Xie Huiqiao, Lei Yang, Wang Tonghe, Roper Justin, Axente Marian, Bradley Jeffrey D, Liu Tian, Yang Xiaofeng
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA.
Med Phys. 2022 May;49(5):3278-3287. doi: 10.1002/mp.15578. Epub 2022 Mar 7.
Gadolinium-based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network.
The proposed workflow consists of two sequential networks: (1) a retina U-Net, which is first trained to derive semantic features from the non-contrast MR images in representing the tumor regions; and (2) a synthesis module, which is trained after the retina U-Net to take the concatenation of the semantic feature maps and non-contrast MR image as input and to generate the synthetic contrast enhanced MR images. After network training, only the non-contrast enhanced MR images are required for the input in the proposed workflow. The MR images of 369 patients from the multimodal brain tumor segmentation challenge 2020 (BraTS2020) dataset were used in this study to evaluate the proposed workflow for synthesizing contrast enhanced MR images (200 patients for five-fold cross-validation and 169 patients for hold-out test). Quantitative evaluations were conducted by calculating the normalized mean absolute error (NMAE), structural similarity index measurement (SSIM), and Pearson correlation coefficient (PCC). The original contrast enhanced MR images were considered as the ground truth in this analysis.
The proposed cascade deep learning workflow synthesized contrast enhanced MR images that are not visually differentiable from the ground truth with and without supervision of the tumor contours during the network training. Difference images and profiles of the synthetic contrast enhanced MR images revealed that intensity differences could be observed in the tumor region if the contour information was not incorporated in network training. Among the hold-out test patients, mean values and standard deviations of the NMAE, SSIM, and PCC were 0.063±0.022, 0.991±0.007 and 0.995±0.006, respectively, for the whole brain; and were 0.050±0.025, 0.993±0.008 and 0.999±0.003, respectively, for the tumor contour regions. Quantitative evaluations with five-fold cross-validation and hold-out test showed that the calculated metrics can be significantly enhanced (p-values ≤ 0.002) with the tumor contour supervision in network training.
The proposed workflow was able to generate synthetic contrast enhanced MR images that closely resemble the ground truth images from non-contrast enhanced MR images when the network training included tumor contours. These results suggest that it may be possible to minimize the use of GBCAs in cranial MR imaging studies.
基于钆的造影剂（GBCAs）在磁共振成像（MR）中广泛用于诊断研究和治疗规划。尽管GBCAs通常被认为是安全的，但最近人们对其在MR成像中的使用提出了各种健康和环境方面的担忧。这项工作的目的是使用一种将轮廓信息纳入网络的级联深度学习工作流程，从未增强的对应图像中生成合成对比增强MR图像，从而无需使用GBCAs。
所提出的工作流程由两个连续的网络组成：（1）视网膜U-Net，首先对其进行训练，以从非对比MR图像中提取语义特征来表示肿瘤区域；（2）合成模块，在视网膜U-Net之后进行训练，将语义特征图和非对比MR图像的拼接作为输入，并生成合成对比增强MR图像。网络训练后，所提出的工作流程仅需要非对比增强MR图像作为输入。本研究使用了来自2020年多模态脑肿瘤分割挑战赛（BraTS2020）数据集的369例患者的MR图像，以评估所提出的合成对比增强MR图像的工作流程（200例患者用于五折交叉验证，169例患者用于留出测试）。通过计算归一化平均绝对误差（NMAE）、结构相似性指数测量（SSIM）和皮尔逊相关系数（PCC）进行定量评估。在该分析中，将原始对比增强MR图像视为基准真值。
所提出的级联深度学习工作流程合成的对比增强MR图像，在网络训练期间有无肿瘤轮廓监督的情况下，在视觉上与基准真值无法区分。合成对比增强MR图像的差异图像和剖面图显示，如果在网络训练中未纳入轮廓信息，则在肿瘤区域可观察到强度差异。在留出测试患者中，全脑NMAE、SSIM和PCC的平均值和标准差分别为0.063±0.022、0.99l±0.007和0.995±0.006；肿瘤轮廓区域分别为0.050±0.025、0.993±0.008和0.999±0.003‘五折交叉验证和留出测试的定量评估表明，在网络训练中进行肿瘤轮廓监督时，计算出的指标可显著提高（p值≤0.002）。
当网络训练包括肿瘤轮廓时，所提出的工作流程能够从非对比增强MR图像生成与基准真值图像非常相似的合成对比增强MR图像：这些结果表明，在颅脑MR成像研究中减少GBCAs的使用可能是可行的。
