Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
363-06-010 ISMRM Abstract

Validation of deep-learning-based super-resolution for 4D-flow MRI haemodynamic quantification

Accepted
Yicheng Wang 1, Hernán Mella1,2,3, Julio Garcia4,5, Sergio Uribe1, Julio Sotelo1,6
1Department of Medical Imaging and Radiation Sciences, School of Primary Allied Health Care, Faculty of Medicine, Nursing and Health Sciences,, Monash University, Melbourne, Australia
2Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
3Center for Interdisciplinary Research in Biomedicine, Biotechnology and Well-Being (CID3B), Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
4Stephenson Cardiac Imaging Centre, University of Calgary, Calgary, Canada
5Department of Radiology, University of Calgary, Calgary, Canada
6Departamento de Informática, Universidad Técnica Federico Santa María, Valparaíso, Chile
Presenting Author: Yicheng Wang

Synopsis

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References

1. Ferdian E, Suinesiaputra A, Dubowitz DJ, et al. 4DFlowNet: super-resolution 4D flow MRI using deep learning and computational fluid dynamics. Front Phys. 2020; 8:138. DOI: https://doi.org/10.3389/fphy.2020.00138 [doi]
2. Montalba C, Urbina J, Sotelo J, et al. Variability of 4D flow parameters when subjected to changes in MRI acquisition parameters using a realistic thoracic aortic phantom. Magn Reson Med. 2018; 79(4):1882–1892. DOI: https://doi.org/10.1002/mrm.26834 [doi]
3. Sotelo J, Franco P, Guala A, et al. Fully three-dimensional hemodynamic characterization of altered blood flow in bicuspid aortic valve patients with respect to aortic dilatation. Front Cardiovasc Med. 2022; 9:885338. DOI: https://doi.org/10.3389/fcvm.2022.885338 [doi]

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