In this section
@article{Wu:2026:10.1109/TMI.2026.3739662,
author = {Wu, Y and Rui, H and Wang, F and Huang, J and Zhang, Z and Zhang, H and Wang, Z and Yang, G},
doi = {10.1109/TMI.2026.3739662},
journal = {IEEE Trans Med Imaging},
title = {Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis.},
url = {http://dx.doi.org/10.1109/TMI.2026.3739662},
volume = {PP},
year = {2026}
}
TY - JOUR
AB - 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 paired target-domain LR/HR training data or paired cross-modal HR reference-target training data. Here, 'unpaired' refers to the population-level training setting; at subject-specific inference, as in standard MCSR, the method uses a matched HR reference image from another contrast together with the subject's LR target image. 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 (16×, 32×), a regime where competing methods fail. Our work presents a data-efficient, flexible, and computationally lightweight paradigm for MCSR, enabling high-fidelity, arbitrary-scale reconstruction without the need for paired population-level supervision.
AU - Wu,Y
AU - Rui,H
AU - Wang,F
AU - Huang,J
AU - Zhang,Z
AU - Zhang,H
AU - Wang,Z
AU - Yang,G
DO - 10.1109/TMI.2026.3739662
PY - 2026///
TI - Decoupling Multi-Contrast Super-Resolution: Self-Supervised Implicit Re-Representation for Unpaired Cross-Modal Synthesis.
T2 - IEEE Trans Med Imaging
UR - http://dx.doi.org/10.1109/TMI.2026.3739662
UR - https://www.ncbi.nlm.nih.gov/pubmed/42853735
VL - PP
ER -
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