Ultrafast T2-weighted MR Imaging of the Urinary Bladder using Deep Learning-Accelerated HASTE at 3 Tesla

Abstract Objective 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. Methods 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. Results Overall image quality of DL-HASTE (5; [IQR]: 4, 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). Conclusion DL-HASTE allows for ultrafast imaging of the bladder with high image quality and is a promising additional sequence to T2-TSE.