Effect of Deep Learning Reconstruction on Respiratory-triggered T2-weighted MR Imaging of the Liver: A Comparison between the Single-shot Fast Spin-echo and Fast Spin-echo Sequences

  • Kiso Kengo
    Department of Radiology, Osaka University Graduate School of Medicine
  • Tsuboyama Takahiro
    Department of Radiology, Osaka University Graduate School of Medicine
  • Onishi Hiromitsu
    Department of Radiology, Osaka University Graduate School of Medicine
  • Ogawa Kazuya
    Department of Radiology, Osaka University Graduate School of Medicine
  • Nakamoto Atsushi
    Department of Radiology, Osaka University Graduate School of Medicine
  • Tatsumi Mitsuaki
    Department of Radiology, Osaka University Graduate School of Medicine
  • Ota Takashi
    Department of Radiology, Osaka University Graduate School of Medicine
  • Fukui Hideyuki
    Department of Radiology, Osaka University Graduate School of Medicine
  • Yano Keigo
    Department of Radiology, Osaka University Graduate School of Medicine
  • Honda Toru
    Department of Radiology, Osaka University Graduate School of Medicine
  • Kakemoto Shinji
    Department of Radiology, Osaka University Graduate School of Medicine
  • Koyama Yoshihiro
    Department of Radiology, Osaka University Hospital
  • Tarewaki Hiroyuki
    Department of Radiology, Osaka University Hospital
  • Tomiyama Noriyuki
    Department of Radiology, Osaka University Graduate School of Medicine

抄録

<p>Purpose: To compare the effects of deep learning reconstruction (DLR) on respiratory-triggered T2-weighted MRI of the liver between single-shot fast spin-echo (SSFSE) and fast spin-echo (FSE) sequences.</p><p>Methods: Respiratory-triggered fat-suppressed liver T2-weighted MRI was obtained with the FSE and SSFSE sequences at the same spatial resolution in 55 patients. Conventional reconstruction (CR) and DLR were applied to each sequence, and the SNR and liver-to-lesion contrast were measured on FSE-CR, FSE-DLR, SSFSE-CR, and SSFSE-DLR images. Image quality was independently assessed by three radiologists. The results of the qualitative and quantitative analyses were compared among the four types of images using repeated-measures analysis of variance or Friedman’s test for normally and non-normally distributed data, respectively, and a visual grading characteristics (VGC) analysis was performed to evaluate the image quality improvement by DLR on the FSE and SSFSE sequences.</p><p>Results: The liver SNR was lowest on SSFSE-CR and highest on FSE-DLR and SSFSE-DLR (P < 0.01). The liver-to-lesion contrast did not differ significantly among the four types of images. Qualitatively, noise scores were worst on SSFSE-CR but best on SSFSE-DLR because DLR significantly reduced noise (P < 0.01). In contrast, artifact scores were worst both on FSE-CR and FSE-DLR (P < 0.01) because DLR did not reduce the artifacts. Lesion conspicuity was significantly improved by DLR compared with CR in the SSFSE (P < 0.01) but not in FSE sequences for all readers. Overall image quality was significantly improved by DLR compared with CR for all readers in the SSFSE (P < 0.01) but only one reader in the FSE (P < 0.01). The mean area under the VGC curve values for the FSE-DLR and SSFSE-DLR sequences were 0.65 and 0.94, respectively.</p><p>Conclusion: In liver T2-weighted MRI, DLR produced more marked improvements in image quality in SSFSE than in FSE.</p>

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