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@ARTICLE{Hinsen:304832,
      author       = {M. Hinsen and A. Nagel$^*$ and R. Heiss and M. May and M.
                      Wiesmueller and C. Mathy and M. Zeilinger and J. Hornung and
                      S. Mueller and M. Uder and M. Kopp},
      title        = {{D}eep learning-based acceleration and denoising of 0.55{T}
                      {MRI} for enhanced conspicuity of vestibular {S}chwannoma
                      post contrast administration.},
      journal      = {Neuroradiology},
      volume       = {nn},
      issn         = {0028-3940},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2025-01941},
      pages        = {nn},
      year         = {2025},
      note         = {epub},
      abstract     = {Deep-learning (DL) based MRI denoising techniques promise
                      improved image quality and shorter examination times. This
                      advancement is particularly beneficial for 0.55T MRI, where
                      the inherently lower signal-to-noise (SNR) ratio can
                      compromise image quality. Sufficient SNR is crucial for the
                      reliable detection of vestibular schwannoma (VS). The
                      objective of this study is to evaluate the VS conspicuity
                      and acquisition time (TA) of 0.55T MRI examinations with
                      contrast agents using a DL-denoising algorithm.From January
                      2024 to October 2024, we retrospectively included 30
                      patients with VS (9 women). We acquired a clinical reference
                      protocol of the cerebellopontine angle containing a T1w
                      fat-saturated (fs) axial (number of signal averages [NSA] 4)
                      and a T1w Spectral Attenuated Inversion Recovery (SPAIR)
                      coronal (NSA 2) sequence after contrast agent (CA)
                      application without advanced DL-based denoising (w/o DL). We
                      reconstructed the T1w fs CA sequence axial and the T1w SPAIR
                      CA coronal with full DL-denoising mode without change of
                      NSA, and secondly with 1 NSA for T1w fs CA axial and T1w
                      SPAIR coronal $(DL\&1NSA).$ Each sequence was rated on a
                      5-point Likert scale (1: insufficient, 3: moderate,
                      clinically sufficient; 5: perfect) for: overall image
                      quality; VS conspicuity, and artifacts. Secondly, we
                      analyzed the reliability of the size measurements. Two
                      radiologists specializing in head and neck imaging performed
                      the reading and measurements. The Wilcoxon Signed-Rank Test
                      was used for non-parametric statistical comparison.The
                      $DL\&4NSA$ axial/coronal study sequence achieved the highest
                      overall IQ (median 4.9). The image quality (IQ) for
                      $DL\&1NSA$ was higher (M: 4.0) than for the reference
                      sequence (w/o DL; median 4.0 versus 3.5, each p < 0.01).
                      Similarly, the VS conspicuity was best for $DL\&4NSA$ (M:
                      4.9), decreased for $DL\&1NSA$ (M: 4.1), and was lower but
                      still sufficient for w/o DL (M: 3.7, each p < 0.01). The TA
                      for the axial and coronal post-contrast sequences was 8:59
                      minutes for $DL\&4NSA$ and w/o DL and decreased to 3:24
                      minutes with $DL\&1NSA.This$ study underlines that advanced
                      DL-based denoising techniques can reduce the examination
                      time by more than half while simultaneously improving image
                      quality.},
      keywords     = {0.55T MRI (Other) / Deep learning denoising of MRI (Other)
                      / Head and neck imaging (Other) / Low field MRI (Other) /
                      Magnetic resonance imaging (Other) / Vestibular schwannoma
                      (Other)},
      cin          = {E020},
      ddc          = {610},
      cid          = {I:(DE-He78)E020-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40970959},
      doi          = {10.1007/s00234-025-03758-z},
      url          = {https://inrepo02.dkfz.de/record/304832},
}