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Rationale and Objectives: To determine the impact on acquisition time reduction and image quality of a deep learning (DL) reconstruction for accelerated diffusion-weighted imaging (DWI) of the pelvis at 1.5 T compared to standard DWI., Materials and Methods: A total of 55 patients (mean age, 61 ± 13 years; range, 27-89; 20 men, 35 women) were consecutively included in this retrospective, monocentric study between February and November 2022. Inclusion criteria were (1) standard DWI (DWI S ) in clinically indicated magnetic resonance imaging (MRI) at 1.5 T and (2) DL-reconstructed DWI (DWI DL ). All patients were examined using the institution's standard MRI protocol according to their diagnosis including DWI with two different b-values (0 and 800 s/mm 2 ) and calculation of apparent diffusion coefficient (ADC) maps. Image quality was qualitatively assessed by four radiologists using a visual 5-point Likert scale (5 = best) for the following criteria: overall image quality, noise level, extent of artifacts, sharpness, and diagnostic confidence. The qualitative scores for DWI S and DWI DL were compared with the Wilcoxon signed-rank test., Results: The overall image quality was evaluated to be significantly superior in DWI DL compared to DWI S for b = 0 s/mm 2 , b = 800 s/mm 2 , and ADC maps by all readers (P < .05). The extent of noise was evaluated to be significantly less in DWI DL compared to DWI S for b = 0 s/mm 2 , b = 800 s/mm 2 , and ADC maps by all readers (P < .001). No significant differences were found regarding artifacts, lesion detectability, sharpness of organs, and diagnostic confidence (P > .05). Acquisition time for DWI S was 2:06 minutes, and simulated acquisition time for DWI DL was 1:12 minutes., Conclusion: DL image reconstruction improves image quality, and simulation results suggest that a reduction in acquisition time for diffusion-weighted MRI of the pelvis at 1.5 T is possible., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships that may be considered potential competing interests: Elisabeth Weiland and Thomas Benkert report a relationship with Siemens Healthineers that includes employment, and both provided us with the deep learning reconstruction. Full control over the patient data was with all other authors at the University of Tübingen., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWI DL ) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence., Materials and Methods: One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800 s/mm²) between February and April 2022 were retrospectively included. There were 54 men and 46 women, with a mean age of 59 ± 14 (SD) years (range: 21-88 years). The single average raw data were retrospectively processed using a deep learning (DL) image reconstruction algorithm leading to a simulated acquisition time of 1 min 28 s for DWI DL as compared to 2 min 31 s for standard DWI (DWI Std ) via reduction of signal averages. All DWI datasets were reviewed by four radiologists using a Likert scale ranging from 1-4 using the following criteria: noise level, extent of artifacts, sharpness, overall image quality, and diagnostic confidence. Furthermore, quantitative assessment of noise and signal-to-noise ratio (SNR) was performed via regions of interest., Results: No significant differences were found regarding artifacts and overall image quality (P > 0.05). Noise measurements for the spleen, liver, and erector spinae muscles revealed significantly lower noise for DWI DL versus DWI Std (P < 0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWI DL versus DWI Std for b = 50 s/mm² and ADC maps (all P < 0.001). For b = 800 s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles., Conclusions: DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality., Competing Interests: Declaration of Competing Interest Thomas Benkert and Elisabeth Weiland are employees of Siemens Healthcare GmbH., (Copyright © 2022 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.)