Evaluating Second-Generation Deep Learning Technique for Noise Reduction in Myocardial T1-Mapping Magnetic Resonance Imaging
SR-DLR effectively reduces measurement variability while preserving T1 accuracy, enhancing the reliability of myocardial T1 mapping in both clinical and research settings.
Authors
- Tatsuya Hirano (ORCID: https://orcid.org/0000-0002-4219-6473)
- Shingo Kato (ORCID: https://orcid.org/0000-0002-7545-0669)
- T. Iwahashi
- Shungo Sawamura
- Daisuke Utsunomiya (ORCID: https://orcid.org/0000-0002-1723-1724)
- Taiga Kato (ORCID: https://orcid.org/0000-0001-5478-809X)
- Naofumi Yasuda
Institutions
- Yokohama City University Hospital (JP)
- Yokosuka Kyosai Hospital (JP)
- Yokohama City University (JP)
Publication Details
- Journal
- Diseases
- Published
- 2025-05-18
- DOI
- https://doi.org/10.3390/diseases13050157
- Citations
- 1
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 1.36