CT-US registration for guidance of transcatheter aortic valve implantation

Authors:
Pencilla Lang;Michael W. Chu;Daniel Bainbridge;Elvis C. S. Chen;Terry M. Peters
Affiliations:
Robarts Research Institute, The University of Western Ontario, Canada, Biomedical Engineering, The University of Western Ontario, Canada;Division of Cardiac Surgery, The University of Western Ontario, Canada;Division of Anesthesia, The University of Western Ontario, Canada;Robarts Research Institute, The University of Western Ontario, Canada;Robarts Research Institute, The University of Western Ontario, Canada, Biomedical Engineering, The University of Western Ontario, Canada
Venue:
AE-CAI'11 Proceedings of the 6th international conference on Augmented Environments for Computer-Assisted Interventions
Year:
2011

Citing 3
Cited 0

A Method for Registration of 3-D Shapes

IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
3D TEE Registration with X-Ray Fluoroscopy for Interventional Cardiac Applications

FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Three-dimensional ultrasound probe pose estimation from single-perspective x-rays for image-guided interventions

MIAR'10 Proceedings of the 5th international conference on Medical imaging and augmented reality

Quantified Score

Hi-index	0.00

Visualization

Abstract

Transcatheter aortic valve implantation is a minimally invasive procedure that delivers a replacement aortic valve to a beating heart using a valved stent in a catheter delivered either transapically or via the femoral artery. Current image guidance of this procedure relies on fluoroscopy, which provides poor visualization of cardiac anatomy. Combining a preoperative CT with intraoperative TEE images into a common environment would allow for improved image-guidance using only standard clinical images. A fast intraoperative registration of these images based on an iterative closest point registration is proposed and validated on human images. Three methods of extracting surface points from the TEE images are compared: 1) Biplane images outlined manually, 2) Multiple planes from the 3D TEE outlined manually, and 3) An automatically extracted surface from 3D TEE. Results from this initial validation demonstrate that a target registration error of less than 5mm may be achieved.