Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering.

ABSTRACT: Surgical decisions are informed by aligning rapid portable 2D intraoperative images (e.g. X-rays) to a high-fidelity 3D preoperative reference scan (e.g. CT). However, 2D/3D registration can often fail in practice: conventional optimization methods are prohibitively slow and susceptible to local minima, while neural networks trained on small datasets fail on new patients or require impractical landmark supervision. We present DiffPose, a self-supervised approach that leverages patient-specific simulation and differentiable physics-based rendering to achieve accurate 2D/3D registration without relying on manually labeled data. Preoperatively, a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT. The CNN then initializes rapid intraope

SUBMITTER: Gopalakrishnan V 

PROVIDER: S-EPMC12505627 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature