Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

Qian Chen; Shu Fang; Yang Yuchen; Ruokun Li; Rong Deng; Yongjun Chen; Di Ma; Huimin Lin; Fuhua Yan

doi:10.1016/j.ejrad.2023.111149

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

Eur J Radiol. 2023 Nov:168:111149. doi: 10.1016/j.ejrad.2023.111149. Epub 2023 Oct 13.

Authors

Qian Chen¹, Shu Fang², Yang Yuchen³, Ruokun Li², Rong Deng², Yongjun Chen³, Di Ma³, Huimin Lin⁴, Fuhua Yan⁵

Affiliations

¹ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060, China.
² Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
³ Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
⁴ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China. Electronic address: lhm12362@rjh.com.cn.
⁵ Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China. Electronic address: yfh11655@rjh.com.cn.

PMID: 37862927
DOI: 10.1016/j.ejrad.2023.111149

Abstract

Purpose: Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla.

Method: In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI₀), 50 (DWI₅₀) and 800 s/mm² (DWI₈₀₀) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant.

Results: A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI₈₀₀ the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001).

Conclusions: DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.

Keywords: Apparent diffusion coefficient; Deep learning reconstruction; Diffusion weighted imaging.

MeSH terms

Deep Learning*
Diffusion Magnetic Resonance Imaging / methods
Feasibility Studies
Humans
Liver / diagnostic imaging
Neoplasms*
Prospective Studies