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 suscepti-ble to local minima, while neural networks trained on small datasets fail on new patients or require impractical land-mark 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 pre-operative CT. The CNN then initializes rapid intraoperative test-time optimization that uses the differentiable X-ray ren-derer to refine the solution. Our work further proposes several geometrically principled methods for sampling camera poses from SE (3), for sparse differentiable rendering, and for driving registration in the tangent space sc(3) with geodesic and multiscale locality-sensitive losses. DiffPose achieves sub-millimeter accuracy across surgical datasets at intraoperative speeds, improving upon existing unsupervised methods by an order of magnitude and even outperforming supervised baselines. Our implementation is at https://github.com/eigenvivek/DiffPose.