Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor.

Autor: Huang Y; EPSRC Cambridge Mathematics of Information in Healthcare, University of Cambridge, Cambridge, UK; Department of Radiology, University of Cambridge, Cambridge, UK., Wang S; Department of Radiology, University of Cambridge, Cambridge, UK; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Fudan University, Shanghai, China; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China., Luo T; Department of Engineering, University of Cambridge, Cambridge, UK., Du MH; Department of Radiology, University of Cambridge, Cambridge, UK; John Farman Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK., Sun C; Department of Radiology, University of Cambridge, Cambridge, UK., Sadat U; Cambridge Vascular Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK., Schönlieb CB; EPSRC Cambridge Mathematics of Information in Healthcare, University of Cambridge, Cambridge, UK; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK., Gillard JH; Department of Radiology, University of Cambridge, Cambridge, UK., Zhang J; Department of Radiology, Zhejiang Hospital, Zhejiang University School of Medicine, Hangzhou, China., Teng Z; Department of Radiology, University of Cambridge, Cambridge, UK; Nanjing Jingsan Medical Science and Technology, Ltd, Jiangsu, China. Electronic address: zt215@cam.ac.uk.
Jazyk: angličtina
Zdroj: Journal of biomechanics [J Biomech] 2022 Jan; Vol. 131, pp. 110910. Date of Electronic Publication: 2021 Dec 17.
DOI: 10.1016/j.jbiomech.2021.110910
Abstrakt: Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.
(Copyright © 2021 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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