Intraoperative Registration by Cross-Modal Inverse Neural Rendering

TL;DR

This paper introduces a novel cross-modal inverse neural rendering method for intraoperative 3D/2D registration in neurosurgery, leveraging disentangled neural representations to improve accuracy and clinical applicability.

Contribution

It proposes a new neural rendering approach that separates anatomical structure and appearance, enabling accurate intraoperative registration with improved performance over existing methods.

Findings

Outperforms state-of-the-art registration methods

Meets clinical standards for neurosurgical applications

Validated on retrospective patient data

Abstract

We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field's appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration. Code and additional resources can be found at https://maxfehrentz.github.io/style-ngp/.