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Multi-contrast MRI (MC-MRI) captures multiple complementary imagingmodalities to aid in radiological decision-making. Given the need for loweringthe time cost of multiple acquisitions, current deep accelerated MRIreconstruction networks focus on exploiting the redundancy between multiplecontrasts. However, existing works are largely supervised with paired dataand/or prohibitively expensive fully-sampled MRI sequences. Further,reconstruction networks typically rely on convolutional architectures which arelimited in their capacity to model long-range interactions and may lead tosuboptimal recovery of fine anatomical detail. To these ends, we present adual-domain self-supervised transformer (DSFormer) for accelerated MC-MRIreconstruction. DSFormer develops a deep conditional cascade transformer (DCCT)consisting of several cascaded Swin transformer reconstruction networks(SwinRN) trained under two deep conditioning strategies to enable MC-MRIinformation sharing. We further present a dual-domain (image and k-space)self-supervised learning strategy for DCCT to alleviate the costs of acquiringfully sampled training data. DSFormer generates high-fidelity reconstructionswhich experimentally outperform current fully-supervised baselines. Moreover,we find that DSFormer achieves nearly the same performance when trained eitherwith full supervision or with our proposed dual-domain self-supervision.

DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction