Diagnostic Confidence And Feasibility Of A Deep Learning Accelerated Haste Sequence Of The Abdomen In A Single Breath-Hold

INVESTIGATIVE RADIOLOGY(2021)

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摘要
ObjectiveThe 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 (HASTE(DL)) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE). Materials and MethodsSixty-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 (HASTE(S)) (TA = 1:30 minutes), as well as a single breath-hold HASTE with deep learning reconstruction (HASTE(DL)) (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. ResultsHASTE(DL) was successfully acquired in all patients. Overall image quality for HASTE(DL) was rated as good (median, 3; interquartile range, 3-4) and was significantly superior to HASTE(s) (P < 0.001) and inferior to BLADE (P = 0.001). Noise, sharpness, and artifacts for HASTE(DL) reached similar levels to BLADE (P <= 0.176) and were significantly superior to HASTE(s) (P < 0.001). Diagnostic confidence for HASTE(DL) was rated excellent by both readers and significantly superior to HASTE(s) (P < 0.001) and inferior to BLADE (P = 0.044). Lesion detectability and lesion characterization for HASTE(DL) reached similar levels to those of BLADE (P <= 0.523) and were significantly superior to HASTE(s) (P < 0.001). Concerning the number of detected lesions and the measured diameter of the largest lesion, no significant differences were found comparing BLADE, HASTE(S), and HASTE(DL) (P <= 0.912). ConclusionsThe single breath-hold HASTE(DL) 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.
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关键词
magnetic resonance imaging, deep learning reconstruction, image processing, diagnostic imaging, neoplasm staging
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