  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.
