Real-Time Brain Biomechanics Prediction with Neural Operators: Toward Clinically Deployable Traumatic Brain Injury Models
🤖 AI Summary
Existing neural operators lack the capacity to model patient-specific, heterogeneous multimodal biomechanical data—such as MRI, magnetic resonance elastography (MRE) stiffness maps, and demographic features—required for real-time clinical assessment of traumatic brain injury (TBI).
Method: We present the first systematic validation of neural operators for clinically applicable brain biomechanics modeling. Our approach introduces a multigrid Fourier neural operator (MG-FNO) for high-fidelity 3D displacement field prediction, augmented by a factorized FNO for accelerated training and a DeepONet architecture optimized for edge deployment. The framework jointly encodes anatomical imaging, mechanical parameters, and clinical covariates.
Contribution/Results: MG-FNO achieves 94.3% spatial accuracy and MSE = 0.0023; DeepONet enables millisecond-scale inference (14.5 iterations/s), accelerating simulation by six orders of magnitude over conventional finite-element methods. All models rigorously preserve anatomical fidelity, enabling real-time TBI risk assessment and protective equipment optimization.
Application Category
📝 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$ imes$ faster than standard FNO. DeepONet offered the fastest inference (14.5 iterations/s) with a 7$ imes$ 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.
Problem
Research questions and friction points this paper is trying to address.
Develops multimodal neural operators for real-time traumatic brain injury modeling.
Fuses heterogeneous data to predict brain displacement fields rapidly.
Enables patient-specific TBI risk assessment with efficient computational speed.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Multimodal neural operators fuse heterogeneous data for TBI modeling
Field projection and branch decomposition enable efficient data fusion
Real-time inference reduces computation from hours to milliseconds
🔎 Similar Papers
No similar papers found.
