A Sequential 3D Thinning Algorithm and Its Medical Applications
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
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
Muliscale Vessel Enhancement Filtering
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
Fully Automatic 3D/2D Subtracted Angiography Registration
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
3D-2D projective registration of free-form curves and surfaces
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Liver registration for the follow-up of hepatic tumors
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Registration of 3d angiographic and x-ray images using sequential monte carlo sampling
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Segmentation-driven 2D-3D registration for abdominal catheter interventions
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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2D-3D registration for angiographic liver interventions is an unsolved problem mainly because of two reasons. First, a suitable protocol for Computed Tomography Angiography (CTA) to contrast liver arteries is not used in clinical practice. Second, an adequate registration algorithm which addresses the difficult task of aligning deformed vessel structures has not been developed yet. We address the first issue by introducing an angiographic CT scanning phase and thus create a strong link between radiologists and interventionalists. The scan visualizes arteries similar to the vasculature captured with an intraoperative C-arm acquiring Digitally Subtracted Angiograms (DSAs). Furthermore, we propose a registration algorithm using the new CT phase that aligns arterial structures in two steps: a) Initialization of one corresponding feature using vessel diameter information, b) optimization on three rotational and one translational parameter to register vessel structures that are represented as centerline graphs. We form a space of good features by iteratively creating new graphs from projected centerline images and by restricting the correspondence search only on branching points (the vertices) of the vessel tree. This algorithm shows good convergence and proves to be robust against deformation changes, which is demonstrated through studies on one phantom and three patients.