In magnetic resonance imaging (MRI), several images can be obtained using different imaging settings (e.g. T1, T2, DWI, and Flair). These images have similar anatomical structures but are with different contrasts, which provide a wealth of information for diagnosis. However, the images under specific imaging settings may not be available due to the limitation of scanning time or corruption caused by noises. It is attractive to derive missing images with some settings from the available MR images. In this paper, we propose a novel end-to-end multisetting MR image synthesis method. The proposed method is based on generative adversarial networks (GANs) - a deep learning model. In the proposed method, different MR images obtained by different settings are used as the inputs of a GANs and each image is encoded by an encoder. Each encoder includes a refinement structure which is used to extract a multiscale feature map from an input image. The multiscale feature maps from different input images are then fused to generate several desired target images under specific settings. Because the resultant images obtained with GANs have blurred edges, we fuse gradient prior information in the model to protect high frequency information such as important tissue textures of medical images. In the proposed model, the multiscale information is also adopted in the adversarial learning (not just in the generator or discriminator) so that we can produce high quality synthesized images. We evaluated the proposed method on two public datasets: BRATS and ISLES. Experimental results demonstrate that the proposed approach is superior to current state-of-the-art methods.

中文翻译：

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使用梯度先验和对抗学习的多模态 MR 图像合成

在磁共振成像 (MRI) 中，可以使用不同的成像设置（例如 T1、T2、DWI 和 Flair）获得多个图像。这些图像具有相似的解剖结构，但具有不同的对比度，为诊断提供了丰富的信息。但是，由于扫描时间的限制或噪声导​​致的损坏，特定成像设置下的图像可能无法使用。从可用的 MR 图像中获取具有某些设置的缺失图像是很有吸引力的。在本文中，我们提出了一种新颖的端到端多设置 MR 图像合成方法。所提出的方法基于生成对抗网络（GAN）——一种深度学习模型。在所提出的方法中，通过不同设置获得的不同 MR 图像用作 GAN 的输入，每个图像由编码器编码。每个编码器都包含一个细化结构，用于从输入图像中提取多尺度特征图。然后融合来自不同输入图像的多尺度特征图，以在特定设置下生成多个所需的目标图像。由于用 GAN 获得的结果图像边缘模糊，我们在模型中融合梯度先验信息以保护高频信息，例如医学图像的重要组织纹理。在所提出的模型中，对抗学习中也采用了多尺度信息（不仅仅是在生成器或鉴别器中），以便我们可以生成高质量的合成图像。我们在两个公共数据集上评估了所提出的方法：BRATS 和 ISLES。实验结果表明，所提出的方法优于当前最先进的方法。