Supervised deep learning methods have shown promise in Magnetic Resonance
Imaging (MRI) undersampling reconstruction, but their requirement for paired
data limits their generalizability to the diverse MRI acquisition parameters.
Recently, unsupervised controllable generative diffusion models have been
applied to MRI undersampling reconstruction, without paired data or model
retraining for different MRI acquisitions. However, diffusion models are
generally slow in sampling and state-of-the-art acceleration techniques can
lead to sub-optimal results when directly applied to the controllable
generation process. This study introduces a new algorithm called
Predictor-Projector-Noisor (PPN), which enhances and accelerates controllable
generation of diffusion models for MRI undersampling reconstruction. Our
results demonstrate that PPN produces high-fidelity MR images that conform to
undersampled k-space measurements with significantly shorter reconstruction
time than other controllable sampling methods. In addition, the unsupervised
PPN accelerated diffusion models are adaptable to different MRI acquisition
parameters, making them more practical for clinical use than supervised
learning techniques.

该研究介绍了一种名为 PPN（Predictor-Projector-Noisor）的新算法，它增强并加速了 MRI 低采样重建的可控生成扩散模型，结果表明 PPN 比其他可控采样方法更快速地产生与低采样 k 空间测量一致的高保真 MR 图像，无监督 PPN 加速的扩散模型适应不同的 MRI 采集参数，比监督学习技术更适用于临床使用。