Deep Learning–Based Generation of DSC MRI Parameter Maps Using Dynamic Contrast-Enhanced MRI Data
DSC-derived parameter maps could be synthesized by using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI by using a single dose of contrast agent.
Authors
- Linda Knutsson (ORCID: https://orcid.org/0000-0002-4263-113X)
- Doris D M Lin (ORCID: https://orcid.org/0000-0002-6001-2383)
- Paul Nyquist (ORCID: https://orcid.org/0000-0001-6078-3543)
- Peter van Zijl
- Haoyang Pei (ORCID: https://orcid.org/0009-0007-9258-673X)
- Xiang Xu (ORCID: https://orcid.org/0000-0001-8293-3776)
- Fang Liu (ORCID: https://orcid.org/0000-0003-4794-4319)
- Sebastian Lambrecht
- Yixuan Lyu (ORCID: https://orcid.org/0000-0001-7608-9150)
- Feng Li (ORCID: https://orcid.org/0000-0003-3169-6349)
Institutions
- Kennedy Krieger Institute (US)
- Harvard University (US)
- Johns Hopkins University (US)
- Lund University (SE)
- Johns Hopkins Medicine (US)
- Massachusetts General Hospital (US)
- Athinoula A. Martinos Center for Biomedical Imaging (US)
- New York University (US)
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
- Beihang University (CN)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.3174/ajnr.a8768
- Primary Topic
- Medical Imaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00