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Proceedings Paper

Estimating heart shift and morphological changes during minimally invasive cardiac interventions
Author(s): Cristian A. Linte; Mathew Carias; Daniel S. Cho; Danielle F. Pace; John Moore; Chris Wedlake; Daniel Bainbridge; Bob Kiaii; Terry M. Peters
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Paper Abstract

Image-guided interventions rely on the common assumption that pre-operative information can depict intraoperative morphology with sufficient accuracy. Nevertheless, in the context of minimally invasive cardiac therapy delivery, this assumption loses ground; the heart is a soft-tissue organ prone to changes induced during access to the heart and especially intracardiac targets. In addition to its clinical value for cardiac interventional guidance and assistance with the image- and model-to-patient registration, here we show how ultrasound imaging may be used to estimate changes in the heart position and morphology of structures of interest at different stages in the procedure. Using a magnetically tracked 2D transesophageal echocardiography transducer, we acquired in vivo images of the heart at different stages during the procedural workflow of common minimally invasive cardiac procedures, including robot-assisted coronary artery bypass grafting, mitral valve replacement/repair, or modelenhanced US-guided intracardiac interventions, all in the coordinate system of the tracking system. Anatomical features of interest (mitral and aortic valves) used to register the pre-operative anatomical models to the intraoperative coordinate frame were identified from each dataset. This information allowed us to identify the global position of the heart and also characterize the valvular structures at various peri-operative stages, in terms of their orientation, size, and geometry. Based on these results, we can estimate the differences between the preand intra-operative anatomical features, their effect on the model-to-subject registration, and also identify the need to update or optimize any pre-operative surgical plan to better suit the intra-operative procedure workflow.

Paper Details

Date Published: 27 February 2010
PDF: 11 pages
Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 762509 (27 February 2010); doi: 10.1117/12.845579
Show Author Affiliations
Cristian A. Linte, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Mathew Carias, The Univ. of Western Ontario (Canada)
Daniel S. Cho, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Danielle F. Pace, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
John Moore, Robarts Research Institute (Canada)
Chris Wedlake, Robarts Research Institute (Canada)
Daniel Bainbridge, The Univ. of Western Ontario (Canada)
Canadian Surgical Technologies and Advanced Robotics (Canada)
Bob Kiaii, The Univ. of Western Ontario (Canada)
Canadian Surgical Technologies and Advanced Robotics (Canada)
Terry M. Peters, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Canadian Surgical Technologies and Advanced Robotics (Canada)


Published in SPIE Proceedings Vol. 7625:
Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; Michael I. Miga, Editor(s)

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