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 this https URL

通过整合扩张注意力机制和卷积网络，我们提出了一种基于窗口的变换器网络，用于加速 MRI 图像重建，并通过自我监督学习进行训练。实验证明，相比于其他架构和并行域自我监督学习基准，所提出的模型在 PSNR 上平均提高了 1.40 dB，在 SSIM 上平均提高了 0.028，并且在 PSNR 上相对于并行域自我监督学习提高了 1.44 dB，在 SSIM 上提高了 0.029。

SDLFormer：一种稀疏和密集的增强局部性 Transformer 用于加速 MR 图像重建

SDLFormer: A Sparse and Dense Locality-enhanced Transformer for  Accelerated MR Image Reconstruction

We propose to formulate MRI image reconstruction as an optimization problem
and model the optimization trajectory as a dynamic process using ordinary
differential equations (ODEs). We model the dynamics in ODE with a neural
network and solve the desired ODE with the off-the-shelf (fixed) solver to
obtain reconstructed images. We extend this model and incorporate the knowledge
of off-the-shelf ODE solvers into the network design (learned solvers). We
investigate several models based on three ODE solvers and compare models with
fixed solvers and learned solvers. Our models achieve better reconstruction
results and are more parameter efficient than other popular methods such as
UNet and cascaded CNN. We introduce a new way of tackling the MRI
reconstruction problem by modeling the continuous optimization dynamics using
neural ODEs.

本文提出将 MRI 图像重建问题作为一种优化问题，使用神经 ODE 模型其优化轨迹，并探究了基于三种 ODE 求解的模型，相比 UNet 和级联 CNN 等其他方法，模型表现更好，参数效率更高，从而引入一种新的通过神经 ODE 建模连续优化动力学的 MRI 重建方式。

利用神经常微分学习优化进行 MRI 图像重建

MRI Image Reconstruction via Learning Optimization Using Neural ODEs

Iterative learning to infer approaches have become popular solvers for
inverse problems. However, their memory requirements during training grow
linearly with model depth, limiting in practice model expressiveness. In this
work, we propose an iterative inverse model with constant memory that relies on
invertible networks to avoid storing intermediate activations. As a result, the
proposed approach allows us to train models with 400 layers on 3D volumes in an
MRI image reconstruction task. In experiments on a public data set, we
demonstrate that these deeper, and thus more expressive, networks perform
state-of-the-art image reconstruction.

本文提出了一种具有恒定内存的迭代逆模型，利用可逆网络避免存储中间激活数据，可以在 MRI 图像重建任务中训练具有 400 层的模型，并在公共数据集上证明这些更深、更具表现力的网络实现了最先进的图像重建。