Uni-COAL: A Unified Framework for Cross-Modality Synthesis and
  Super-Resolution of MR Images
Zhiyun Song Zengxin Qi Xin Wang Xiangyu Zhao Zhenrong Shen Sheng Wang Manman Fei Zhe Wang Di Zang Dongdong Chen Linlin Yao Qian Wang Xuehai Wu Lichi Zhang
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.