An Unbiased Detector of Curvilinear Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Model of a vascular C-arm for 3D augmented fluoroscopy in interventional radiology
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Monocular deformable model-to-image registration of vascular structures
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
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Despite rapid advances in interventional imaging, the navigation of a guide wire through abdominal vasculature remains, not only for novice radiologists, a difficult task. Since this navigation is mostly based on 2D fluoroscopic image sequences from one view, the process is slowed down significantly due to missing depth information and patient motion. We propose a novel approach for 3D dynamic roadmapping in deformable regions by predicting the location of the guide wire tip in a 3D vessel model from the tip's 2D location, respiratory motion analysis, and view geometry. In a first step, the method compensates for the apparent respiratory motion in 2D space before backprojecting the 2D guide wire tip into three dimensional space, using a given projection matrix. To countervail the error connected to the projection parameters and the motion compensation, as well as the ambiguity caused by vessel deformation, we establish a statistical framework, which computes a reliable estimate of the guide wire tip location within the 3D vessel model. With this 2D-to-3D transfer, the navigation can be performed from arbitrary viewing angles, disconnected from the static perspective view of the fluoroscopic sequence. Tests on a realistic breathing phantom and on synthetic data with a known ground truth clearly reveal the superiority of our approach compared to naïve methods for 3D roadmapping.The concepts and information presented in this paper are based on research and are not commercially available.