Collaborative tracking for MRI-guided robotic intervention on the beating heart

Authors:
Y. Zhou;E. Yeniaras;P. Tsiamyrtzis;N. Tsekos;I. Pavlidis
Affiliations:
Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX;Department of Statistics, Athens University of Economics, Athens, Greece;Department of Computer Science, University of Houston, Houston, TX;Department of Computer Science, University of Houston, Houston, TX
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

Citing 4
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Abstract

Magnetic Resonance Imaging (MRI)-guided robotic interventions for aortic valve repair promise to dramatically reduce time and cost of operations when compared to endoscopically guided (EG) procedures. A challenging issue is real-time and robust tracking of anatomical landmark points. The interventional tool should be constantly adjusted via a closed feedback control loop to avoid harming these points while valve repair is taking place in the beating heart. A Bayesian network of particle filter trackers proves capable to produce real-time, yet robust behavior. The algorithm is extremely flexible and general - more sophisticated behaviors can be produced by simply increasing the cardinality of the tracking network. Experimental results on 16 MRI cine sequences highlight the promise of the method.