Deep learning accelerated ultrafast MR imaging of the pelvis to screen for pelvic metastases in patients with suspicion for prostate cancer.
Motivation: Deep learning (DL) accelerated image reconstruction in prostate MRI can improve the problem of limited scan capacity, while maintaining image quality. Goal(s): To demonstrate the feasibility of a research post-contrast T1w VIBE Dixon sequence with DL acceleration. Approach: Radiologists compared 2 DL accelerated sequences (15 and 45 seconds) to the standard sequence (2:23 minutes) for image quality and diagnostic confidence in detecting pelvic metastases. Additionally, signal homogeneity was measured in the piriformis muscle. Results: The faster DL sequence proves equal or better in all aspects. The slower DL sequence demonstrates more artifacts, slightly lower signal homogeneity, but improved sharpness and equal diagnostic confidence. Impact: Deep learning accelerated T1w VIBE Dixon imaging with drastically shortened acquisition time of 15 seconds demonstrates equal or superior image quality and equal diagnostic confidence for pelvic lymph nodes and bone lesions in patients with suspicion for prostate cancer.
Authors
- Fabian Bamberg (ORCID: https://orcid.org/0000-0002-7460-3942)
- Caroline Wilpert (ORCID: https://orcid.org/0000-0003-2375-2478)
- Dominik M. Nickel (ORCID: https://orcid.org/0000-0002-0360-7233)
- Benedict Oerther (ORCID: https://orcid.org/0000-0003-4267-3601)
- Ralph Strecker (ORCID: https://orcid.org/0000-0002-8232-8952)
- Maximilian Frederik Russe (ORCID: https://orcid.org/0000-0003-3187-2429)
- Andrea Nedelcu (ORCID: https://orcid.org/0009-0000-6812-5334)
- Hannes Engel (ORCID: https://orcid.org/0000-0003-1985-325X)
- Jakob Weiss (ORCID: https://orcid.org/0000-0002-0336-8519)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/2447
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00