Vision-based intraoperative cone-beam CT stitching for non-overlapping volumes

Bernhard Fuerst, Javad Fotouhi, Nassir Navab

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Cone-Beam Computed Tomography (CBCT) is one of the primary imaging modalities in radiation therapy, dentistry, and orthopedic interventions. While providing crucial intraoperative imaging, CBCT is bounded by its limited imaging volume, motivating the use of image stitching techniques. Current methods rely on overlapping volumes, leading to an excessive amount of radiation exposure, or on external tracking hardware, which may increase the setup complexity. We attach an optical camera to a CBCT enabled C-arm, and co-register the video and X-ray views. Our novel algorithm recovers the spatial alignment of non-overlapping CBCT volumes based on the observed optical views, as well as the laser projection provided by the X-ray system. First, we estimate the transformation between two volumes by automatic detection and matching of natural surface features during the patient motion. Then, we recover 3D information by reconstructing the projection of the positioning-laser onto an unknown curved surface, which enables the estimation of the unknown scale. We present a full evaluation of the methodology, by comparing vision- and registration-based stitching.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages387-395
Number of pages9
DOIs
StatePublished - Jan 1 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9349
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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