Nathan A Wood1, David Schwartzman2, Michael J Passineau3, Robert J Moraca4, Marco A Zenati5, Cameron N Riviere1. 1. The Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. 2. Cardiovascular Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. 3. Gene Therapy Program, Allegheny Health Network, Pittsburgh, Pennsylvania, USA. 4. Cardiovascular Institute, Allegheny General Hospital, Pittsburgh, Pennsylvania, USA. 5. BHS Department of Cardiothoracic Surgery, Harvard Medical School, West Roxbury, Massachusetts, USA.
Abstract
BACKGROUND: Organ-mounted robots address the problem of beating-heart surgery by adhering to the heart, passively providing a platform that approaches zero relative motion. Because of the quasi-periodic deformation of the heart due to heartbeat and respiration, registration must address not only spatial registration but also temporal registration. METHODS: Motion data were collected in the porcine model in vivo (N = 6). Fourier series models of heart motion were developed. By comparing registrations generated using an iterative closest-point approach at different phases of respiration, the phase corresponding to minimum registration distance is identified. RESULTS: The spatiotemporal registration technique presented here reduces registration error by an average of 4.2 mm over the 6 trials, in comparison with a more simplistic static registration that merely averages out the physiological motion. CONCLUSIONS: An empirical metric for spatiotemporal registration of organ-mounted robots is defined and demonstrated using data from animal models in vivo.
BACKGROUND: Organ-mounted robots address the problem of beating-heart surgery by adhering to the heart, passively providing a platform that approaches zero relative motion. Because of the quasi-periodic deformation of the heart due to heartbeat and respiration, registration must address not only spatial registration but also temporal registration. METHODS: Motion data were collected in the porcine model in vivo (N = 6). Fourier series models of heart motion were developed. By comparing registrations generated using an iterative closest-point approach at different phases of respiration, the phase corresponding to minimum registration distance is identified. RESULTS: The spatiotemporal registration technique presented here reduces registration error by an average of 4.2 mm over the 6 trials, in comparison with a more simplistic static registration that merely averages out the physiological motion. CONCLUSIONS: An empirical metric for spatiotemporal registration of organ-mounted robots is defined and demonstrated using data from animal models in vivo.
Authors: Adam D Costanza; Macauley S Breault; Nathan A Wood; Michael J Passineau; Robert J Moraca; Cameron N Riviere Journal: IEEE Robot Autom Lett Date: 2016-02-15
Authors: Nathan A Wood; David Schwartzman; Michael J Passineau; M Scott Halbreiner; Robert J Moraca; Marco A Zenati; Cameron N Riviere Journal: Int J Med Robot Date: 2018-11-29 Impact factor: 2.547