Advantages of deep learning reconstruction algorithm in ultra-high-resolution CT for the diagnosis of pancreatic cystic neoplasm
DLR improved image quality and diagnostic performance by effectively reducing image noise and improving lesion conspicuity in the diagnosis of PCNs on UHR-CT. The DLR demonstrated greater diagnostic confidence for the assessment of imaging findings associated with PCNs.
Authors
- Arata Sakai (ORCID: https://orcid.org/0000-0003-1072-5243)
- Takamichi Murakami (ORCID: https://orcid.org/0000-0001-7782-548X)
- Hirochika Toyama
- Shinji Yabe (ORCID: https://orcid.org/0000-0002-3685-0264)
- Eisuke Ueshima (ORCID: https://orcid.org/0000-0001-8802-8793)
- Atsuhiro Masuda (ORCID: https://orcid.org/0000-0002-2138-1207)
- Takumi Fukumoto (ORCID: https://orcid.org/0000-0003-1592-4524)
- Takeru Yamaguchi (ORCID: https://orcid.org/0000-0002-4368-4882)
- Keitaro Sofue (ORCID: https://orcid.org/0000-0001-6132-1623)
- Y. UENO (ORCID: https://orcid.org/0000-0003-3090-6009)
- Hori Masatoshi
Institutions
- Kobe University (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1007/s11604-025-01804-7
- Citations
- 2
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 2.09