosis and treatment planning. A set of coupled advection-reaction-diffusion type PDEs is constructed to track the evolution of the influential factors associated with ISR, such as platelet-derived growth factor (PDGF), the transforming growth factor- β (TGF- β ), the extracellular matrix (ECM), the density of smooth muscle cells (SMC), and the drug concentration. The nature of PINNs allows for the integration of patient-specific data (procedure-related, clinical and genetic, etc.) into the model, improving prediction accuracy and assisting in the optimization of stent implantation parameters to mitigate risks. This research addresses the existing gap in predictive models for ISR using DL and holds the potential to enhance patient outcomes through predictive risk assessment.
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Affiliation(s)
- Jianye Shi
- Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany.
| | - Kiran Manjunatha
- Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany
| | - Marek Behr
- Chair for Computational Analysis of Technical Systems, RWTH Aachen University, Aachen, Germany
| | - Felix Vogt
- Department of Cardiology, Pulmonology, Intensive Care and Vascular Medicine, RWTH Aachen University, Aachen, Germany
| | - Stefanie Reese
- Institute of Applied Mechanics, RWTH Aachen University, Aachen, Germany
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Williams J, Ahlqvist H, Cunningham A, Kirby A, Katz I, Fleming J, Conway J, Cunningham S, Ozel A, Wolfram U. Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks. PLoS One 2024; 19:e0297437. [PMID: 38277381 PMCID: PMC10817191 DOI: 10.1371/journal.pone.0297437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/04/2024] [Indexed: 01/28/2024] Open
Abstract
For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.
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Affiliation(s)
- Josh Williams
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
- Hartree Centre, STFC Daresbury Laboratory, Daresbury, United Kingdom
| | - Haavard Ahlqvist
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
| | - Alexander Cunningham
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
| | - Andrew Kirby
- Royal Hospital for Children and Young People, NHS Lothian, Edinburgh, United Kingdom
| | | | - John Fleming
- National Institute of Health Research Biomedical Research Centre in Respiratory Disease, Southampton, United Kingdom
- Department of Medical Physics and Bioengineering, University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Joy Conway
- National Institute of Health Research Biomedical Research Centre in Respiratory Disease, Southampton, United Kingdom
- Respiratory Sciences, Centre for Health and Life Sciences, Brunel University, London, United Kingdom
| | - Steve Cunningham
- Centre for Inflammation Research, University of Edinburgh, Edinburgh, United Kingdom
| | - Ali Ozel
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
| | - Uwe Wolfram
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
- Institute for Material Science and Engineering, TU Clausthal, Clausthal-Zellerfeld, Germany
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Feng Y, Fu R, Sun H, Wang X, Yang Y, Wen C, Hao Y, Sun Y, Li B, Li N, Yang H, Feng Q, Liu J, Liu Z, Zhang L, Liu Y. Non-invasive fractional flow reserve derived from reduced-order coronary model and machine learning prediction of stenosis flow resistance. Artif Intell Med 2024; 147:102744. [PMID: 38184351 DOI: 10.1016/j.artmed.2023.102744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Recently, computational fluid dynamics enables the non-invasive calculation of fractional flow reserve (FFR) based on 3D coronary model, but it is time-consuming. Currently, machine learning technique has emerged as an efficient and reliable approach for prediction, which allows saving a lot of analysis time. This study aimed at developing a simplified FFR prediction model for rapid and accurate assessment of functional significance of stenosis. METHODS A reduced-order lumped parameter model (LPM) of coronary system and cardiovascular system was constructed for rapidly simulating coronary flow, in which a machine learning model was embedded for accurately predicting stenosis flow resistance at a given flow from anatomical features of stenosis. Importantly, the LPM was personalized in both structures and parameters according to coronary geometries from computed tomography angiography and physiological measurements such as blood pressure and cardiac output for personalized simulations of coronary pressure and flow. Coronary lesions with invasive FFR ≤ 0.80 were defined as hemodynamically significant. RESULTS A total of 91 patients (93 lesions) who underwent invasive FFR were involved in FFR derived from machine learning (FFRML) calculation. Of the 93 lesions, 27 lesions (29.0%) showed lesion-specific ischemia. The average time of FFRML simulation was about 10 min. On a per-vessel basis, the FFRML and FFR were significantly correlated (r = 0.86, p < 0.001). The diagnostic accuracy, sensitivity, specificity, positive predictive value and negative predictive value were 91.4%, 92.6%, 90.9%, 80.6% and 96.8%, respectively. The area under the receiver-operating characteristic curve of FFRML was 0.984. CONCLUSION In this selected cohort of patients, the FFRML improves the computational efficiency and ensures the accuracy. The favorable performance of FFRML approach greatly facilitates its potential application in detecting hemodynamically significant coronary stenosis in future routine clinical practice.
