TY - CHAP
T1 - Vision-based intraoperative cone-beam CT stitching for non-overlapping volumes
AU - Fuerst, Bernhard
AU - Fotouhi, Javad
AU - Navab, Nassir
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-24553-9_48
DO - 10.1007/978-3-319-24553-9_48
M3 - Chapter
AN - SCOPUS:84947549537
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 387
EP - 395
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -