Radiomics-based aortic flow profile characterization with 4D phase-contrast MRI
4D PC MRI of the aorta has become a routinely available examination and a multitude of single parameters has been suggested for the quantitative assessment of relevant flow features for clinical studies and diagnosis. However, the clinically applicable assessment of complex flow patterns is still ch...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
03 April 2023
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| In: |
Frontiers in Cardiovascular Medicine
Year: 2023, Volume: 10, Pages: 1-12 |
| ISSN: | 2297-055X |
| DOI: | 10.3389/fcvm.2023.1102502 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3389/fcvm.2023.1102502 Verlag, kostenfrei, Volltext: https://www.frontiersin.org/articles/10.3389/fcvm.2023.1102502 
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| Author Notes: | Markus Huellebrand, Lina Jarmatz, Chiara Manini, Ann Laube, Matthias Ivantsits, Jeanette Schulz-Menger, Sarah Nordmeyer, Andreas Harloff, Jochen Hansmann, Sebastian Kelle and Anja Hennemuth |
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| 245 | 1 | 0 | |a Radiomics-based aortic flow profile characterization with 4D phase-contrast MRI |c Markus Huellebrand, Lina Jarmatz, Chiara Manini, Ann Laube, Matthias Ivantsits, Jeanette Schulz-Menger, Sarah Nordmeyer, Andreas Harloff, Jochen Hansmann, Sebastian Kelle and Anja Hennemuth |
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| 520 | |a 4D PC MRI of the aorta has become a routinely available examination and a multitude of single parameters has been suggested for the quantitative assessment of relevant flow features for clinical studies and diagnosis. However, the clinically applicable assessment of complex flow patterns is still challenging. We present a concept for the application of radiomics for the quantitative characterization of flow patterns in the aorta. To this end we derive cross-sectional scalar parameter maps, related to parameters suggested in literature such as throughflow, flow direction, vorticity and normalized helicity. Derived radiomics features are selected with regard to their inter-scanner and inter-observer reproducibility as well as their performance in the differentiation of gender-, age- and disease-related flow properties. This feature subset was used to calculate radiomics signatures of user-selected types of flow profiles. In future work, such signatures could be applied for quantitative flow assessment in clinical studies or disease phenotyping. | ||
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