MRI-based two-way fluid-structure interaction simulation for discriminating symptomatic carotid atherosclerosis

To evaluate the additive value of two-way fluid-structure interaction (twFSI)-derived biomechanical metrics for artery-level discrimination of symptomatic carotid disease.

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).

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.

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.