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。

快速可控扩散模型用于少样本 MRI 重建