Deep learning has shown the capability to substantially accelerate MRI
reconstruction while acquiring fewer measurements. Recently, diffusion models
have gained burgeoning interests as a novel group of deep learning-based
generative methods. These methods seek to sample data points that belong to a
target distribution from a Gaussian distribution, which has been successfully
extended to MRI reconstruction. In this work, we proposed a Cold
Diffusion-based MRI reconstruction method called CDiffMR. Different from
conventional diffusion models, the degradation operation of our CDiffMR is
based on \textit{k}-space undersampling instead of adding Gaussian noise, and
the restoration network is trained to harness a de-aliaseing function. We also
design starting point and data consistency conditioning strategies to guide and
accelerate the reverse process. More intriguingly, the pre-trained CDiffMR
model can be reused for reconstruction tasks with different undersampling
rates. We demonstrated, through extensive numerical and visual experiments,
that the proposed CDiffMR can achieve comparable or even superior
reconstruction results than state-of-the-art models. Compared to the diffusion
model-based counterpart, CDiffMR reaches readily competing results using only
$1.6 \sim 3.4\%$ for inference time. The code is publicly available at
this https URL

本研究提出了一种名为 CDiffMR 的 MRI 重建方法，利用深度学习中的冷扩散卷积神经网络 (Cold Diffusion-based MRI Reconstruction) 和 k 空间下采样技术，以及合理的初始化和数据一致性条件策略，能够显著加快 MRI 重建速度，并且获得与现有最先进模型可比甚至更好的重建效果。

CDiffMR: 能否替代高斯噪声，采用 K - 空间欠采样进行快速磁共振成像？

CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling  for Fast MRI?

Magnetic resonance imaging (MRI) is an important non-invasive clinical tool
that can produce high-resolution and reproducible images. However, a long
scanning time is required for high-quality MR images, which leads to exhaustion
and discomfort of patients, inducing more artefacts due to voluntary movements
of the patients and involuntary physiological movements. To accelerate the
scanning process, methods by k-space undersampling and deep learning based
reconstruction have been popularised. This work introduced SwinMR, a novel Swin
transformer based method for fast MRI reconstruction. The whole network
consisted of an input module (IM), a feature extraction module (FEM) and an
output module (OM). The IM and OM were 2D convolutional layers and the FEM was
composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D
convolutional layers. The RSTB consisted of a series of Swin transformer layers
(STLs). The shifted windows multi-head self-attention (W-MSA/SW-MSA) of STL was
performed in shifted windows rather than the multi-head self-attention (MSA) of
the original transformer in the whole image space. A novel multi-channel loss
was proposed by using the sensitivity maps, which was proved to reserve more
textures and details. We performed a series of comparative studies and ablation
studies in the Calgary-Campinas public brain MR dataset and conducted a
downstream segmentation experiment in the Multi-modal Brain Tumour Segmentation
Challenge 2017 dataset. The results demonstrate our SwinMR achieved
high-quality reconstruction compared with other benchmark methods, and it shows
great robustness with different undersampling masks, under noise interruption
and on different datasets. The code is publicly available at
this https URL.

采用 Swin transformer 以及 multi-channel loss 的方式用于快速 MRI 重建，在进行了多个比较和消融实验以及对多模态脑肿瘤分割的实践后，得出了良好的重建效果。