The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
An image-based modeling framework for patient-specific computational hemodynamics
Open accessAbstract
We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine.
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Cited by
Oxley, T. J., Opie, N. L., John, S. E., Rind, G. S., Ronayne, S. M., Wheeler, T. L., Judy, J. W., McDonald, A. J., Dornom, A., Lovell, T. J. H., et al. (2016) · Nature Biotechnology · doi:10.1038/nbt.3428 · Cited by 339
Multiple aneurysms anatomy challenge 2018 (MATCH): phase I: segmentation
Berg, P., Voß, S., Saalfeld, S., Janiga, G., Bergersen, A. W., Valen-Sendstad, K., Bruening, J., Goubergrits, L., Spuler, A., Cancelliere, N. M., et al. (2018) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-018-00376-0 · Cited by 79
Corrado, C., Williams, S., Karim, R., Plank, G., O’Neill, M., & Niederer, S. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.04.005 · Cited by 50
Cito, S., Geers, A. J., Arroyo, M. P., Palero, V. R., Pallarés, J., Vernet, A., Blasco, J., San Román, L., Fu, W., Qiao, A., et al. (2015) · Annals of Biomedical Engineering · doi:10.1007/s10439-014-1082-9 · Cited by 20
Rodriguez-Florez, N., Borghi, A., Yauwan, D. D., Heuntinck, P., Bruse, J. L., Tenhagen, M., Göktekin, Ö. K., Angullia, F., Schievano, S., Dunaway, D. J., et al. (2020) · Journal of Craniofacial Surgery · doi:10.1097/scs.0000000000006863 · Cited by 12
Metadata
- Authors
- Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., & Steinman, D. A.
- Venue
- Medical & Biological Engineering & Computing
- Published
- 2008-11
- Volume
- 46
- Issue
- 11
- Pages
- 1097-112
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- OpenAlex
- W1967282533
- Cited by
- 850
- References
- 21
- FWCI
- 28.16
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Antiga, L., Piccinelli, M., Botti, L., Ene-Iordache, B., Remuzzi, A., & Steinman, D. A. (2008). An image-based modeling framework for patient-specific computational hemodynamics. Medical & Biological Engineering & Computing. https://doi.org/10.1007/s11517-008-0420-1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
