Joint Super-Resolution and Modality Translation Network for Multi-Contrast Arbitrary-Scale Isotropic MRI Reconstruction

Due to time and cost limitations, Magnetic Resonance (MR) imaging often employs anisotropic scanning with large slice spacing and thickness. This causes blurring in views perpendicular to the slices, which adversely affects clinical diagnosis and research. Taking into account the complementary information from the reference modality, deep learning (DL) based multi-contrast methods have become a focal point of research. These methods aim to reconstruct the isotropic target MR image with the auxiliary high-resolution (HR) reference modality. However, most of the methods primarily concentrate on the structural restoration of the target low-resolution (LR) image, neglecting the crucial aspect that the coexisting structural and modality differences between target and reference modalities can impede effective restoration. Additionally, these methods are designed for a fixed upsampling scale, not accounting for the practical scenario of varying slice thickness. In this work, we propose a joint Super-resolution and Modality translation network (SMNet) for multi-contrast arbitrary-scale isotropic MRI reconstruction. The modality translation branch includes the Modality-Specific-Augmented Alignment (MSAA) block, which eliminates modality distribution disparities and enhances modality-specific regions on the reference feature before fusion. And the super-resolution branch employs the Reliability-based Spatial Fusion (RSF) block for the structural restoration of the target LR feature using a reliability prior. The outputs from these two branches are then ensembled to obtain the final reconstructed result. Extensive experiments on both a private dataset and the Brasts2021 dataset demonstrate the effectiveness and generalizability of the proposed method. Our code is available at https://github.com/11710615/smnet.