Transformers have emerged as viable alternatives to convolutional neural networks owing to their ability to learn non-local region relationships in the spatial domain. The self-attention mechanism of the transformer enables transformers to capture long-range dependencies in the images, which might be desirable for accelerated MRI image reconstruction as the effect of undersampling is non-local in the image domain. Despite its computational efficiency, the window-based transformers suffer from restricted receptive fields as the dependencies are limited to within the scope of the image windows. We propose a window-based transformer network that integrates dilated attention mechanism and convolution for accelerated MRI image reconstruction. The proposed network consists of dilated and dense neighborhood attention transformers to enhance the distant neighborhood pixel relationship and introduce depth-wise convolutions within the transformer module to learn low-level translation invariant features for accelerated MRI image reconstruction. The proposed model is trained in a self-supervised manner. We perform extensive experiments for multi-coil MRI acceleration for coronal PD, coronal PDFS and axial T2 contrasts with 4x and 5x under-sampling in self-supervised learning based on k-space splitting. We compare our method against other reconstruction architectures and the parallel domain self-supervised learning baseline. Results show that the proposed model exhibits improvement margins of (i) around 1.40 dB in PSNR and around 0.028 in SSIM on average over other architectures (ii) around 1.44 dB in PSNR and around 0.029 in SSIM over parallel domain self-supervised learning. The code is available at https://github.com/rahul-gs-16/sdlformer.git

翻译：Transformer因其在空间域中学习非局部区域关系的能力，已成为卷积神经网络的有效替代方案。Transformer的自注意力机制使其能够捕捉图像中的长程依赖关系，这对于加速MRI图像重建尤为重要，因为欠采样效应在图像域中是非局部的。尽管窗口化Transformer具有计算效率优势，但其感受野受限，依赖关系局限于图像窗口范围内。我们提出了一种融合膨胀注意力机制与卷积的窗口化Transformer网络，用于加速MRI图像重建。该网络由膨胀注意力Transformer与密集邻域注意力Transformer组成，以增强远距离邻域像素关系，并在Transformer模块中引入深度可分离卷积，从而学习低层平移不变特征以加速MRI图像重建。所提模型采用自监督方式训练。我们基于k空间分割的自监督学习，针对多线圈MRI加速任务进行了广泛实验，涉及冠状位PD、冠状位PDFS及轴向T2对比度，在4倍和5倍欠采样条件下进行验证。我们将该方法与其他重建架构及并行域自监督学习基线进行对比。结果表明，所提模型性能提升显著：（i）相较于其他架构，平均PSNR提升约1.40 dB，SSIM提升约0.028；（ii）相较于并行域自监督学习，PSNR提升约1.44 dB，SSIM提升约0.029。代码已开源：https://github.com/rahul-gs-16/sdlformer.git