Acquisition time reduction of diffusion-weighted liver imaging using deep learning image reconstruction - 06/12/22
Deep learning image reconstruction of diffusion-weighted liver imaging including acquisition time reduction of more than 40% is feasible without loss of image quality.
Deep learning image reconstruction of diffusion-weighed liver imaging provides significant reduction of the noise (P < 0.001).
Deep learning image reconstruction of diffusion-weighted liver imaging provides significantly greater signal intensities on ADC map for the liver, spleen, and erector spinae muscles.
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.
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 DWIDL as compared to 2 min 31 s 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 = 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.
DL image reconstruction of liver DWI at 1.5-T is feasible including significant reduction of acquisition time without compromised image quality.Le texte complet de cet article est disponible en PDF.
Keywords : Deep learning, Diffusion-weighted imaging, Image reconstruction, Liver, Magnetic resonance imaging, Signal-to-noise ratio
Abbreviations : ADC, DL, DLR, DWI, DWIStd, DWIDL, GRE, MRI, PI, ROI, SD, SI, SMS, SNR, TA, TSE
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