Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses
Radiomics features were significantly different on DLR images which should encourage caution for clinical and research purposes. DLR algorithm decreases noise while preserving overall image quality.
Authors
- Véronique Fortier (ORCID: https://orcid.org/0000-0003-1859-003X)
- Evan McNabb (ORCID: https://orcid.org/0000-0002-3028-0694)
- Jérémy Dana (ORCID: https://orcid.org/0000-0003-0352-4128)
- Caroline Reinhold (ORCID: https://orcid.org/0000-0002-8852-3273)
- Giovanni Artho
- Simon Gauvin (ORCID: https://orcid.org/0009-0003-0266-6607)
- Yoshie Omiya (ORCID: https://orcid.org/0000-0002-4510-4482)
- Juan Jose Castro
- Ibtisam Al-Qanoobi
- Kenny Ah-Lan
Institutions
- Inserm (FR)
- McGill University Health Centre (CA)
- McGill University (CA)
- Université de Strasbourg (FR)
Publication Details
- Journal
- Research in Diagnostic and Interventional Imaging
- Published
- 2025-05-22
- DOI
- https://doi.org/10.1016/j.redii.2025.100059
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52