Percept-Aware Surgical Planning for Visual Cortical Prostheses with Vascular Avoidance

TL;DR

This paper introduces a percept-aware surgical planning framework for cortical visual prostheses that optimizes electrode placement to improve perceptual outcomes while ensuring safety and vascular avoidance.

Contribution

It presents a novel differentiable optimization approach for electrode placement that considers perceptual, anatomical, and safety constraints in cortical prosthesis design.

Findings

Percept-aware optimization improves visual reconstruction fidelity.

Vascular safety constraints prevent margin violations.

Framework enables co-optimization of electrode configurations.

Abstract

Cortical visual prostheses aim to restore sight by electrically stimulating neurons in early visual cortex (V1). With the emergence of high-density and flexible neural interfaces, electrode placement within three-dimensional cortex has become a critical surgical planning problem. Existing strategies emphasize visual field coverage and anatomical heuristics but do not directly optimize predicted perceptual outcomes under safety constraints. We present a percept-aware framework for surgical planning of cortical visual prostheses that formulates electrode placement as a constrained optimization problem in anatomical space. Electrode coordinates are treated as learnable parameters and optimized end-to-end using a differentiable forward model of prosthetic vision. The objective minimizes task-level perceptual error while incorporating vascular avoidance and gray matter feasibility constraints. Evaluated on simulated reading and natural image tasks using realistic folded cortical geometry (FreeSurfer fsaverage), percept-aware optimization consistently improves reconstruction fidelity relative to coverage-based placement strategies. Importantly, vascular safety constraints eliminate margin violations while preserving perceptual performance. The framework further enables co-optimization of multi-electrode thread configurations under fixed insertion budgets. These results demonstrate how differentiable percept models can inform anatomically grounded, safety-aware computer-assisted planning for cortical neural interfaces and provide a foundation for optimizing next-generation visual prostheses.