Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general, and for clinical applications in particular, is slow sampling. Due to their iterative nature, the number of function evaluations (NFE) required is usually on the order of $10-10^3$, both for conditional and unconditional generation. In this paper, we present posterior sampling Poisson flow generative models (PPFM), a novel image denoising technique for low-dose and photon-counting CT that produces excellent image quality whilst keeping NFE=1. Updating the training and sampling processes of Poisson flow generative models (PFGM)++, we learn a conditional generator which defines a trajectory between the prior noise distribution and the posterior distribution of interest. We additionally hijack and regularize the sampling process to achieve NFE=1. Our results shed light on the benefits of the PFGM++ framework compared to diffusion models. In addition, PPFM is shown to perform favorably compared to current state-of-the-art diffusion-style models with NFE=1, consistency models, as well as popular deep learning and non-deep learning-based image denoising techniques, on clinical low-dose CT images and clinical images from a prototype photon-counting CT system.

翻译：扩散模型与泊松流模型在包括低剂量CT图像去噪在内的广泛生成任务中展现出卓越性能。然而，其在临床应用中普遍存在采样速度慢的局限性。由于迭代特性，无论是有条件生成还是无条件生成，所需函数评估次数通常处于$10-10^3$量级。本文提出后验采样泊松流生成模型（PPFM），一种面向低剂量及光子计数CT的新型图像去噪技术，在保持NFE=1的前提下实现优异图像质量。通过改进泊松流生成模型（PFGM）++的训练与采样过程，我们学习了一个定义先验噪声分布与目标后验分布之间轨迹的条件生成器。此外，我们通过劫持并正则化采样过程实现NFE=1。实验结果揭示了PFGM++框架相较于扩散模型的优势。同时，在临床低剂量CT图像及原型光子计数CT系统采集的临床图像上，PPFM相比当前NFE=1的最先进扩散模型、一致性模型以及主流的深度学习与非深度学习图像去噪技术均展现出更优性能。