Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

Journal: Academic radiology
Published Date:

Abstract

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.5 T and 3 T between August 2020 and February 2021 were enrolled in this single-center, retrospective study. HASTE and standard sequences were assessed regarding overall and organ-based image quality, noise, contrast, sharpness, artifacts, diagnostic confidence, as well as lesion detectability using a Likert scale ranging from 1 to 4 (4 = best). The number of visible lesions of each organ was counted and the largest diameter of the major lesion was measured. HASTE showed excellent image quality (median 4, interquartile range 3-4), although BLADE (median 4, interquartile range 4-4) was rated significantly higher for overall and organ-based image quality of the adrenal gland (P < .001), contrast (P < 0.001), sharpness (P < 0.001), artifacts (P < 0.001), as well as diagnostic confidence (P < .001). No significant differences were found concerning noise (P = 0.886), organ-based image quality of the liver, pancreas, spleen, and kidneys (P = 0.120-0.366), number and measured diameter of the detected lesions (ICC = 0.972-1.0). Reduction of the aquisition time (TA) was at least 89% for 1.5 T images and 86% for 3 T images. HASTE provided excellent image quality, good diagnostic confidence and lesion detection compared to a standard T2-sequences, allowing an eminent reduction of the acquisition time.

Authors

  • Judith Herrmann
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Daniel Wessling
    From the Departments of Diagnostic and Interventional Radiology.
  • Dominik Nickel
    MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Simon Arberet
    Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA.
  • Haidara Almansour
    From the Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen.
  • Carmen Afat
    Internal Medicine I, Eberhard Karls University Tuebingen, Tuebingen.
  • Saif Afat
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany.
  • Sebastian Gassenmaier
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
  • Ahmed E Othman
    Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.