Lung cancer, the second most common cancer in the United States, is diagnosed and staged through the analysis of biopsy specimens, often obtained through transbronchial biopsy (TBB). However, accurate TBB for small nodules is hindered by CT body divergence – misalignment between pre-operative CT and intra-operative coordinate frames. We propose a comprehensive image guidance system, leveraging a stationary multi-source fluoroscopy imager together with deformable 3D/2D registration to solve for a motion field parameterized by implicit neural representations(INR) to jointly track pulmonary and bronchoscopic motion.
We evaluate our algorithm using a simulated imaging chain and a 4D-CT dataset, as well as on simulated TBB. Using 5 views, we demonstrate a median landmark TRE of 1.42 mm and a bronchoscope tip error of 2.8 mm. We demonstrate a promising 3D image guidance approach to improving the accuracy of trans-bronchial biopsy using a multi-view stationary imager and estimation of patient motion through deformable 3D/2D registration, which can be extended to track respiratory and bronchoscope motion over time for real-time navigation.
Purpose. Mobile C-arms capable of 2D fluoroscopy and 3D cone-beam CT (CBCT) are finding application in guidance of transbronchial lung biopsy, but unresolved deformable motion presents challenges to accurate target localization and guidance. We report the initial implementation of a method to resolve deformations via locally rigid / globally deformable 3D-2D registration for motion-compensated overlay of planning data in fluoroscopically-guided pulmonary interventions. Methods. The algorithm proceeds in 3 steps: (1) initialization by 3D-2D rigid registration of CBCT to fluoroscopy (driven by bone gradients); (2) local rigid 3D-2D registration of lung-thresholded CBCT to fluoroscopy within a region of interest (ROI) about each target location; and (3) aggregation of local rigid registrations to estimate global deformation. Several objective functions and optimizers were evaluated for soft-tissue target registration. Phantom studies were performed to determine operating parameters and assess performance with simulated lung deformation. Results. Soft-tissue thresholding and contrast enhancement improved target registration error (TRE) from 10.3 mm for conventional 3D-2D registration (driven primarily by rib gradients) to 3.8 mm using locally rigid 3D-2D registration in regions of interest about each target. The soft-tissue gradient orientation (GO) objective function was found to be superior to alternative similarity measures by de-emphasizing gradient magnitude (in favor of gradient orientation), permitting the algorithm to be better driven by soft-tissue edges. Conclusions. Registration driven by soft-tissue targets is achievable via a novel processing framework to de-emphasize non-target gradients. The proposed method could improve the accuracy of guidance in pulmonary interventions by updating target overlay in fluoroscopy.
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