Today Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic Resonance Imaging (MRI) for diagnosing various diseases. However, GBCAs are expensive and may accumulate in patients with potential side effects, thus dose-reduction is recommended. Still, it is unclear to which extent the GBCA dose can be reduced while preserving the diagnostic value -- especially in pathological regions. To address this issue, we collected brain MRI scans at numerous non-standard GBCA dosages and developed a conditional GAN model for synthesizing corresponding images at fractional dose levels. Along with the adversarial loss, we advocate a novel content loss function based on the Wasserstein distance of locally paired patch statistics for the faithful preservation of noise. Our numerical experiments show that conditional GANs are suitable for generating images at different GBCA dose levels and can be used to augment datasets for virtual contrast models. Moreover, our model can be transferred to openly available datasets such as BraTS, where non-standard GBCA dosage images do not exist.

翻译：目前，钆基对比剂在磁共振成像中对于诊断多种疾病不可或缺。然而，钆基对比剂价格昂贵且可能在患者体内积累并产生潜在副作用，因此建议降低剂量。但仍不清楚钆基对比剂剂量可降低到何种程度仍能保留诊断价值——尤其是在病理区域。为解决这一问题，我们收集了大量非标准钆基对比剂剂量的脑部MRI扫描数据，并开发了一种条件生成对抗网络模型，用于合成对应分数剂量水平的图像。除了对抗性损失外，我们提出了一种基于局部配对块统计的Wasserstein距离的新型内容损失函数，以忠实保留噪声特征。数值实验表明，条件生成对抗网络适用于生成不同钆基对比剂剂量水平的图像，并可用于扩充虚拟对比模型的数据集。此外，我们的模型可迁移至公开数据集（如BraTS），而这类数据中不存在非标准钆基对比剂剂量图像。