Virtual intraoperative CT (viCT): sequential anatomic updates for modeling tissue resection throughout endoscopic sinus surgery

Abstract

Abstract Purpose Incomplete dissection is a common cause of persistent disease and revision endoscopic sinus surgery (ESS) in chronic rhinosinusitis. Current image-guided surgery systems typically reference static preoperative CT (pCT) and do not model evolving resection boundaries. We present Virtual Intraoperative CT (viCT), a method for sequentially updating pCT throughout ESS using intraoperative 3D reconstructions from monocular endoscopic video to enable visualization of evolving anatomy in CT format. Methods Monocular endoscopic video is processed using a depth-supervised NeRF framework with virtual stereo synthesis to generate metrically scaled 3D reconstructions at multiple surgical intervals. Reconstructions undergo rigid, landmark-based registration in 3D Slicer guided by anatomical correspondences and are then voxelized into the pCT grid. viCT volumes were generated using a ray-based voxel occupancy comparison between pCT and reconstruction to delete outdated voxels and remap preserved anatomy and updated boundaries. Performance is evaluated in a cadaveric feasibility study of four specimens across four ESS stages using volumetric overlap (DSC and Jaccard) and surface metrics (HD95, Chamfer, MSD, and RMSD), alongside qualitative slice comparisons with ground-truth interval CT. Results viCT updates show high volumetric agreement and submillimeter mean global surface error. The Dice Similarity Coefficient was $$0.88 \pm 0.05$$ 0.88 ± 0.05 , and the Jaccard Index was $$0.79 \pm 0.07$$ 0.79 ± 0.07 . The 95th-percentile Hausdorff distance (HD95) was $$0.69 \pm 0.28$$ 0.69 ± 0.28 mm, Chamfer distance was $$0.09 \pm 0.05$$ 0.09 ± 0.05 mm, mean surface distance (MSD) was $$0.11 \pm 0.05$$ 0.11 ± 0.05 mm, and root-mean-square distance (RMSD) was $$0.32 \pm 0.10$$ 0.32 ± 0.10 mm. Conclusion viCT enables CT-format, sequential anatomic updating in an ESS setting without external tracking or additional intraoperative imaging. Future work will focus on fully automating registration, expanding validation in live cases, and optimizing runtime for intraoperative deployment