Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
This work addresses the need for more precise surgical navigation in sinus surgery, where current systems have accuracy issues, but it appears incremental as it builds on existing registration methods with anatomical constraints.
The authors tackled the problem of improving registration accuracy in functional endoscopic sinus surgery navigation systems, achieving robust performance with anatomically constrained video-CT registration that incorporates multiple video features and is tested on simulated and in-vivo data.
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.