Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising

作者：Xuan Liu, Yaoqin Xie, Chenbin Liu, Jun Cheng, Songhui Diao, Shan Tan, Xiaokun Liang · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17431 · 被引用次数：41 · 研究领域：Image and Signal Denoising Methods、Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging

BACKGROUND: Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically require pairs of low-dose and normal-dose CT images for training, which are challenging to obtain in clinical settings. Existing unsupervised deep learning-based methods often require training with a large number of low-dose CT images or rely on specially designed data acquisition processes to obtain training data. PURPOSE: To address these limitations, we propose a novel unsupervised method that only utilizes normal-dose CT images during training, enabling zero-shot denoising of low-dose CT images. METHODS: Our method leverages the diffusion model, a powerful generative model. We begin by training a cascaded unconditional diffusion model capable of generating high-quality normal-dose CT images from low-resolution to high-resolution. The cascaded architecture makes the training of high-resolution diffusion models more feasible. Subsequently, we introduce low-dose CT images into the reverse process of the diffusion model as likelihood, combined with the priors provided by the diffusion model and iteratively solve multiple maximum a posteriori (MAP) problems to achieve denoising. Additionally, we propose methods to adaptively adjust the coefficients that balance the likelihood and prior in MAP estimations, allowing for adaptation to...