Pituitary neuroendocrine tumor: evaluation with super resolution deep learning reconstruction
SR-DLR tended to enhance inter-reader agreement in the evaluations of pituitary stalk deviation and significantly improved quality of pituitary MRI images compared to conventional ZIP images.
Authors
- Yuko Sato (ORCID: https://orcid.org/0000-0003-2927-1149)
- Yusuke Asari
- Shiori Amemiya (ORCID: https://orcid.org/0000-0002-2132-1642)
- Shigeru Kiryu (ORCID: https://orcid.org/0000-0003-1440-9483)
- Naoya Sakamoto (ORCID: https://orcid.org/0000-0003-2335-1359)
- Koichiro Yasaka (ORCID: https://orcid.org/0000-0002-0324-6562)
- Jun Kanzawa (ORCID: https://orcid.org/0009-0008-8203-8665)
- Akira Katayama (ORCID: https://orcid.org/0000-0001-9677-1421)
- Yuki Sonoda (ORCID: https://orcid.org/0009-0005-8622-2433)
- Yuichi Suzuki (ORCID: https://orcid.org/0000-0002-0955-9578)
- Osamu Abe
Institutions
- Tokyo Ohka Kogyo (Japan) (JP)
- The University of Tokyo (JP)
- International University of Health and Welfare (JP)
Publication Details
- Journal
- Neuroradiology
- Published
- 2025-10-21
- DOI
- https://doi.org/10.1007/s00234-025-03819-3
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00