Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning
Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.
Authors
- Jordan D. LeGout (ORCID: https://orcid.org/0000-0003-3608-726X)
- Adam T. Froemming (ORCID: https://orcid.org/0000-0001-7706-8060)
- Naoki Takahashi (ORCID: https://orcid.org/0000-0002-7946-6078)
- Akira Kawashima (ORCID: https://orcid.org/0000-0003-4686-8369)
- Stephen J. Riederer (ORCID: https://orcid.org/0000-0001-8045-910X)
- Eric A. Borisch (ORCID: https://orcid.org/0000-0002-0849-1487)
- Hirotsugu Nakai (ORCID: https://orcid.org/0000-0002-2080-5901)
- Jacob N Gloe
Institutions
- Mayo Clinic (US)
- Kyoto University (JP)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic Hospital (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-04-29
- DOI
- https://doi.org/10.1186/s41747-025-00584-z
- Citations
- 5
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 7.59
Funders
- National Institutes of Health
- National Institute of Biomedical Imaging and Bioengineering
- National Center for Research Resources