CT-override: endoscopic updates to preoperative anatomical models during ablative surgery

Preoperative CT provides a detailed anatomical basis for navigation in sinus surgery, guiding surgeons through intricate nasal structures while providing 3D awareness of critical anatomy. However, this static representation lacks the capacity to adapt to intraoperative tissue ablation. Efforts toward endoscope-based navigation reconstruct anatomy from visual cues alone, often without incorporating the CT prior that anchors surgical context. We propose a method to ground both CT and endoscope in a unified representation that bridges pre- and intraoperative domains, enabling dynamic updates of the surgical scene.

We initialize the CT as a volumetric signed distance field (SDF), reformulating the update process with non-projective SDFs and accumulating camera-space observations with dense depth estimated from endoscopic video. These volumes are reconciled to produce intraoperative SDFs for updated surface mesh extraction.

We validated our method on three multi-step cadaveric sinus datasets with paired preoperative CT and intraoperative endoscopic video of a simulated sinus surgery. We extract intraoperative meshes at each surgical step and compare to ground-truth intraoperative CT. Our vision-guided update method demonstrates improved surface alignment of the intraoperative representations throughout surgical progression, achieving submillimeter geometric accuracy.

We show that integrating CT priors with intraoperative vision enables anatomically consistent updates to patient-specific models. This method provides a foundation for CT-consistent endoscopic reconstruction, and future work aimed at integrating camera localization toward a fully realized vision-guided surgical navigation system.