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Magnetic resonance (MR) imaging produces detailed images of organs andtissues with better contrast, but it suffers from a long acquisition time,which makes the image quality vulnerable to say motion artifacts. Recently,many approaches have been developed to reconstruct full-sampled images frompartially observed measurements to accelerate MR imaging. However, mostapproaches focused on reconstruction over a single modality, neglecting thediscovery of correlation knowledge between the different modalities. Here wepropose a Multi-modal Aggregation network for mR Image recOnstruction withauxiliary modality (MARIO), which is capable of discovering complementaryrepresentations from a fully sampled auxiliary modality, with which tohierarchically guide the reconstruction of a given target modality. Thisimplies that our method can selectively aggregate multi-modal representationsfor better reconstruction, yielding comprehensive, multi-scale, multi-modalfeature fusion. Extensive experiments on IXI and fastMRI datasets demonstratethe superiority of the proposed approach over state-of-the-art MR imagereconstruction methods in removing artifacts.

Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality