Authors: Avisha Kumar, Xuzhe Zhi, Zan Ahmad, Minglang Yin, Amir Manbachi
Abstract: Focused ultrasound (FUS) therapy is a promising tool for optimally targeted
treatment of spinal cord injuries (SCI), offering submillimeter precision to
enhance blood flow at injury sites while minimizing impact on surrounding
tissues. However, its efficacy is highly sensitive to the placement of the
ultrasound source, as the spinal cord’s complex geometry and acoustic
heterogeneity distort and attenuate the FUS signal. Current approaches rely on
computer simulations to solve the governing wave propagation equations and
compute patient-specific pressure maps using ultrasound images of the spinal
cord anatomy. While accurate, these high-fidelity simulations are
computationally intensive, taking up to hours to complete parameter sweeps,
which is impractical for real-time surgical decision-making. To address this
bottleneck, we propose a convolutional deep operator network (DeepONet) to
rapidly predict FUS pressure fields in patient spinal cords. Unlike
conventional neural networks, DeepONets are well equipped to approximate the
solution operator of the parametric partial differential equations (PDEs) that
govern the behavior of FUS waves with varying initial and boundary conditions
(i.e., new transducer locations or spinal cord geometries) without requiring
extensive simulations. Trained on simulated pressure maps across diverse
patient anatomies, this surrogate model achieves real-time predictions with
only a 2% loss on the test set, significantly accelerating the modeling of
nonlinear physical systems in heterogeneous domains. By facilitating rapid
parameter sweeps in surgical settings, this work provides a crucial step toward
precise and individualized solutions in neurosurgical treatments.
Source: http://arxiv.org/abs/2412.16118v1
