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Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability.

, , , , , , , , , and . IEEE Trans. Medical Imaging, 40 (5): 1438-1449 (2021)

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Unsupervised Learning and Statistical Shape Modeling of the Morphometry and Hemodynamics of Coarctation of the Aorta., , , , , and . MICCAI (4), volume 12264 of Lecture Notes in Computer Science, page 776-785. Springer, (2020)Impact of patient-specific LVOT inflow profiles on aortic valve prosthesis and ascending aorta hemodynamics., , , , , , , and . J. Comput. Sci., (2018)Deep learning-based cardiovascular modelling (Deep Learning-basierte kardiovaskuläre Modellierung). TU Berlin, Germany, (2023)base-search.net (ftdepositonce:oai:depositonce.tu-berlin.de:11303/19482).Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics., , , , , , , , , and . IEEE J. Biomed. Health Informatics, 26 (4): 1815-1825 (2022)Hemodynamic in Aortic Coarctation Using MRI-Based Inflow Condition., , , , , and . STACOM, volume 8330 of Lecture Notes in Computer Science, page 65-73. Springer, (2013)Synthetic Database of Aortic Morphometry and Hemodynamics: Overcoming Medical Imaging Data Availability., , , , , , , , , and . IEEE Trans. Medical Imaging, 40 (5): 1438-1449 (2021)