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.

通过提出统一的共模调制无混叠网络 (Uni-COAL) ，该论文研究了 MRI 图像的不同模态综合、超分辨率和综合任务，并利用 Shannon-Nyquist 信号处理框架实现无混叠的综合，以及利用 Segment Anything Model (SAM) 的语义先验来指导合成过程，取得了在多个数据集上优于其他方法的综合结果，提高了模型在广泛应用领域中的泛化性。

Uni-COAL：一种用于跨模态合成和超分辨率的磁共振图像统一框架

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

People ``understand'' the world via vision, hearing, tactile, and also the
past experience. Human experience can be learned through normal learning (we
call it explicit knowledge), or subconsciously (we call it implicit knowledge).
These experiences learned through normal learning or subconsciously will be
encoded and stored in the brain. Using these abundant experience as a huge
database, human beings can effectively process data, even they were unseen
beforehand. In this paper, we propose a unified network to encode implicit
knowledge and explicit knowledge together, just like the human brain can learn
knowledge from normal learning as well as subconsciousness learning. The
unified network can generate a unified representation to simultaneously serve
various tasks. We can perform kernel space alignment, prediction refinement,
and multi-task learning in a convolutional neural network. The results
demonstrate that when implicit knowledge is introduced into the neural network,
it benefits the performance of all tasks. We further analyze the implicit
representation learnt from the proposed unified network, and it shows great
capability on catching the physical meaning of different tasks. The source code
of this work is at : this https URL

该论文提出了一种统一的网络结构，用于编码人类所学习到的显性知识和潜意识下的隐性知识，并能够同时服务于多种任务，并通过在卷积神经网络中引入隐性知识，进一步提高了各个任务的性能表现。