A Similarity-Constrained Multi-way Gated Attention Network for Focused Ultrasound-induced Blood-brain Barrier Opening Evaluation

Abstract

Objective: The blood-brain barrier (BBB) poses a significant challenge for central nervous system drug delivery due to its selective permeability. Focused ultrasound (FUS) combined with bubble agents enables non-invasive, targeted BBB disruption. However, current methods for assessing efficacy and safety have limitations of high cost, low temporal resolution, or moderate predictive reliability, etc. Methods: This study proposes a novel gated attention-based model (GAB) for predicting BBB opening outcomes using time-domain acoustic signal clips. The GAB architecture incorporates an acoustic encoder to extract spectral and temporal features from short clips while modeling dependency between adjacent clips. A multi-way gated attention mechanism aggregates clip-level features, enhancing inter-class discriminability through adaptive selection. A task-specific loss function combining classification and similarity constraints further improves prediction by reducing redundancy in attention patterns. The outcomes of 174 FUS treatments were classified into three categories: BBB not opened, BBB opened without hemorrhage, and BBB opened with hemorrhage. Results: The GAB achieved superior performance in hemorrhage prediction (accuracy = 86.6 ± 4.9%, recall = 82.1 ± 10.9%, AUC = 0.894 ± 0.036, F1 score = 0.846 ± 0.062) compared to traditional cavitation dose-based methods and our previously developed frequency-domain deep learning models. Visualization of attention weights revealed that the model effectively distinguished broadband inertial cavitation signals (associated with hemorrhage) from ultra-harmonic stable cavitation signals (linked to safe BBB opening). Conclusion: The proposed method enhances temporal resolution, achieves superior predictive performance with interpretable cavitation features, and shows strong potential for real-time outcome prediction in FUS-mediated BBB disruption.