Dual-type deep learning-based image reconstruction for advanced denoising and super-resolution processing in head and neck T2-weighted imaging
Dual-type DL-based image reconstruction by an effective denoising and super-resolution process successfully provided high image quality in head and neck Fs-T2WI with a shortened scan time compared to the conventional imaging method.
Authors
- Nayuta Tsushima (ORCID: https://orcid.org/0000-0002-9856-1054)
- Satoshi Kano (ORCID: https://orcid.org/0000-0003-4750-5332)
- Kohsuke Kudo (ORCID: https://orcid.org/0000-0001-5351-9242)
- Masami Yoneyama
- Yukie Shimizu (ORCID: https://orcid.org/0000-0002-6070-3766)
- Taisuke Harada (ORCID: https://orcid.org/0000-0003-0468-2777)
- Hiroyuki Kameda (ORCID: https://orcid.org/0000-0003-3667-4085)
- Noriyuki Fujima (ORCID: https://orcid.org/0000-0001-9021-347X)
- Akihiro Homma (ORCID: https://orcid.org/0000-0003-1488-0646)
- Yohei Ikebe (ORCID: https://orcid.org/0000-0001-9129-0979)
- Jihun Kwon (ORCID: https://orcid.org/0000-0003-1810-0205)
Institutions
- Hokkaido University (JP)
- Hokkaido University Hospital (JP)
Publication Details
- Journal
- Japanese Journal of Radiology
- Published
- 2025-03-05
- DOI
- https://doi.org/10.1007/s11604-025-01756-y
- Citations
- 5
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 6.80