Advancing 1.5T MR imaging: toward achieving 3T quality through deep learning super-resolution techniques
This approach presents TCGAN offers a cost-effective and widely accessible alternative for generating high-quality images without the need for expensive, high-field MRI scans and leads to inconsistencies and complicate data comparison and harmonization challenges across studies utilizing various scanners.
Authors
- Jeiran Choupan (ORCID: https://orcid.org/0000-0002-4760-7796)
- Bino Varghese (ORCID: https://orcid.org/0000-0002-0508-2899)
- Sk Rahatul Jannat
- Steve Cen
- Kirsten M. Lynch (ORCID: https://orcid.org/0000-0003-3976-5783)
- Nasim Sheikh‐Bahaei (ORCID: https://orcid.org/0000-0001-7029-7215)
- Gaurav Pandey (ORCID: https://orcid.org/0000-0002-4838-802X)
- Maryam Fotouhi (ORCID: https://orcid.org/0000-0003-4569-7094)
Institutions
- University of Southern California (US)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Frontiers in Human Neuroscience
- Published
- 2025-06-18
- DOI
- https://doi.org/10.3389/fnhum.2025.1532395
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- University of Southern California
- National Institutes of Health