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.

翻译：磁共振成像（MRI）是一种广泛使用的非侵入式成像技术。然而，一个长期存在的挑战在于平衡图像质量与成像速度。这种权衡主要受限于k空间测量，这些测量在空间傅里叶域（k空间）中沿特定轨迹进行。为缩短采集时间，这些测量通常采用欠采样，从而导致图像伪影和质量下降。生成模型通过学习图像分布，可用于从欠采样的k空间数据重建高质量图像。在本研究中，我们提出了用于图像序列的自回归图像扩散（AID）模型，并利用其对后验分布进行采样，以实现加速MRI重建。该算法同时整合了欠采样的k空间数据和先验信息。使用fastMRI数据集训练的模型得到了全面评估。结果表明，AID模型能够稳健地生成序列连贯的图像序列。在3D和动态MRI中，由于学习了图像间的依赖关系，AID能够超越标准扩散模型并减少幻觉伪影。