Multiviewunet: A Deep Learning Surrogate for Wall Shear Stress Prediction in Aortic Aneurysmal Diseases DOI
Md. Ahasan Atick Faisal, Onur Mutlu, Sakib Mahmud

et al.

Published: Jan. 1, 2023

Computational Fluid Dynamics (CFD) analysis is widely used to simulate hemodynamics and investigate the biofluid mechanics of different tissue, whole organs, tissue–medical device interactions. However, CFD simulations are time-consuming computationally expensive; hence not readily available practical for patient-specific time-sensitive clinical applications prohibiting quick responses from clinicians. Disturbed known influence progression many cardiac conditions. Aorta main blood artery in body diseases this vessel very common. One such condition Abdominal Aortic Aneurysm (AAA), where abdominal aorta widens has risk rupture. Precise determination Wall Shear Stress (WSS) on aneurysmal wall essential assess rupture tissue. In study, we have proposed a Deep Learning (DL) surrogate estimating aortic WSS distribution. The DL model was created trained receive input output distributions directly, bypassing procedure. A novel way analyzing geometry-to-geometry problems also using domain transformation, which compatible with existing state-of-the-art Neural Networks (NN). framework, MultiViewUnet, 23 real 230 synthetic geometries. algorithm predicted stress an average Normalized Mean Absolute Error (NMAE) 0.362%. We believe our will open up new dimensions precise levels important.

Language: Английский

Multiviewunet: A Deep Learning Surrogate for Wall Shear Stress Prediction in Aortic Aneurysmal Diseases DOI
Md. Ahasan Atick Faisal, Onur Mutlu, Sakib Mahmud

et al.

Published: Jan. 1, 2023

Computational Fluid Dynamics (CFD) analysis is widely used to simulate hemodynamics and investigate the biofluid mechanics of different tissue, whole organs, tissue–medical device interactions. However, CFD simulations are time-consuming computationally expensive; hence not readily available practical for patient-specific time-sensitive clinical applications prohibiting quick responses from clinicians. Disturbed known influence progression many cardiac conditions. Aorta main blood artery in body diseases this vessel very common. One such condition Abdominal Aortic Aneurysm (AAA), where abdominal aorta widens has risk rupture. Precise determination Wall Shear Stress (WSS) on aneurysmal wall essential assess rupture tissue. In study, we have proposed a Deep Learning (DL) surrogate estimating aortic WSS distribution. The DL model was created trained receive input output distributions directly, bypassing procedure. A novel way analyzing geometry-to-geometry problems also using domain transformation, which compatible with existing state-of-the-art Neural Networks (NN). framework, MultiViewUnet, 23 real 230 synthetic geometries. algorithm predicted stress an average Normalized Mean Absolute Error (NMAE) 0.362%. We believe our will open up new dimensions precise levels important.

Language: Английский

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