A unique advantage of magnetic resonance imaging (MRI) is its mechanism for generating various image contrasts depending on tissue-specific parameters, which provides useful clinical information. Unfortunately, a complete set of MR contrasts is often difficult to obtain in a real clinical environment. Recently, there have been claims that generative models such as generative adversarial networks (GANs) can synthesize MR contrasts that are not acquired. However, the poor scalability of existing GAN-based image synthesis poses a fundamental challenge to understanding the nature of MR contrasts: which contrasts matter, and which cannot be synthesized by generative models? Here, we show that these questions can be addressed systematically by learning the joint manifold of multiple MR contrasts using collaborative generative adversarial networks. Our experimental results show that the exogenous contrast provided by contrast agents is not replaceable, but endogenous contrasts such as T₁ and T₂ can be synthesized from other contrasts. These findings provide important guidance for the acquisition-protocol design of MR in clinical environments.

磁共振成像（MRI）的独特优势是其根据组织特定参数生成各种图像对比度的机制，可提供有用的临床信息。不幸的是，在真实的临床环境中通常很难获得完整的MR对比。近来，已经声称诸如生成对抗网络（GAN）之类的生成模型可以合成未获得的MR对比。但是，现有基于GAN的图像合成的可伸缩性差，对理解MR对比的本质提出了根本性挑战：哪些对比很重要，而哪些不能由生成模型合成？在这里，我们表明可以通过使用协作生成对抗网络学习多个MR对比的联合流形来系统地解决这些问题。T ₁和T ₂可以由其他对比合成。这些发现为临床环境中MR的采集方案设计提供了重要的指导。