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Rezaeitaleshmahalleh M, Lyu Z, Mu N, Wang M, Zhang X, Rasmussen TE, McBane Ii RD, Jiang J. Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations. Ann Biomed Eng 2024:10.1007/s10439-024-03572-3. [PMID: 39020077 DOI: 10.1007/s10439-024-03572-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/27/2024] [Indexed: 07/19/2024]
Abstract
Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5 mm/yr] or slowly growing [< 5 mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value > 0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.
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Affiliation(s)
- Mostafa Rezaeitaleshmahalleh
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Zonghan Lyu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
| | - Nan Mu
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA
- Sichuan Normal University, Chengdu, Sichuan, China
| | - Min Wang
- Department of Management Science and Statistics, The University of Texas at San Antonio, San Antonino, TX, USA
| | - Xiaoming Zhang
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Todd E Rasmussen
- Department of Vascular Surgery, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Jingfeng Jiang
- Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA.
- Joint Center for Biocomputing and Digital Health, Health Research Institute, and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, MI, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
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Tang X, Wu C. A predictive surrogate model for hemodynamics and structural prediction in abdominal aorta for different physiological conditions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107931. [PMID: 37992570 DOI: 10.1016/j.cmpb.2023.107931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 11/24/2023]
Abstract
BACKGROUND AND OBJECTIVE This study investigates the application of a Predictive Surrogate Model (PSM) for the prediction of the fluid and solid variables in the abdominal aorta by integrating Proper Orthogonal Decomposition (POD) and Long Short-Term Memory (LSTM) techniques. METHODS The Fluid-Structure Interaction (FSI) solver, which serves as the Full-Order Model (FOM), can capture the blood hemodynamics and structural mechanics precisely for a variety of physiological states, namely the rest and exercise conditions. RESULTS Detailed analyses have been conducted on velocity components, pressure, Wall Shear Stress (WSS), and Oscillatory Shear Index (OSI) variables. Firstly, the reconstruction error has been derived based on a specific number of POD bases to assess the Reduced Order Model (ROM). Notably, the reconstruction error for velocity components in the rest condition is one order of magnitude higher than that in the exercise condition, yet both remained below 10%. This error for pressure is even more minimal, being less than 1%. CONCLUSIONS The PSM is evaluated against rest and exercise conditions, exhibiting promising results despite the inherent complexities of the physiological conditions. Despite the inherent complexities of phenomena in the aorta, the predictive model demonstrates consistent error magnitudes for velocity components and wall-related indices, while solid variables show slightly higher errors.
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Affiliation(s)
- Xuan Tang
- Department of Physical Education, Yunnan University, Kunming, Yunnan Province, 650000, China; Department of Physical Education, Jeonbuk National University, Jeonju, Jeollabuk, 54896, Korea
| | - ChaoJie Wu
- Department of Physical Education, Jeonbuk National University, Jeonju, Jeollabuk, 54896, Korea.
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Moretti S, Tauro F, Orrico M, Mangialardi N, Facci AL. Comparative Analysis of Patient-Specific Aortic Dissections through Computational Fluid Dynamics Suggests Increased Likelihood of Degeneration in Partially Thrombosed False Lumen. Bioengineering (Basel) 2023; 10:bioengineering10030316. [PMID: 36978707 PMCID: PMC10045026 DOI: 10.3390/bioengineering10030316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Aortic dissection is a life-threatening vascular disease associated with high rates of morbidity and mortality, especially in medically underserved communities. Understanding patients’ blood flow patterns is pivotal for informing evidence-based treatment as they greatly influence the disease outcome. The present study investigates the flow patterns in the false lumen of three aorta dissections (fully perfused, partially thrombosed, and fully thrombosed) in the chronic phase, and compares them to a healthy aorta. Three-dimensional geometries of aortic true and false lumens (TLs and FLs) are reconstructed through an ad hoc developed and minimally supervised image analysis procedure. Computational fluid dynamics (CFD) is performed through a finite volume unsteady Reynolds-averaged Navier–Stokes approach assuming rigid wall aortas, Newtonian and homogeneous fluid, and incompressible flow. In addition to flow kinematics, we focus on time-averaged wall shear stress and oscillatory shear index that are recognized risk factors for aneurysmal degeneration. Our analysis shows that partially thrombosed dissection is the most prone to false lumen degeneration. In all dissections, the arteries connected to the false lumen are generally poorly perfused. Further, both true and false lumens present higher turbulence levels than the healthy aorta, and critical stagnation points. Mesh sensitivity and a thorough comparison against literature data together support the reliability of the CFD methodology. Image-based CFD simulations are efficient tools to assess the possibility of aortic dissection to lead to aneurysmal degeneration, and provide new knowledge on the hemodynamic characteristics of dissected versus healthy aortas. Similar analyses should be routinely included in patient-specific hemodynamics investigations, to plan and design tailored therapeutic strategies, and to timely assess their effectiveness.
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Affiliation(s)
- Simona Moretti
- DEIM Department of Economics, Engineering, Society and Business Administration, University of Tuscia, Largo dell’Università, 01100 Viterbo, Italy
| | - Flavia Tauro
- DIBAF Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy
- Correspondence: ; Tel.: +39-0761-357355
| | - Matteo Orrico
- Vascular and Endovascular Surgery Unit, San Camillo Forlanini Hospital, Circonvallazione Gianicolense 87, 00149 Roma, Italy
| | - Nicola Mangialardi
- Vascular and Endovascular Surgery Unit, San Camillo Forlanini Hospital, Circonvallazione Gianicolense 87, 00149 Roma, Italy
| | - Andrea Luigi Facci
- DEIM Department of Economics, Engineering, Society and Business Administration, University of Tuscia, Largo dell’Università, 01100 Viterbo, Italy
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