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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 thedesired modality and reducing the slice thickness. Despite the promisingsynthetic 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 imagesynthesis tasks with arbitrary requirements of modality and resolutionsettings, so that the resources for training and deploying the models can begreatly reduced. However, none of the previous works is capable of performingCMS, SR, and CMSR using a unified network. Moreover, these MRI reconstructionmethods often treat alias frequencies improperly, resulting in suboptimaldetail restoration. In this paper, we propose a Unified Co-Modulated Alias-freeframework (Uni-COAL) to accomplish the aforementioned tasks with a singlenetwork. The co-modulation design of the image-conditioned and stochasticattribute representations ensures the consistency between CMS and SR, whilesimultaneously accommodating arbitrary combinations of input/output modalitiesand thickness. The generator of Uni-COAL is also designed to be alias-freebased on the Shannon-Nyquist signal processing framework, ensuring effectivesuppression of alias frequencies. Additionally, we leverage the semantic priorof Segment Anything Model (SAM) to guide Uni-COAL, ensuring a more authenticpreservation of anatomical structures during synthesis. Experiments on threedatasets demonstrate that Uni-COAL outperforms the alternatives in CMS, SR, andCMSR tasks for MR images, which highlights its generalizability to wide-rangeapplications.

Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images