Magnetic resonance imaging (MRI) is a widely used non-invasive imaging
modality. However, a persistent challenge lies in balancing image quality with
imaging speed. This trade-off is primarily constrained by k-space measurements,
which traverse specific trajectories in the spatial Fourier domain (k-space).
These measurements are often undersampled to shorten acquisition times,
resulting in image artifacts and compromised quality. Generative models learn
image distributions and can be used to reconstruct high-quality images from
undersampled k-space data. In this work, we present the autoregressive image
diffusion (AID) model for image sequences and use it to sample the posterior
for accelerated MRI reconstruction. The algorithm incorporates both
undersampled k-space and pre-existing information. Models trained with fastMRI
dataset are evaluated comprehensively. The results show that the AID model can
robustly generate sequentially coherent image sequences. In 3D and dynamic MRI,
the AID can outperform the standard diffusion model and reduce hallucinations,
due to the learned inter-image dependencies.

非侵入性成像模式中，磁共振成像是一种广泛使用的成像方式，但其图像质量和成像速度之间存在平衡难题。本文介绍了一种基于自回归图像扩散（AID）模型的加速 MRI 重建方法，该算法结合了未采样的 k 空间和现有信息，评估了 fastMRI 数据集训练的模型，并展示了 AID 模型能够稳健地生成时序连贯的图像序列，在 3D 和动态 MRI 中的表现优于标准扩散模型，还能减少幻觉，并学习图像间的依赖关系。