부산대학교 도서관

Multi-contrast high-resolution (HR) magnetic resonance (MR) images enrich available information for diagnosis and analysis. However, the resolution of MR images is typically low due to the limitations of hardware conditions and scanning time. Although convolutional neural network (CNN)-based and Transformer-based super-resolution (SR) methods are effective in improving the resolution and sharpness of images, most SR methods for multi-contrast magnetic resonance imaging (MRI) still have the following shortcomings. Firstly, most CNN-based methods are deficient in capturing global information, which is essential for regions with complicated anatomical structures. Secondly, although numerous Transformer-based methods capture long-range dependencies in the spatial dimension, they neglect the self-attention in the channel dimension, which is also important for low-level vision tasks. To address the above issues, we propose a novel dual-domain and dual-aggregation Transformer (DDFormer) for multi-contrast MRI SR. The dual-domain detail-enhanced Transformer (DDT) generates global features of the target domain; the dual-aggregation Transformer (DAT) effectively captures long-range dependencies in both spatial and channel dimensions. Specifically, we design DDT to model long-range dependencies in both reference and target images, so that the proposed DDFormer restores sharp structures and natural textures. Moreover, we propose a Channel-wise-Spatial Locally-enhanced Self-Attention layer to construct DAT to capture local features within a patch as well as global contextual features between patches in a single-channel feature and deeply aggregate semantic information of multiple protocols for MR images. Extensive experiments verify the effectiveness of our DDFormer, which outperforms state-of-the-art methods on benchmarks quantitatively and visually.