Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Republic of Korea.
Med Phys. 2020 Mar;47(3):1151-1160. doi: 10.1002/mp.13999. Epub 2020 Jan 30.
Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptibility induced tissue contrast and SNR visualization of the SN pars compacta (SNPC) using conventional imaging technique in the clinical field strength (≤3T) has been limited. Susceptibility map-weighted imaging (SMWI) has been proposed to visualize SNPC at 3T. To better visualize nigrosome 1 and SN areas using SMWI, accurate estimation of the quantitative susceptibility mapping (QSM) map is essential. In SMWI processing, however, QSM processing time using conventional algorithms is the most time-consuming step and may limit clinical use. In this study, we introduce an efficient SMWI processing approach using the deep neural network (QSMnet). To improve the processing speed of SMWI while maintaining similar image quality to that obtained with the conventional method, QSMnet was applied to generate a susceptibility mask for SMWI processing.
To conduct a deep learning-based image to image operation modified QSMnet was utilized. The network was trained with in vivo MR data from 57 healthy controls. To validate SMWI results from QSMnet, four datasets from healthy controls were used as the test datasets. As a preliminary attempt to explore the clinical applicability, Parkinson's disease patient data were additionally tested. The SWMI images generated by QSMnet and conventional model-based QSM outputs were compared. To validate SMWI results, region of interest (ROI) analysis was performed. The mean signal values at the nigrosome 1 and the surrounding regions were measured to calculate contrast-to-noise ratio (CNR).
The experimental results confirmed that the proposed approach using QSMnet provided similar QSM and SMWI compared to that obtained with conventional iLSQR while the processing speed was much improved (5.4 times faster). The QSM results from QSMnet show similar tissue contrast with results from iLSQR. When compared, the absolute mean intensity difference between two methods near the nigrosome 1 was 0.015 ppm. SMWI results using susceptibility masks from QSMnet demonstrated signal distribution and tissue contrast that was comparable with those results from the conventional iLSQR method. The absolute difference maps of SMWI were calculated to show the similarity between the two methods. The overall mean absolute difference value in the presented ROIs obtained from healthy controls (n = 4) and a PD patient (n = 1) were 2.31 and 1.81, respectively. Mean CNR values (10 ROIs; n = 5; including both sides; 1.42 for QSMnet; 1.43 for iLSQR) between SN and nigrosome 1 in SMWI results obtained by ROI analysis were similar (P = 0.724).
In this study, we assessed an efficient approach for SMWI visualizing SN and nigrosome 1 on 3T. QSMnet provides a similar SMWI image to that obtained with the conventional iterative QSM algorithm and improves QSM processing speed by avoiding iterative computation. Since QSM is the most time-consuming step of SMWI processing, QSMnet can help to achieve a higher processing speed of SMWI. These results suggest that SMWI imaging with susceptibility masks using QSMnet is a more efficient approach.
黑质 1 （SN）中的黑质 1 可视性被用作帕金森病的磁共振成像生物标志物。由于在临床场强（≤3T）下使用常规成像技术对 SN pars compacta （SNPC）的组织对比和 SNR 可视化的敏感性较低，因此已经受到限制。已经提出了使用磁化率图加权成像（SMWI）在 3T 下可视化 SNPC。为了使用 SMWI 更好地可视化黑质 1 和 SN 区域，必须准确估计定量磁化率映射（QSM）图。然而，在 SMWI 处理中，使用常规算法的 QSM 处理时间是最耗时的步骤，可能会限制临床使用。在这项研究中，我们介绍了一种使用深度神经网络（QSMnet）的高效 SMWI 处理方法。为了在保持与传统方法相似的图像质量的同时提高 SMWI 的处理速度，应用 QSMnet 为 SMWI 处理生成磁化率掩模。
使用基于深度学习的图像到图像操作修改了 QSMnet。该网络使用来自 57 名健康对照者的体内 MR 数据进行训练。为了验证 QSMnet 的 SMWI 结果，使用了来自 4 个健康对照组的四个数据集作为测试数据集。作为探索临床适用性的初步尝试，还对帕金森病患者的数据进行了测试。比较了由 QSMnet 和常规基于模型的 QSM 输出生成的 SWMI 图像。为了验证 SMWI 结果，进行了感兴趣区域（ROI）分析。测量黑质 1 和周围区域的平均信号值，以计算对比噪声比（CNR）。
实验结果证实，与传统的 iLSQR 相比，使用 QSMnet 的所提出的方法提供了相似的 QSM 和 SMWI，同时处理速度大大提高（快 5.4 倍）。QSMnet 的 QSM 结果显示出与 iLSQR 相似的组织对比度。当比较时，两种方法在黑质 1 附近的绝对平均强度差异为 0.015 ppm。使用 QSMnet 感测率掩模的 SMWI 结果显示出与传统 iLSQR 方法相当的信号分布和组织对比度。为了显示两种方法之间的相似性，计算了 SMWI 的绝对差值图。在来自健康对照者（n=4）和 PD 患者（n=1）的所呈现 ROI 中获得的总体平均绝对差值值分别为 2.31 和 1.81。通过 ROI 分析获得的 SN 和黑质 1 在 SMWI 结果中的平均 CNR 值（10 个 ROI；n=5；包括双侧；QSMnet 为 1.42；iLSQR 为 1.43）相似（P=0.724）。
在这项研究中，我们评估了一种在 3T 上可视化 SN 和黑质 1 的高效 SMWI 方法。QSMnet 提供了与传统迭代 QSM 算法获得的相似的 SMWI 图像，并通过避免迭代计算提高了 QSM 处理速度。由于 QSM 是 SMWI 处理中最耗时的步骤，因此 QSMnet 可以帮助实现更高的 SMWI 处理速度。这些结果表明，使用 QSMnet 的磁化率掩模进行 SMWI 成像更有效。
