Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging

Sung Hwan Bae, Jiyoung Hwang, Seong Sook Hong, Eun Ji Lee, Jewon Jeong, Thomas Benkert, Jae Kon Sung, Simon Arberet

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Abstract

Purpose: To assess the clinical feasibility of accelerated deep learning–reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI. Methods: Seventy-four consecutive patients who underwent 3 T abdominal magnetic resonance imaging (MRI) were retrospectively enrolled. DWI were acquired using both conventional DWI and DWI with deep-learning reconstruction (DL DWI). Image quality (overall image quality, anatomic sharpness and details, artifacts, noise, and lesion conspicuity) was scored by two radiologists and compared between two DWI sequences. The apparent diffusion coefficient (ADC) was measured in six locations of the liver parenchyma and focal lesions and compared between two DWI sequences. Results: The mean acquisition time for the DL DWI (216.87 ± 49.23 sec) was significantly shorter (P < 0.001) than for conventional DWI (358.69 ± 105.93 sec). DL DWI achieved higher scores than conventional DWI for all qualitative image quality parameters (P < 0.001). DL DWI had a more homogeneous distribution of ADC values throughout the liver, except for the left superior section, compared with conventional DWI. The standard deviations of the ADC values for all hepatic areas were significantly lower in DL DWI than in conventional DWI (all, P < 0.001). The ADC values for the liver parenchyma and focal hepatic lesions were lower in DL DWI than in conventional DWI. Conclusions: DL DWI is a feasible acquisition technique in clinical routines and provides improved image quality and simultaneously significant reduction in scan time compared with conventional DWI.

Original languageEnglish
Article number110428
JournalEuropean Journal of Radiology
Volume154
DOIs
StatePublished - Sep 2022

Keywords

  • apparent diffusion coefficient
  • Deep learning reconstruction
  • Diffusion weighted imaging,
  • liver

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