Matching 3D MR angiography data and 2D X-ray angiograms
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Real-Time Registration of 3D Cerebral Vessels to X-ray Angiograms
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
3D/2D Registration via Skeletal Near Projective Invariance in Tubular Objects
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Statistical 3D Vessel Segmentation Using a Rician Distribution
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
Fusing Speed and Phase Information for Vascular Segmentation in Phase Contrast MR Angiograms
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Measuring and modeling soft tissue deformation for image guided interventions
IS4TM'03 Proceedings of the 2003 international conference on Surgery simulation and soft tissue modeling
2D fast vessel visualization using a vessel wall mask guiding fine vessel detection
Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
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We have compared the performance of six similarity measures for registration of three-dimensional (3D) magnetic resonance angiography (MRA) to two-dimensional (2D) x-ray angiography images of the cerebral vasculature. The accuracy and robustness of each measure was investigated using a ground truth registration of a neuro-vascular phantom which was obtained using fiducial markers, and using "gold-standard" registrations of four clinical data sets calculated using manual alignment by a neuro-radiologist. Of the six similarity measures, pattern intensity, gradient difference and gradient correlation performed consistently accurately and robustly for all data sets. Using these similarity measures, and for starting positions within 8掳 rotation, 3mm in-plane translation and 50mm out-of-plane translation from the gold-standard/ground-truth positions, we obtained a success rate of greater than 80% for the clinical data sets, whilst none of the phantom registrations failed. The root-mean-square (rms) target reprojection error was less than 1.3mm for the clinical data sets. The rms target reprojection error for the phantom images was less than 1mm when using the most accurate similarity measures.