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September 17, 2025Proceedings 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 Exhibition0 citations

Deep learning accelerated ultrafast MR imaging of the pelvis to screen for pelvic metastases in patients with suspicion for prostate cancer.

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ANAndrea NedelcuCWCaroline WilpertBOBenedict Oerther

Key Points

  • The DL-accelerated MRI achieved superior image quality while significantly reducing scan time to 15 seconds.
  • Diagnostic confidence remained equal when comparing both the 15-second and 45-second DL sequences against the standard sequence.
  • Image quality assessment indicated that while artifacts were noted in the slower DL sequence, its sharpness was improved.
  • This study suggests deep learning methods can enhance MRI capabilities, emphasizing their potential role in prostate cancer diagnostics.

Abstract

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.

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Cite This Study

Nedelcu et al. (2025) studied this question.

synapsesocial.com/papers/68d45b0b31b076d99fa5d3aahttps://doi.org/10.58530/2025/2447
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