Literature DB >> 23428830

Automatic and efficient contrast-based 2-D/3-D fusion for trans-catheter aortic valve implantation (TAVI).

Rui Liao1, Shun Miao, Yefeng Zheng.   

Abstract

Trans-catheter aortic valve implantation (TAVI) is a new breakthrough in the field of minimally invasive surgery applied on high-risk patients with aortic valve defects. 2-D X-ray angiographic and fluoroscopic images are typically used to guide TAVI procedures, for which contrast agent needs to be injected from time to time in order to make the anatomy of the aortic root visible under X-ray. Advanced visualization and guidance technology involving patient-specific 3-D models of the aorta can greatly facilitate the relatively complex TAVI procedures by providing a more realistic anatomy of the aortic root and more accurate C-Arm angulation. In this paper, a fully automatic and efficient system for contrast-based 2-D/3-D fusion for TAVI is presented. Contrast agent injection into the aortic root is automatically detected based on histogram analysis and a likelihood ratio test on the X-ray images. A hybrid method is then applied for contrast-based 2-D/3-D registration between the 3-D model and the detected angiographic frame. By integrating the information of aorta segmentation and aortic landmark detection into intensity-based registration, the proposed method combines the merits of intensity-based registration and feature/landmark-based registration. Experiments on 34 clinical data sets from TAVI patients achieve 100% correct detection on the contrast-enhanced frame, and a mean registration error of 0.66±0.47mm for 2-D/3-D registration. The proposed method is furthermore highly efficient with an average processing time of 2.5s after the most contrast-enhanced frame is available, demonstrating the efficacy of the proposed method to be adopted in a clinical setup.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 23428830     DOI: 10.1016/j.compmedimag.2013.01.008

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  2 in total

1.  Registration of vascular structures using a hybrid mixture model.

Authors:  Siming Bayer; Zhiwei Zhai; Maddalena Strumia; Xiaoguang Tong; Ying Gao; Marius Staring; Berend Stoel; Rebecca Fahrig; Arya Nabavi; Andreas Maier; Nishant Ravikumar
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-06-07       Impact factor: 2.924

2.  An augmented reality system for image guidance of transcatheter procedures for structural heart disease.

Authors:  Jun Liu; Subhi J Al'Aref; Gurpreet Singh; Alexandre Caprio; Amir Ali Amiri Moghadam; Sun-Joo Jang; S Chiu Wong; James K Min; Simon Dunham; Bobak Mosadegh
Journal:  PLoS One       Date:  2019-07-01       Impact factor: 3.240

  2 in total

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