Stop Holding Your Breath: CT-Informed Gaussian Splatting for Dynamic Bronchoscopy

2026-04-30

AI Summary

This work addresses the 5–20 mm discrepancy between preoperative CT and intraoperative bronchial anatomy caused by respiratory motion, a challenge exacerbated by conventional breath-hold protocols that are difficult to reproduce and disrupt surgical workflow. The authors propose a dynamic reconstruction method that requires neither breath-holding nor external sensors. It constructs a patient-specific respiratory deformation space from preoperative inspiratory–expiratory CT pairs, reduces it to a one-dimensional respiratory phase via registration, and integrates it into a mesh-anchored Gaussian splatting framework. A lightweight phase estimator infers the current respiratory state in real time from endoscopic RGB images. This approach uniquely combines patient-specific respiratory modeling with Gaussian splatting, achieving geometrically faithful reconstructions on the RESPIRE simulation platform. It accelerates training by over 20× and reduces target localization error to 1.22 mm—surpassing single-CT baselines and meeting the clinical accuracy requirement of 3 mm.

Abstract

Bronchoscopic navigation relies on registering endoscopic video to a preoperative CT scan, but respiratory motion deforms the airway by 5-20 mm, creating CT-to-body divergence that limits localization accuracy. In practice, this is mitigated through breath-hold protocols, which attempt to match the intraoperative anatomy to a static CT, but are difficult to reproduce and disrupt clinical workflow. We propose to eliminate the need for breath-hold protocols by leveraging patient-specific respiratory modeling. Paired inhale-exhale CT scans, already acquired for planning, implicitly define the patient-specific deformation space of the breathing airway. By registering these scans, we reduce respiratory motion to a single scalar breathing phase per frame, constraining all reconstructions to anatomically observed configurations. We embed this representation within a mesh-anchored Gaussian splatting framework, where a lightweight estimator infers breathing phase directly from endoscopic RGB, enabling continuous, deformation-aware reconstruction throughout the respiratory cycle without breath-holds or external sensing. To enable quantitative evaluation, we introduce RESPIRE, a physically grounded bronchoscopy simulation pipeline with per-frame ground truth for geometry, pose, breathing phase, and deformation. Experiments on RESPIRE show that our approach achieves geometrically faithful reconstruction, over 20x faster training, and 1.22 mm target localization accuracy (within the 3mm clinically relevant tolerances) outperforming unconstrained single-CT baselines. Please check out our website for additional visuals: https://asdunnbe.github.io/RESPIRE/

Problem

• respiratory motion
• bronchoscopic navigation
• CT-to-body divergence
• breath-hold protocols
• airway deformation

Innovation

• Gaussian Splatting
• Respiratory Motion Modeling
• Dynamic Bronchoscopy
• CT-to-Endoscopy Registration
• Breath-Hold Elimination