Chapter title |
Physiologically based construction of optimized 3-D arterial tree models.
|
---|---|
Chapter number | 51 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2011
|
DOI | 10.1007/978-3-642-23623-5_51 |
Pubmed ID | |
Book ISBNs |
978-3-64-223622-8, 978-3-64-223623-5
|
Authors |
Matthias Schneider, Sven Hirsch, Bruno Weber, Gábor Székely, Schneider, Matthias, Hirsch, Sven, Weber, Bruno, Székely, Gábor |
Abstract |
We present an approach to generate 3-D arterial tree models based on physiological principles while at the same time certain morphological properties are enforced at construction time in order to build individual vascular models down to the capillary level. The driving force of our approach is an angiogenesis model incorporating case-specific information about the metabolic activity in the considered domain. Additionally, we enforce morphometrically confirmed bifurcation statistics of vascular networks. The proposed method is able to generate artificial, yet physiologically plausible, arterial tree models that match the metabolic demand of the embedding tissue and fulfill the enforced morphological properties at the same time. We demonstrate the plausibility of our method on synthetic data for different metabolic configurations and analyze physiological and morphological properties of the generated tree models. |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 2 | 6% |
Japan | 1 | 3% |
Switzerland | 1 | 3% |
Unknown | 31 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 10 | 29% |
Student > Ph. D. Student | 9 | 26% |
Professor > Associate Professor | 4 | 11% |
Student > Master | 4 | 11% |
Student > Bachelor | 1 | 3% |
Other | 5 | 14% |
Unknown | 2 | 6% |
Readers by discipline | Count | As % |
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Engineering | 13 | 37% |
Computer Science | 11 | 31% |
Medicine and Dentistry | 6 | 17% |
Agricultural and Biological Sciences | 2 | 6% |
Physics and Astronomy | 1 | 3% |
Other | 0 | 0% |
Unknown | 2 | 6% |