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

Abstract

The purpose of this study was to investigate the impact of deep learning accelerated diffusion-weighted imaging (DWIDL) in 1.5-T liver MRI on image quality, sharpness, and diagnostic confidence. One-hundred patients who underwent liver MRI at 1.5-T including DWI with two different b-values (50 and 800s/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 1min 28s for DWIDL as compared to 2min 31s for standard DWI (DWIStd) 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. 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 DWIDL versus DWIStd (P<0.001). SNR measurements in the above-mentioned tissues also showed significantly superior results for DWIDL versus DWIStd for b=50s/mm² and ADC maps (all P<0.001). For b=800s/mm², significantly superior results were found for the spleen, right hemiliver, and erector spinae muscles. DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.