Machine‐learning‐based prediction of respiratory flow and lung volume from real‐time cardiac MRI using MR‐compatible spirometry
Machine-learning-based prediction of respiratory flow and lung volume from cardiac RT-MR images after a short training phase with MRcS is feasible and can help to shorten the time with MRcS while providing accurate respiratory data during RT-MRI.
Authors
- Halima Malik (ORCID: https://orcid.org/0009-0007-2570-9904)
- Lena Maria Röwer (ORCID: https://orcid.org/0000-0002-5525-6383)
- Dirk Klee (ORCID: https://orcid.org/0000-0002-5969-564X)
- Tobias Uelwer
- Dirk Voit (ORCID: https://orcid.org/0000-0002-0058-2157)
- Frank Pillekamp (ORCID: https://orcid.org/0000-0003-1968-8944)
- Stefan Harmeling
- Jens Frahm (ORCID: https://orcid.org/0000-0002-8279-884X)
- Pablo Emilio Verde (ORCID: https://orcid.org/0000-0001-8625-7242)
- J. Hußmann (ORCID: https://orcid.org/0000-0001-9525-7826)
Institutions
- TU Dortmund University (DE)
- Düsseldorf University Hospital (DE)
- Max Planck Institute for the Study of Religious and Ethnic Diversity (DE)
- Max Planck Institute of Experimental Medicine (DE)
- Heinrich Heine University Düsseldorf (DE)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/mp.18019
- Citations
- 1
- Primary Topic
- Atomic and Subatomic Physics Research
- Type
- article
- Field-Weighted Citation Impact
- 1.25