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.

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

Tsiamyrtzis P.;
2010-01-01

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.
2010
Proceedings of the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
978-3-642-15710-3
978-3-642-15711-0
Cardiac-Gated Imaging Techniques; Cardiovascular Surgical Procedures; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging, Cine; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Surgery, Computer-Assisted; Algorithms
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1115227
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