深度学习重建在肝脏弥散加权成像中的临床可行性：改善图像质量和对表观弥散系数值的影响。
Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value.
机构信息
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.
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
出版信息
Eur J Radiol. 2023 Nov;168:111149. doi: 10.1016/j.ejrad.2023.111149. Epub 2023 Oct 13.
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 (DWI), 50 (DWI) and 800 s/mm (DWI) 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 DWI 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.
目的
肝脏弥散加权成像（DWI）的分辨率低、噪声大且存在伪影。本研究旨在探讨深度学习重建（DLR）对 3T 肝脏 DWI 图像质量和表观扩散系数（ADC）定量的影响。
方法
本前瞻性研究纳入了 2022 年 2 月至 2023 年 2 月连续因肝脏病变行肝脏 DWI（b 值分别为 0、50 和 800 s/mm²）的患者，将图像分别进行有（DLR 组）和无（非 DLR 组）DLR 重建，采用 Liker 评分系统进行定性评估，采用信号噪声比（SNR）、对比噪声比（CNR）和 ROI 分析肝/实质边界锐利度进行定量评估。测量病变的 ADC 值。还进行了体模实验，以研究决定 DLR 对 ADC 值影响的因素。采用配对 t 检验和 Wilcoxon 符号秩检验比较 DWI 的定性评分、SNR、CNR、边界锐利度和表观扩散系数（ADC）。P 值<0.05 被认为具有统计学意义。
结果
共纳入 85 例患者，共 170 个病灶。DLR 组的定性评分高于非 DLR 组，例如 DWI 的评分为 4.77±0.52 分，而非 DLR 组的评分为 4.30±0.63 分（P<0.001）。DLR 组的 SNR、CNR 和边界锐利度也高于非 DLR 组。DLR 降低了恶性肿瘤的 ADC 值（1.105[0.904, 1.340] 与 1.114[0.904, 1.320]）（P<0.001），但 DLR 和非 DLR 组的良恶性鉴别诊断效能无显著差异（P=57.3）。体模研究证实，低分辨率图像的 ADC 值降低，且不均匀结构的 ADC 值降低幅度大于均匀结构（P<0.001）。
结论
DLR 提高了肝脏 DWI 的图像质量。DLR 降低了病变的 ADC 值，但在 3.0T MRI 系统上，ADC 对良恶性肿瘤的鉴别诊断效能无影响。
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