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Affiliation(s)
- Yili Feng
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Ruisen Fu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Hao Sun
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Xue Wang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Department of Equipment and Materials, Tianjin First Central Hospital, Tianjin, China
| | - Yang Yang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Chuanqi Wen
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Yaodong Hao
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Yutong Sun
- Department of Cardiology, Peking University People's Hospital, Beijing, China
| | - Bao Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Na Li
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai'an, Shandong 271016, China
| | - Haisheng Yang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
| | - Quansheng Feng
- Department of Cardiology, The First People's Hospital of Guangshui, Hubei 432700, China
| | - Jian Liu
- Department of Cardiology, Peking University People's Hospital, Beijing, China
| | - Zhuo Liu
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Liyuan Zhang
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
| | - Youjun Liu
- Department of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
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Xu S, Wang F, Mai P, Peng Y, Shu X, Nie R, Zhang H. Mechanism Analysis of Vascular Calcification Based on Fluid Dynamics. Diagnostics (Basel) 2023; 13:2632. [PMID: 37627891 PMCID: PMC10453151 DOI: 10.3390/diagnostics13162632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Vascular calcification is the abnormal deposition of calcium phosphate complexes in blood vessels, which is regarded as the pathological basis of multiple cardiovascular diseases. The flowing blood exerts a frictional force called shear stress on the vascular wall. Blood vessels have different hydrodynamic properties due to discrepancies in geometric and mechanical properties. The disturbance of the blood flow in the bending area and the branch point of the arterial tree produces a shear stress lower than the physiological magnitude of the laminar shear stress, which can induce the occurrence of vascular calcification. Endothelial cells sense the fluid dynamics of blood and transmit electrical and chemical signals to the full-thickness of blood vessels. Through crosstalk with endothelial cells, smooth muscle cells trigger osteogenic transformation, involved in mediating vascular intima and media calcification. In addition, based on the detection of fluid dynamics parameters, emerging imaging technologies such as 4D Flow MRI and computational fluid dynamics have greatly improved the early diagnosis ability of cardiovascular diseases, showing extremely high clinical application prospects.
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Affiliation(s)
- Shuwan Xu
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Feng Wang
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Peibiao Mai
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
| | - Yanren Peng
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Xiaorong Shu
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Ruqiong Nie
- Department of Cardiology, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510120, China; (Y.P.); (X.S.)
| | - Huanji Zhang
- Department of Cardiology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen 518033, China; (S.X.); (F.W.); (P.M.)
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Yevtushenko P, Goubergrits L, Franke B, Kuehne T, Schafstedde M. Modelling blood flow in patients with heart valve disease using deep learning: A computationally efficient method to expand diagnostic capabilities in clinical routine. Front Cardiovasc Med 2023; 10:1136935. [PMID: 36937926 PMCID: PMC10020717 DOI: 10.3389/fcvm.2023.1136935] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 02/13/2023] [Indexed: 03/06/2023] Open
Abstract
Introduction The computational modelling of blood flow is known to provide vital hemodynamic parameters for diagnosis and treatment-support for patients with valvular heart disease. However, most diagnosis/treatment-support solutions based on flow modelling proposed utilize time- and resource-intensive computational fluid dynamics (CFD) and are therefore difficult to implement into clinical practice. In contrast, deep learning (DL) algorithms provide results quickly with little need for computational power. Thus, modelling blood flow with DL instead of CFD may substantially enhances the usability of flow modelling-based diagnosis/treatment support in clinical routine. In this study, we propose a DL-based approach to compute pressure and wall-shear-stress (WSS) in the aorta and aortic valve of patients with aortic stenosis (AS). Methods A total of 103 individual surface models of the aorta and aortic valve were constructed from computed tomography data of AS patients. Based on these surface models, a total of 267 patient-specific, steady-state CFD simulations of aortic flow under various flow rates were performed. Using this simulation data, an artificial neural network (ANN) was trained to compute spatially resolved pressure and WSS using a centerline-based representation. An unseen test subset of 23 cases was used to compare both methods. Results ANN and CFD-based computations agreed well with a median relative difference between both methods of 6.0% for pressure and 4.9% for wall-shear-stress. Demonstrating the ability of DL to compute clinically relevant hemodynamic parameters for AS patients, this work presents a possible solution to facilitate the introduction of modelling-based treatment support into clinical practice.
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Affiliation(s)
- Pavlo Yevtushenko
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Leonid Goubergrits
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
| | - Benedikt Franke
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Titus Kuehne
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Marie Schafstedde
- Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany
- Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
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8
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Wang S, Wu D, Li G, Zhang Z, Xiao W, Li R, Qiao A, Jin L, Liu H. Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments. Front Physiol 2023; 13:1094743. [PMID: 36703930 PMCID: PMC9872942 DOI: 10.3389/fphys.2022.1094743] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/09/2022] [Indexed: 01/11/2023] Open
Abstract
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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Affiliation(s)
- Sirui Wang
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Dandan Wu
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Gaoyang Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Zhiyuan Zhang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Weizhong Xiao
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruichen Li
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Aike Qiao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Long Jin
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,*Correspondence: Hao Liu, ; Long Jin,
| | - Hao Liu
- Graduate School of Engineering, Chiba University, Chiba, Japan,*Correspondence: Hao Liu, ; Long Jin,
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9
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Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond. Ann Biomed Eng 2022; 50:615-627. [PMID: 35445297 DOI: 10.1007/s10439-022-02967-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.
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Yevtushenko P, Goubergrits L, Gundelwein L, Setio A, Ramm H, Lamecker H, Heimann T, Meyer A, Kuehne T, Schafstedde M. Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modelling of Hemodynamics. IEEE J Biomed Health Inform 2021; 26:1815-1825. [PMID: 34591773 DOI: 10.1109/jbhi.2021.3116764] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clinical outcome is widely recognized, these methods require considerable computational resources since they are mostly based on conventional numerical methods such as computational fluid dynamics (CFD). As an alternative to the numerical methods, we propose a machine learning (ML) based approach to calculate patient-specific hemodynamic parameters. Compared to CFD based methods, our approach holds the benefit of being able to calculate a patient-specific hemodynamic outcome instantly with little need for computational power. In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.e. locally averaged) form. Considering the complex relation between vessels shape and hemodynamics on the one hand and the limited availability of suitable clinical data on the other, a sufficient accuracy of the ANN may however not be achieved with available data only. Another key aspect of this study is therefore the successful augmentation of available clinical data. Using a statistical shape model, additional training data was generated which substantially increased the ANNs accuracy, showcasing the ability of ML based methods to perform in-silico modelling tasks previously requiring resource intensive CFD simulations.
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