Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration.
2D/3D registration of patient vasculature from preinterventional computed tomography angiography (CTA) to interventional X-ray angiography is of interest to improve guidance in percutaneous coronary interventions. In this paper we present a novel feature based 2D/3D registration framework, that is based on probabilistic point correspondences, and show its usefulness on aligning 3D coronary artery centerlines derived from CTA images with their 2D projection derived from interventional X-ray angiography. The registration framework is an extension of the Gaussian mixture model (GMM) based point-set registration to the 2D/3D setting, with a modified distance metric. We also propose a way to incorporate orientation in the registration, and show its added value for artery registration on patient datasets as well as in simulation experiments. The oriented GMM registration achieved a median accuracy of 1.06 mm, with a convergence rate of 81% for nonrigid vessel centerline registration on 12 patient datasets, using a statistical shape model. The method thereby outperformed the iterative closest point algorithm, the GMM registration without orientation, and two recently published methods on 2D/3D coronary artery registration.
N
N
Baka
C T
CT
Metz
C J
CJ
Schultz
R-J
RJ
van Geuns
W J
WJ
Niessen
T
T
van Walsum
D000465:Algorithms; D017023:Coronary Angiography / Q000379:methods*; D003331:Coronary Vessels / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016011:Normal Distribution; D062645:Percutaneous Coronary Intervention; D025321:Surgery, Computer-Assisted