Multiscale Fusion for Spatially Decoupled Multimodal MRI Super-Resolution Reconstruction

Super-resolution (SR) is primarily tailored for single-modal medical images. While in many applications of magnetic resonance imaging (MRI), multimodal images with diverse parameters are available. Current single-modal SR approaches are limited in fully exploring and exploiting the correlations between these cross-modal images, leading to degraded reconstruction performance. Thus, this article presents a novel multiscale (MS) fusion approach for cross-modal MRI SR reconstruction. The proposed method develops domain-specific convolutions to spatially decouple MRI into different subspaces and task-specific modules for reconstruction. Specifically, a CNN-based framework is constructed to explore the mapping between a low-resolution T2-weighted (LR T2w) image and a high-resolution (HR) T2w one, by incorporating an HR T1-weighted (T1w) image. In the proposed network, the low-frequency filtering modules have been integrated into it to remove the low-frequency components of the HR T1w while extracting its high-frequency information. Therefore, by fusing the detailed features of the HR T1w and the LR T2w at two different scales, the network generates the HR T2w image. Extensive results across benchmark MRI datasets demonstrate the effectiveness of the proposed method in MRI SR reconstruction.