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Proceedings Paper

Accuracy assessment of fluoroscopy-transesophageal echocardiography registration
Author(s): Pencilla Lang; Petar Seslija; Daniel Bainbridge; Gerard M. Guiraudon; Doug L. Jones; Michael W. Chu; David W. Holdsworth; Terry M. Peters
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Paper Abstract

This study assesses the accuracy of a new transesophageal (TEE) ultrasound (US) fluoroscopy registration technique designed to guide percutaneous aortic valve replacement. In this minimally invasive procedure, a valve is inserted into the aortic annulus via a catheter. Navigation and positioning of the valve is guided primarily by intra-operative fluoroscopy. Poor anatomical visualization of the aortic root region can result in incorrect positioning, leading to heart valve embolization, obstruction of the coronary ostia and acute kidney injury. The use of TEE US images to augment intra-operative fluoroscopy provides significant improvements to image-guidance. Registration is achieved using an image-based TEE probe tracking technique and US calibration. TEE probe tracking is accomplished using a single-perspective pose estimation algorithm. Pose estimation from a single image allows registration to be achieved using only images collected in standard OR workflow. Accuracy of this registration technique is assessed using three models: a point target phantom, a cadaveric porcine heart with implanted fiducials, and in-vivo porcine images. Results demonstrate that registration can be achieved with an RMS error of less than 1.5mm, which is within the clinical accuracy requirements of 5mm. US-fluoroscopy registration based on single-perspective pose estimation demonstrates promise as a method for providing guidance to percutaneous aortic valve replacement procedures. Future work will focus on real-time implementation and a visualization system that can be used in the operating room.

Paper Details

Date Published: 1 March 2011
PDF: 10 pages
Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79641Y (1 March 2011); doi: 10.1117/12.877899
Show Author Affiliations
Pencilla Lang, Robarts Research Institute (Canada)
The Univ. of Western Ontario (Canada)
Petar Seslija, Robarts Research Institute (Canada)
Daniel Bainbridge, London Health Sciences Ctr. (Canada)
Gerard M. Guiraudon, Canadian Surgical Technologies and Advanced Robotics (Canada)
Doug L. Jones, The Univ. of Western Ontario (Canada)
Michael W. Chu, Canadian Surgical Technologies and Advanced Robotics (Canada)
The Univ. of Western Ontario (Canada)
David W. Holdsworth, Robarts Research Institute (Canada)
Terry M. Peters, Robarts Research Institute (Canada)


Published in SPIE Proceedings Vol. 7964:
Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; David R. Holmes, Editor(s)

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