Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI). Their primary goals are to enhance the imaging quality by synthesizing the desired modality and reducing the slice thickness. Despite the promising synthetic results, these techniques are often tailored to specific tasks, thereby limiting their adaptability to complex clinical scenarios. Therefore, it is crucial to build a unified network that can handle various image synthesis tasks with arbitrary requirements of modality and resolution settings, so that the resources for training and deploying the models can be greatly reduced. However, none of the previous works is capable of performing CMS, SR, and CMSR using a unified network. Moreover, these MRI reconstruction methods often treat alias frequencies improperly, resulting in suboptimal detail restoration. In this paper, we propose a Unified Co-Modulated Alias-free framework (Uni-COAL) to accomplish the aforementioned tasks with a single network. The co-modulation design of the image-conditioned and stochastic attribute representations ensures the consistency between CMS and SR, while simultaneously accommodating arbitrary combinations of input/output modalities and thickness. The generator of Uni-COAL is also designed to be alias-free based on the Shannon-Nyquist signal processing framework, ensuring effective suppression of alias frequencies. Additionally, we leverage the semantic prior of Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authentic preservation of anatomical structures during synthesis. Experiments on three datasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, and CMSR tasks for MR images, which highlights its generalizability to wide-range applications.

翻译：跨模态合成（CMS）、超分辨率（SR）及其结合（CMSR）在磁共振成像（MRI）领域得到了广泛研究。其主要目标是通过合成所需模态并降低切片厚度来提升成像质量。尽管取得了令人鼓舞的合成结果，但这些技术通常针对特定任务设计，从而限制了其在复杂临床场景中的适应性。因此，构建一个能够处理任意模态与分辨率设置需求的统一网络至关重要，这可以大幅降低模型训练与部署的资源消耗。然而，以往的工作均无法通过统一网络实现CMS、SR及CMSR任务。此外，这些MRI重建方法常对混叠频率处理不当，导致细节恢复效果欠佳。本文提出了一种统一的共调制无混叠框架（Uni-COAL），可通过单一网络完成上述任务。图像条件表征与随机属性表征的共调制设计确保了CMS与SR任务的一致性，同时支持任意输入/输出模态与厚度的组合。Uni-COAL的生成器基于香农-奈奎斯特信号处理框架实现了无混叠设计，从而有效抑制混叠频率。此外，我们利用Segment Anything Model (SAM)的语义先验引导Uni-COAL，在合成过程中更真实地保留解剖结构。在三个数据集上的实验表明，Uni-COAL在MR图像的CMS、SR及CMSR任务中均优于现有方案，凸显了其在广泛场景中的泛化能力。