TY - JOUR
T1 - Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction
T2 - Comparison with conventional diffusion weighted imaging
AU - Bae, Sung Hwan
AU - Hwang, Jiyoung
AU - Hong, Seong Sook
AU - Lee, Eun Ji
AU - Jeong, Jewon
AU - Benkert, Thomas
AU - Sung, Jae Kon
AU - Arberet, Simon
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/9
Y1 - 2022/9
N2 - 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.
AB - 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.
KW - Deep learning reconstruction
KW - Diffusion weighted imaging,
KW - apparent diffusion coefficient
KW - liver
UR - http://www.scopus.com/inward/record.url?scp=85133494441&partnerID=8YFLogxK
U2 - 10.1016/j.ejrad.2022.110428
DO - 10.1016/j.ejrad.2022.110428
M3 - Article
C2 - 35797791
AN - SCOPUS:85133494441
SN - 0720-048X
VL - 154
JO - European Journal of Radiology
JF - European Journal of Radiology
M1 - 110428
ER -