Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction.

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.)