Chapter title |
Reconstruction of Coronary Trees from 3DRA Using a 3D+t Statistical Cardiac Prior
|
---|---|
Chapter number | 77 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
|
Published in |
Lecture notes in computer science, January 2014
|
DOI | 10.1007/978-3-319-10470-6_77 |
Pubmed ID | |
Book ISBNs |
978-3-31-910469-0, 978-3-31-910470-6
|
Authors |
Çimen, Serkan, Hoogendoorn, Corné, Morris, Paul D., Gunn, Julian, Frangi, Alejandro F., Medrano-Gracia, Pau, Ormiston, John, Webster, Mark, Beier, Susann, Ellis, Chris, Wang, Chunliang, Young, Alistair A., Cowan, Brett R., Serkan Çimen, Corné Hoogendoorn, Paul D. Morris, Julian Gunn, Alejandro F. Frangi |
Editors |
Golland, Polina, Howe, Robert, Hornegger, Joachim, Barillot, Christian, Hata, Nobuhiko |
Abstract |
A 3D+t description of the coronary tree is important for diagnosis of coronary artery disease and therapy planning. In this paper, we propose a method for finding 3D+t points on coronary artery tree given tracked 2D+t point locations in X-ray rotational angiography images. In order to cope with the ill-posedness of the problem, we use a bilinear model of ventricle as a spatio-temporal constraint on the nonrigid structure of the coronary artery. Based on an energy minimization formulation, we estimate i) bilinear model parameters, ii) global rigid transformation between model and X-ray coordinate systems, and iii) correspondences between 2D coronary artery points on X-ray images and 3D points on bilinear model. We validated the algorithm using a software coronary artery phantom. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 6% |
Unknown | 15 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 5 | 31% |
Student > Ph. D. Student | 5 | 31% |
Student > Bachelor | 1 | 6% |
Professor | 1 | 6% |
Researcher | 1 | 6% |
Other | 2 | 13% |
Unknown | 1 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 4 | 25% |
Medicine and Dentistry | 4 | 25% |
Computer Science | 3 | 19% |
Biochemistry, Genetics and Molecular Biology | 1 | 6% |
Mathematics | 1 | 6% |
Other | 0 | 0% |
Unknown | 3 | 19% |