无需配对数据，实现任意倍率的MRI超分辨率重建。
Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis
- 分两阶段：先学通用解剖先验，再融合个体低分辨图像。
- 在16x~32x极端放大下仍保持高精度，优于现有方法。
- 适合临床少样本、多尺度重建场景，计算轻量。
多对比度超分辨率（MCSR）对MRI图像增强至关重要，但现有深度学习方法受限于需大量成对的低/高分辨率（LR/HR）训练数据，且仅支持固定上采样倍率。尽管近期自监督方法消除了成对数据需求，却未能利用有价值的群体先验信息。本文提出一种新型解耦式MCSR框架，将任务分解为两阶段：(1) 仅需未配对群体数据训练一次的无配对跨模态合成（uCMS）模块，用于学习鲁棒的解剖先验；(2) 轻量化、患者特异的隐式重表示（IrR）模块，通过自监督方式融合群体先验与个体低分辨目标数据。该设计在无需任何成对数据的情况下，同时实现群体知识与个体保真度的融合。通过构建在隐式神经表示基础上的IrR模块，本框架天然具备尺度无关性。实验表明，该方法在多个数据集上表现更优，在16x和32x极端放大条件下仍保持卓越鲁棒性，而现有方法在此类场景中失效。本工作提供了一种数据高效、灵活且计算轻量的MCSR范式，支持高保真、任意倍率的重建。
原文摘要 · Abstract (English)
Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, paired low- and high-resolution (LR/HR) training datasets, which are scarce, and are trained for fixed upsampling scales. While recent self-supervised methods remove the paired data requirement, they fail to leverage valuable population-level priors. In this work, we propose a novel, decoupled MCSR framework that resolves both limitations. We reformulate MCSR into two stages: (1) an unpaired cross-modal synthesis (uCMS) module, trained once on unpaired population data to learn a robust anatomical prior; and (2) a lightweight, patient-specific implicit re-representation (IrR) module. This IrR module is optimized in a self-supervised manner to fuse the population prior with the subject's own LR target data. This design uniquely fuses population-level knowledge with patient-specific fidelity without requiring any paired LR/HR or paired cross-modal training data. By building the IrR module on an implicit neural representation, our framework is also inherently scale-agnostic. Our method demonstrates superior quantitative performance on different datasets, with exceptional robustness at extreme scales (16x, 32x), a regime where competing methods fail. Our work presents a data-efficient, flexible, and computationally lightweight paradigm for MCSR, enabling high-fidelity, arbitrary-scale
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