Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging
A newly developed deep learning technique for both denoising and artefact reduction in ZTE-MRI presented clinical usefulness. Specifically, AR protocol showed significantly improved image quality and comparable diagnostic accuracy comparable to CBCT. It can be expected that this novel technique would help overcome the current limitation of ZTE-MRI for replacing CBCT in bone imaging of TMJ.
Authors
- Yoon Joo Choi (ORCID: https://orcid.org/0000-0001-9225-3889)
- Sagar Mandava (ORCID: https://orcid.org/0000-0003-3425-7579)
- Chena Lee (ORCID: https://orcid.org/0000-0002-8943-4192)
- Joonsung Lee (ORCID: https://orcid.org/0000-0002-0164-1139)
- Sang‐Sun Han (ORCID: https://orcid.org/0000-0003-1775-7862)
- Kug Jin Jeon (ORCID: https://orcid.org/0000-0002-5862-2975)
- Maggie Fung
Institutions
- General Electric (Spain) (ES)
- Yonsei University (KR)
- Innovative Research (United States) (US)
- Yonsei University College of Dentistry
Publication Details
- Journal
- Dentomaxillofacial Radiology
- Published
- 2025-02-24
- DOI
- https://doi.org/10.1093/dmfr/twae063
- Citations
- 3
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.08