Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm

TL;DR

This paper introduces an anatomically constrained registration algorithm that combines video features with CT data to improve navigation accuracy in sinus surgery, demonstrating robustness and improved precision over existing methods.

Contribution

The paper proposes a novel video-CT registration algorithm with anatomical constraints that enhances robustness and accuracy in sinus navigation systems.

Findings

Robustness to outliers in registration process

Improved accuracy over existing navigation systems

Effective on simulated and in-vivo data

Abstract

Functional endoscopic sinus surgery (FESS) is a surgical procedure used to treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming the preferred choice of treatment due to its minimally invasive nature. However, due to the limited field of view of the endoscope, surgeons rely on navigation systems to guide them within the nasal cavity. State of the art navigation systems report registration accuracy of over 1mm, which is large compared to the size of the nasal airways. We present an anatomically constrained video-CT registration algorithm that incorporates multiple video features. Our algorithm is robust in the presence of outliers. We also test our algorithm on simulated and in-vivo data, and test its accuracy against degrading initializations.