Literature DB >> 33965966

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Judith Herrmann1, Dominik Nickel, John P Mugler, Simon Arberet, Sebastian Gassenmaier, Saif Afat, Konstantin Nikolaou, Ahmed E Othman.   

Abstract

OBJECTIVE: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE).
MATERIALS AND METHODS: First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants. Image quality and diagnostic confidence with 5 different FA evolutions of a HASTEDL were assessed to identify the most suitable FA evolution. Second, the identified FA evolution was implemented clinically and evaluated in 51 patients undergoing a clinically indicated liver magnetic resonance imaging at 3 T. Two radiologists assessed the HASTEDL and standard sequences regarding overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence using a Likert scale ranging from 1 to 4, with 4 being the best. Comparative analyses were conducted to assess the differences between HASTEDL (acquisition time, 21 seconds; single breath-hold) and the routinely used T2-weighted BLADE sequence (acquisition time, 4 minutes; respiratory triggering).
RESULTS: From the volunteer study, the FA evolution characterized by the control points 130-90-110-130 degrees (HASTEDL) was identified as optimal among the 5 evolutions evaluated and was implemented in our clinical protocol. In all 51 patients, HASTEDL was successfully acquired at 3 T and showed excellent image quality (median, 4; interquartile range, 3-4). Although BLADE was rated significantly higher for overall image quality, noise, contrast, sharpness, artifacts, CRSL, and diagnostic confidence than HASTEDL, no differences were found concerning the number (n = 102) and measured diameter of the detected hepatic lesions between the 2 sequences BLADE and HASTEDL.
CONCLUSIONS: The proposed single-breath-hold abdominal HASTEDL with variable refocusing FAs is feasible at 3 T within SAR limits and yields high image quality and diagnostic confidence as compared with a standard T2-weighted acquisition technique, at a 10th of the acquisition time.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Entities:  

Year:  2021        PMID: 33965966     DOI: 10.1097/RLI.0000000000000785

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


  4 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.  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

3.  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 4.  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
  4 in total

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