title: Collaborative tracking for MRI-guided robotic intervention on the beating heart.
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.
authors.affiliation: Department of Computer Science, University of Houston, Houston, TX 77024, USA. yzhou9@uh.edu
authors.forename: Y
authors.initials: Y
authors.lastname: Zhou
authors.forename: E
authors.initials: E
authors.lastname: Yeniaras
authors.forename: P
authors.initials: P
authors.lastname: Tsiamyrtzis
authors.forename: N
authors.initials: N
authors.lastname: Tsekos
authors.forename: I
authors.initials: I
authors.lastname: Pavlidis
mesh_terms: D000465:Algorithms; D055424:Cardiac-Gated Imaging Techniques / Q000379:methods*; D013504:Cardiovascular Surgical Procedures / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*