A lightweight progressive aggregation network for multi-contrast MRI super-resolution

Magnetic resonance imaging (MRI) provides diverse perspectives on anatomical structures, enabling multi-contrast super-resolution (SR) techniques that leverage complementary information across modalities to significantly enhance image quality. However, most existing multi-contrast SR methods are computationally intensive and lack lightweight solutions. In this study, we propose a novel lightweight progressive aggregation network (PAN) architecture for multi-contrast MRI SR. Our approach introduces multi-perception and residual feature aggregation mechanisms, which effectively capture and integrate anatomical details from low-resolution and reference images. Extensive experiments demonstrate that our lightweight method achieves superior efficiency, significantly outperforming other multi-contrast MRI SR methods in experiments, offering a promising solution for resource-constrained multi-contrast MRI super-resolution scenarios where computational efficiency is critical. The code can be found at https://github.com/Huaibei-normal-university-cv-laboratory/PAN .