TY - JOUR
T1 - MRI-based two-way fluid–structure interaction simulation for discriminating symptomatic carotid atherosclerosis
AU - Fu, Jingyu
AU - Li, Lu
AU - Guo, Junwei
AU - Zhang, Xuehuan
AU - Zhu, Chaoshuai
AU - Chen, Duanduan
AU - Yu, Wei
N1 - Publisher Copyright:
Copyright © 2026. Published by Elsevier B.V.
PY - 2026/8/1
Y1 - 2026/8/1
N2 - Objective To evaluate the additive value of two-way fluid–structure interaction (twFSI)-derived biomechanical metrics for artery-level discrimination of symptomatic carotid disease. Methods This single-center retrospective study included 97 patients (125 carotid arteries) who underwent high-resolution vessel wall imaging (HR-VWI). Three-dimensional lumen, vessel wall, and plaque were reconstructed, followed by twFSI simulations to derive hemodynamic and structural indices. Slice-wise morphological descriptors and twFSI-derived biomechanical candidate features were aggregated to the artery level using an attention-based multiple-instance learning framework. Performance was assessed with patient-wise five-fold cross-validation using area under the receiver operating characteristic curve (AUC), accuracy, and macro-averaged F1 score (macro-F1). Results Symptomatic arteries tended to show lower shear-related exposure and a more disturbed low-shear environment, whereas solid-domain descriptors showed more heterogeneous between-group behavior. A clinical-morphology model (CM) achieved an AUC of 0.716. Incorporating twFSI-derived biomechanical features (CM-BM) improved discrimination (AUC = 0.821), with accuracy increasing from 0.668 to 0.710 and macro-F1 from 0.720 to 0.761. The final CM-BM model retained a concise set of morphological and biomechanical descriptors, including plaque type, plaque angle range, maximum plaque area, maximum total deformation, maximum normalized WSS, and maximum ECAP. Conclusions In this single-center cohort, twFSI-derived biomechanical metrics provided incremental discriminative value beyond clinical and morphological variables alone. Attention-based MIL further offered a slice-weighting mechanism that may aid interpretability at the artery level. These findings support the potential of combined structural-dynamic phenotyping for symptom-oriented carotid assessment, while warranting prospective multicenter external validation.
AB - Objective To evaluate the additive value of two-way fluid–structure interaction (twFSI)-derived biomechanical metrics for artery-level discrimination of symptomatic carotid disease. Methods This single-center retrospective study included 97 patients (125 carotid arteries) who underwent high-resolution vessel wall imaging (HR-VWI). Three-dimensional lumen, vessel wall, and plaque were reconstructed, followed by twFSI simulations to derive hemodynamic and structural indices. Slice-wise morphological descriptors and twFSI-derived biomechanical candidate features were aggregated to the artery level using an attention-based multiple-instance learning framework. Performance was assessed with patient-wise five-fold cross-validation using area under the receiver operating characteristic curve (AUC), accuracy, and macro-averaged F1 score (macro-F1). Results Symptomatic arteries tended to show lower shear-related exposure and a more disturbed low-shear environment, whereas solid-domain descriptors showed more heterogeneous between-group behavior. A clinical-morphology model (CM) achieved an AUC of 0.716. Incorporating twFSI-derived biomechanical features (CM-BM) improved discrimination (AUC = 0.821), with accuracy increasing from 0.668 to 0.710 and macro-F1 from 0.720 to 0.761. The final CM-BM model retained a concise set of morphological and biomechanical descriptors, including plaque type, plaque angle range, maximum plaque area, maximum total deformation, maximum normalized WSS, and maximum ECAP. Conclusions In this single-center cohort, twFSI-derived biomechanical metrics provided incremental discriminative value beyond clinical and morphological variables alone. Attention-based MIL further offered a slice-weighting mechanism that may aid interpretability at the artery level. These findings support the potential of combined structural-dynamic phenotyping for symptom-oriented carotid assessment, while warranting prospective multicenter external validation.
KW - Atherosclerosis
KW - Biomechanics
KW - Carotid
KW - Fluid-structure interaction
KW - Multiple-instance learning
UR - https://www.scopus.com/pages/publications/105036472823
U2 - 10.1016/j.cmpb.2026.109370
DO - 10.1016/j.cmpb.2026.109370
M3 - Article
AN - SCOPUS:105036472823
SN - 0169-2607
VL - 282
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 109370
ER -