Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities to aid in radiological decision-making. Given the need for lowering the time cost of multiple acquisitions, current deep accelerated MRI reconstruction networks focus on exploiting the redundancy between multiple contrasts. However, existing works are largely supervised with paired data and/or prohibitively expensive fully-sampled MRI sequences. Further, reconstruction networks typically rely on convolutional architectures which are limited in their capacity to model long-range interactions and may lead to suboptimal recovery of fine anatomical detail. To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction. 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-MRI information sharing. We further present a dual-domain (image and k-space) self-supervised learning strategy for DCCT to alleviate the costs of acquiring fully sampled training data. DSFormer generates high-fidelity reconstructions which experimentally outperform current fully-supervised baselines. Moreover, we find that DSFormer achieves nearly the same performance when trained either with full supervision or with our proposed dual-domain self-supervision.

翻译：多孔多采MRI(MC-MRI)捕捉多种互补成像模式,以帮助辐射决策。鉴于需要降低多次收购的时间成本,目前深层加速的MRI重建网络侧重于利用多重对比的冗余。然而,现有工程在很大程度上由配对数据和(或)过于昂贵的全抽版MRI序列来监督。此外,重建网络通常依赖在模拟长距离互动能力上受到限制并可能导致细解剖细节恢复不尽理想的革命结构。为了达到这些目的,我们为加速MC-MRI重建提供了一种双重多部自上型自上型监督变压器。DSFormer开发了一种深层的有条件的连锁变压器(DCCT),由几个连锁Swinerver变压器(SwinRN)组成,在两种深层调节战略下培训,以便能够共享MC-MRI的信息交流。我们还提出了一种双层(模范和K-空间)自上级的自上调学习战略。我们为DCCT提出了一种双向的双向(模拟和K-space)自我校准的学习战略,以降低获得完全抽样的升级的升级的升级的升级的升级的SDFID-SDFRADFDMDMD 的自我重建成本。