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Proceedings Paper

Feature identification for image-guided transcatheter aortic valve implantation
Author(s): Pencilla Lang; Martin Rajchl; A. Jonathan McLeod; Michael W. Chu; Terry M. Peters
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Paper Abstract

Transcatheter aortic valve implantation (TAVI) is a less invasive alternative to open-heart surgery, and is critically dependent on imaging for accurate placement of the new valve. Augmented image-guidance for TAVI can be provided by registering together intra-operative transesophageal echo (TEE) ultrasound and a model derived from pre-operative CT. Automatic contour delineation on TEE images of the aortic root is required for real-time registration. This study develops an algorithm to automatically extract contours on simultaneous cross-plane short-axis and long-axis (XPlane) TEE views, and register these features to a 3D pre-operative model. A continuous max-flow approach is used to segment the aortic root, followed by analysis of curvature to select appropriate contours for use in registration. Results demonstrate a mean contour boundary distance error of 1.3 and 2.8mm for the short and long-axis views respectively, and a mean target registration error of 5.9mm. Real-time image guidance has the potential to increase accuracy and reduce complications in TAVI.

Paper Details

Date Published: 17 February 2012
PDF: 14 pages
Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83162X (17 February 2012); doi: 10.1117/12.911562
Show Author Affiliations
Pencilla Lang, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Martin Rajchl, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
A. Jonathan McLeod, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Michael W. Chu, The Univ. of Western Ontario (Canada)
Terry M. Peters, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 8316:
Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
David R. Holmes; Kenneth H. Wong, Editor(s)

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