Decoupling Multi-Contrast Super-Resolution: Pairing Unpaired Synthesis with Implicit Representations
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
Magnetic Resonance Imaging (MRI) is critical for clinical diagnostics but is
often limited by long acquisition times and low signal-to-noise ratios,
especially in modalities like diffusion and functional MRI. The multi-contrast
nature of MRI presents a valuable opportunity for cross-modal enhancement,
where high-resolution (HR) modalities can serve as references to boost the
quality of their low-resolution (LR) counterparts-motivating the development of
Multi-Contrast Super-Resolution (MCSR) techniques. Prior work has shown that
leveraging complementary contrasts can improve SR performance; however,
effective feature extraction and fusion across modalities with varying
resolutions remains a major challenge. Moreover, existing MCSR methods often
assume fixed resolution settings and all require large, perfectly paired
training datasets-conditions rarely met in real-world clinical environments. To
address these challenges, we propose a novel Modular Multi-Contrast
Super-Resolution (MCSR) framework that eliminates the need for paired training
data and supports arbitrary upscaling. Our method decouples the MCSR task into
two stages: (1) Unpaired Cross-Modal Synthesis (U-CMS), which translates a
high-resolution reference modality into a synthesized version of the target
contrast, and (2) Unsupervised Super-Resolution (U-SR), which reconstructs the
final output using implicit neural representations (INRs) conditioned on
spatial coordinates. This design enables scale-agnostic and anatomically
faithful reconstruction by bridging un-paired cross-modal synthesis with
unsupervised resolution enhancement. Experiments show that our method achieves
superior performance at 4x and 8x upscaling, with improved fidelity and
anatomical consistency over existing baselines. Our framework demonstrates
strong potential for scalable, subject-specific, and data-efficient MCSR in
real-world clinical settings.
