T2‐weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study
Both conventional reconstruction and DLR provide acceptable image quality for T2W 3D FSE imaging of rectal cancer. DLR was clinically preferred over conventional reconstruction in almost all categories.
Authors
- Harmeet Kaur (ORCID: https://orcid.org/0000-0002-3461-5316)
- Ken‐Pin Hwang (ORCID: https://orcid.org/0000-0002-8196-3794)
- Xinzeng Wang (ORCID: https://orcid.org/0000-0002-0415-7246)
- Jong Bum Son (ORCID: https://orcid.org/0000-0003-2548-5895)
- Usama Salem (ORCID: https://orcid.org/0000-0002-1982-1429)
- Dan Nguyen
- Nir Stanietzky (ORCID: https://orcid.org/0000-0003-4142-3918)
- Peng Wei (ORCID: https://orcid.org/0009-0000-3514-6921)
- Sarah Palmquist (ORCID: https://orcid.org/0000-0002-4293-0199)
- Jingfei Ma (ORCID: https://orcid.org/0000-0003-2827-8891)
- Jia Sun (ORCID: https://orcid.org/0000-0002-1303-4803)
- Randy D. Ernst
Institutions
- The University of Texas MD Anderson Cancer Center (US)
- GE Global Research (United States) (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1002/acm2.70031
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Institutes of Health
- National Cancer Institute