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. 2020 Nov 3;10:18894. doi: 10.1038/s41598-020-75689-z

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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(Upper panel) Visual illustration of the study design. Cardiac MRI of 20 participants with aortic coarctation was acquired during rest and moderate exercise. An absolute increase in heart rate of 50 bpm was targeted during exercise. Using the MRI images, the participant-specific aortic geometry was reconstructed. Using computational fluid dynamics, the transstenotic pressure gradient was calculated during rest and exercise. (Lower panel) Visualization of the image data used for segmentation. The participant-specific anatomy of the aorta was segmented from 3D SSFP (steady-state free precession) images (A,C). If the respective participant was treated using a stent, additional image information from black blood MR sequences (D) was used to improve the segmentation of the stented region of the aorta. An example from only 3D SSFP images is shown in panel (B), whereas a combined segmentation of a previously stented patient is shown in panel (E).