Real-Time Brain Biomechanics Prediction with Neural Operators: Toward Clinically Deployable Traumatic Brain Injury Models

• URL: http://arxiv.org/abs/2510.03248v1
• Date: Fri, 26 Sep 2025 01:48:27 GMT
• Title: Real-Time Brain Biomechanics Prediction with Neural Operators: Toward Clinically Deployable Traumatic Brain Injury Models
• Authors: Anusha Agarwal, Dibakar Roy Sarkar, Somdatta Goswami,
• Abstract summary: Traumatic brain injury (TBI) remains a major public health concern, with over 69 million cases annually worldwide.<n>FE models offer high-fidelity predictions of brain deformation but are computationally expensive, requiring hours per simulation.<n>This study benchmarks state-of-the-art neural operator (NO) architectures for rapid, patient-specific prediction of brain displacement fields.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traumatic brain injury (TBI) remains a major public health concern, with over 69 million cases annually worldwide. Finite element (FE) models offer high-fidelity predictions of brain deformation but are computationally expensive, requiring hours per simulation and limiting their clinical utility for rapid decision-making. This study benchmarks state-of-the-art neural operator (NO) architectures for rapid, patient-specific prediction of brain displacement fields, aiming to enable real-time TBI modeling in clinical and translational settings. We formulated TBI modeling as an operator learning problem, mapping subject-specific anatomical MRI, magnetic resonance elastography (MRE) stiffness maps, and demographic features to full-field 3D brain displacement predictions. Four architectures - Fourier Neural Operator (FNO), Factorized FNO (F-FNO), Multi-Grid FNO (MG-FNO), and Deep Operator Network (DeepONet) were trained and evaluated on 249 MRE datasets across physiologically relevant frequencies (20 - 90 Hz). MG-FNO achieved the highest accuracy (MSE = 0.0023, 94.3\% spatial fidelity) and preserved fine-scale features, while F-FNO converged 2$\times$ faster than standard FNO. DeepONet offered the fastest inference (14.5 iterations/s) with a 7$\times$ computational speed-up over MG-FNO, suggesting utility for embedded or edge computing applications. All NOs reduced computation time from hours to milliseconds without sacrificing anatomical realism. NOs provide an efficient, resolution-invariant approach for predicting brain deformation, opening the door to real-time, patient-specific TBI risk assessment, clinical triage support, and optimization of protective equipment. These results highlight the potential for NO-based digital twins of the human brain, enabling scalable, on-demand biomechanical modeling in both clinical and population health contexts.

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