Ultrafast T2-weighted MR imaging of the urinary bladder using deep learning-accelerated HASTE at 3 Tesla.

Authors

Yan L,Tan Q,Kohnert D,Nickel MD,Weiland E,Kubicka F,Jahnke P,Geisel D,Wagner M,Walter-Rittel T

Affiliations (3)

  • Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.
  • MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
  • Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany. [email protected].

Abstract

This prospective study aimed to assess the feasibility of a half-Fourier single-shot turbo spin echo sequence (HASTE) with deep learning (DL) reconstruction for ultrafast imaging of the bladder with reduced susceptibility to motion artifacts. 50 patients underwent pelvic T2w imaging at 3 Tesla using the following MR sequences in sagittal orientation without antiperistaltic premedication: T2-TSE (time of acquisition [TA]: 2.03-4.00 min), standard HASTE (TA: 0.65-1.10 min), and DL-HASTE (TA: 0.25-0.47 min), with a slice thickness of 3 mm and a varying number of slices (25-45). Three radiologists evaluated the image quality of the three sequences quantitatively and qualitatively. Overall image quality of DL-HASTE (average score: 5) was superior to HASTE and T2-TSE (p < .001). DL-HASTE provided the clearest bladder wall delineation, especially in the apical part of the bladder (p < .001). SNR (36.3 ± 6.3) and CNR (50.3 ± 19.7) were the highest on DL-HASTE, followed by T2-TSE (33.1 ± 6.3 and 44.3 ± 21.0, respectively; p < .05) and HASTE (21.7 ± 5.4 and 35.8 ± 17.5, respectively; p < .01). A limitation of DL-HASTE and HASTE was the susceptibility to urine flow artifact within the bladder, which was absent or only minimal on T2-TSE. Diagnostic confidence in assessment of the bladder was highest with the combination of DL-HASTE and T2-TSE (p < .05). DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising addition to T2-TSE.

Topics

Deep LearningUrinary BladderMagnetic Resonance ImagingJournal Article

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