Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising

TL;DR

This paper introduces an unsupervised diffusion model-based approach for zero-shot denoising of low-dose CT images, requiring only normal-dose images for training and adapting to various noise levels, outperforming existing methods.

Contribution

The authors propose a novel diffusion model-based zero-shot denoising method that trains solely on normal-dose images and adaptively balances likelihood and prior for different noise levels.

Findings

Outperforms state-of-the-art unsupervised denoising methods.

Surpasses several supervised deep learning approaches.

Effective across different regions and dose levels.

Abstract

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. 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. 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 different noise levels in low-dose CT images. We test our method on low-dose CT datasets of different regions with varying dose levels. The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods. Codes are available in https://github.com/DeepXuan/Dn-Dp.