Chapter title |
Towards subject-specific models of the dynamic heart for image-guided mitral valve surgery.
|
---|---|
Chapter number | 12 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2007
|
DOI | 10.1007/978-3-540-75759-7_12 |
Pubmed ID | |
Book ISBNs |
978-3-54-075758-0, 978-3-54-075759-7
|
Authors |
Cristian A. Linte, Marcin Wierzbicki, John Moore, Stephen H. Little, Gérard M. Guiraudon, Terry M. Peters, Linte, Cristian A., Wierzbicki, Marcin, Moore, John, Little, Stephen H., Guiraudon, Gérard M., Peters, Terry M. |
Abstract |
Surgeons need a robust interventional system capable of providing reliable, real-time information regarding the position and orientation of the surgical targets and tools to compensate for the lack of direct vision and to enhance manipulation of intracardiac targets during minimally-invasive, off-pump cardiac interventions. In this paper, we describe a novel method for creating dynamic, pre-operative, subject-specific cardiac models containing the surgical targets and surrounding anatomy, and how they are used to augment the intra-operative virtual environment for guidance of valvular interventions. The accuracy of these pre-operative models was established by comparing the target registration error between the mitral valve annulus characterized in the pre-operative images and their equivalent structures manually extracted from 3D US data. On average, the mitral valve annulus was extracted with a 3.1 mm error across all cardiac phases. In addition, we also propose a method for registering the pre-operative models into the intra-operative virtual environment. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 4% |
United States | 1 | 4% |
Germany | 1 | 4% |
Norway | 1 | 4% |
Unknown | 19 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 5 | 22% |
Student > Master | 5 | 22% |
Student > Ph. D. Student | 3 | 13% |
Other | 2 | 9% |
Professor | 2 | 9% |
Other | 3 | 13% |
Unknown | 3 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 35% |
Medicine and Dentistry | 6 | 26% |
Engineering | 3 | 13% |
Economics, Econometrics and Finance | 1 | 4% |
Physics and Astronomy | 1 | 4% |
Other | 1 | 4% |
Unknown | 3 | 13% |