Literature DB >> 33208596

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.

Judith Herrmann1, Sebastian Gassenmaier1, Dominik Nickel2, Simon Arberet3, Saif Afat1, Andreas Lingg1, Matthias Kündel1, Ahmed E Othman1.   

Abstract

OBJECTIVE: The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE).
MATERIALS AND METHODS: Sixty-six patients who underwent 1.5-T liver magnetic resonance imaging were included in this monocentric, retrospective study. The following T2-weighted sequences in axial orientation and using spectral fat suppression were compared: a conventional respiratory-triggered BLADE sequence (time of acquisition [TA] = 4:00 minutes), a conventional multiple breath-hold HASTE sequence (HASTES) (TA = 1:30 minutes), as well as a single breath-hold HASTE with deep learning reconstruction (HASTEDL) (TA = 0:16 minutes). Two radiologists assessed the 3 sequences regarding overall image quality, noise, sharpness, diagnostic confidence, and lesion detectability as well as lesion characterization using a Likert scale ranging from 1 to 4 with 4 being the best. Comparative analyses were conducted to assess the differences between the 3 sequences.
RESULTS: HASTEDL was successfully acquired in all patients. Overall image quality for HASTEDL was rated as good (median, 3; interquartile range, 3-4) and was significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.001). Noise, sharpness, and artifacts for HASTEDL reached similar levels to BLADE (P ≤ 0.176) and were significantly superior to HASTEs (P < 0.001). Diagnostic confidence for HASTEDL was rated excellent by both readers and significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.044). Lesion detectability and lesion characterization for HASTEDL reached similar levels to those of BLADE (P ≤ 0.523) and were significantly superior to HASTEs (P < 0.001). Concerning the number of detected lesions and the measured diameter of the largest lesion, no significant differences were found comparing BLADE, HASTES, and HASTEDL (P ≤ 0.912).
CONCLUSIONS: The single breath-hold HASTEDL is feasible and yields comparable image quality and diagnostic confidence to standard T2-weighted TSE BLADE and may therefore allow for a remarkable time saving in abdominal imaging.
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Entities:  

Year:  2021        PMID: 33208596     DOI: 10.1097/RLI.0000000000000743

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  5 in total

Review 1.  Current Landscape and Future Perspectives of Abbreviated MRI for Hepatocellular Carcinoma Surveillance.

Authors:  Hyo Jung Park; Nieun Seo; So Yeon Kim
Journal:  Korean J Radiol       Date:  2022-04-13       Impact factor: 7.109

2.  Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence.

Authors:  Daniel Wessling; Judith Herrmann; Saif Afat; Dominik Nickel; Ahmed E Othman; Haidara Almansour; Sebastian Gassenmaier
Journal:  Tomography       Date:  2022-07-06

3.  Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol.

Authors:  Judith Herrmann; Gabriel Keller; Sebastian Gassenmaier; Dominik Nickel; Gregor Koerzdoerfer; Mahmoud Mostapha; Haidara Almansour; Saif Afat; Ahmed E Othman
Journal:  Eur Radiol       Date:  2022-04-07       Impact factor: 7.034

4.  Clinical Evaluation of an Abbreviated Contrast-Enhanced Whole-Body MRI for Oncologic Follow-Up Imaging.

Authors:  Judith Herrmann; Saif Afat; Andreas Brendlin; Maryanna Chaika; Andreas Lingg; Ahmed E Othman
Journal:  Diagnostics (Basel)       Date:  2021-12-16

Review 5.  Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Authors:  Sebastian Gassenmaier; Thomas Küstner; Dominik Nickel; Judith Herrmann; Rüdiger Hoffmann; Haidara Almansour; Saif Afat; Konstantin Nikolaou; Ahmed E Othman
Journal:  Diagnostics (Basel)       Date:  2021-11-24
  5 in total

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