A physics‐informed deep learning framework for dynamic susceptibility contrast perfusion MRI
PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy.
Authors
- Heinz‐Peter Schlemmer (ORCID: https://orcid.org/0000-0002-9291-0954)
- Felix Tobias Kurz (ORCID: https://orcid.org/0000-0001-7616-7147)
- Martin Bendszus (ORCID: https://orcid.org/0000-0002-9094-6769)
- Christian H. Ziener (ORCID: https://orcid.org/0000-0001-8854-7358)
- Wolfgang Wick (ORCID: https://orcid.org/0000-0002-6171-634X)
- Anja Hohmann (ORCID: https://orcid.org/0000-0002-4166-5303)
- Daniel Paech (ORCID: https://orcid.org/0000-0001-5755-6833)
- Lukas T. Rotkopf (ORCID: https://orcid.org/0000-0001-9862-218X)
- Nikolaus von Knebel‐Doeberitz
- Sabine Wolf
Institutions
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- University Hospital Heidelberg (DE)
- University Hospital of Geneva (CH)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-09-20
- DOI
- https://doi.org/10.1002/mp.17415
- Citations
- 6
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.43