@article{Ostendorf2024,
title = {Synthetic surface mesh generation of aortic dissections using statistical shape modeling},
author = {Kai Ostendorf and Kathrin Bäumler and Domenico Mastrodicasa and Dominik Fleischmann and Bernhard Preim and Gabriel Mistelbauer},
url = {https://www.sciencedirect.com/science/article/pii/S009784932400205X},
doi = {https://doi.org/10.1016/j.cag.2024.104070},
issn = {0097-8493},
year = {2024},
date = {2024-10-01},
journal = {Computers & Graphics 2024},
volume = {124},
pages = {104070},
abstract = {Aortic dissection is a rare disease affecting the aortic wall layers splitting the aortic lumen into two flow channels: the true and false lumen. The rarity of the disease leads to a sparsity of available datasets resulting in a low amount of available training data for in-silico studies or the training of machine learning algorithms. To mitigate this issue, we use statistical shape modeling to create a database of Stanford type B dissection surface meshes. We account for the complex disease anatomy by modeling two separate flow channels in the aorta, the true and false lumen. Former approaches mainly modeled the aortic arch including its branches but not two separate flow channels inside the aorta. To our knowledge, our approach is the first to attempt generating synthetic aortic dissection surface meshes. For the statistical shape model, the aorta is parameterized using the centerlines of the respective lumen and the according ellipses describing the cross-section of the lumen while being aligned along the centerline employing rotation-minimizing frames. To evaluate our approach we introduce disease-specific quality criteria by investigating the torsion and twist of the true lumen.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Schroeder2024,
title = {Advanced visualization of aortic dissection anatomy and hemodynamics},
author = {Aaron Schroeder and Kai Ostendorf and Kathrin Bäumler and Domenico Mastrodicasa and Veit Sandfort and Dominik Fleischmann and Bernhard Preim and Gabriel Mistelbauer},
url = {https://www.sciencedirect.com/science/article/pii/S009784932400195X},
doi = {https://doi.org/10.1016/j.cag.2024.104060},
issn = {0097-8493},
year = {2024},
date = {2024-10-01},
journal = {Computers & Graphics 2024},
volume = {124},
pages = {104060},
abstract = {Aortic dissection is a life-threatening cardiovascular disease constituted by the delamination of the aortic wall. Due to the weakened structure of the false lumen, the aorta often dilates over time, which can – after certain diameter thresholds are reached – increase the risk of fatal aortic rupture. The identification of patients with a high risk of late adverse events is an ongoing clinical challenge, further complicated by the complex dissection anatomy and the wide variety among patients. Moreover, patient-specific risk stratification depends not only on morphological, but also on hemodynamic factors, which can be derived from computer simulations or 4D flow magnetic resonance imaging (MRI). However, comprehensible visualizations that depict the complex anatomical and functional information in a single view are yet to be developed. These visualization tools will assist clinical research and decision-making by facilitating a comprehensive understanding of the aortic state. For that purpose, we identified several visualization tasks and requirements in close collaboration with cardiovascular imaging scientists and radiologists. We displayed true and false lumen hemodynamics using pathlines as well as surface hemodynamics on the dissection flap and the inner vessel wall. Pathlines indicate antegrade and retrograde flow, blood flow through fenestrations, and branch vessel supply. Dissection-specific hemodynamic measures, such as interluminal pressure difference and flap compliance, provide further insight of the blood flow throughout the cardiac cycle. Finally, we evaluated our visualization techniques with cardiothoracic and vascular surgeons in two separate virtual sessions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}