  Computed tomography (CT) involves a patient's exposure to ionizing radiation.
To reduce the radiation dose, we can either lower the X-ray photon count or
down-sample projection views. However, either of the ways often compromises
image quality. To address this challenge, here we introduce an iterative
reconstruction algorithm regularized by a diffusion prior. Drawing on the
exceptional imaging prowess of the denoising diffusion probabilistic model
(DDPM), we merge it with a reconstruction procedure that prioritizes data
fidelity. This fusion capitalizes on the merits of both techniques, delivering
exceptional reconstruction results in an unsupervised framework. To further
enhance the efficiency of the reconstruction process, we incorporate the
Nesterov momentum acceleration technique. This enhancement facilitates superior
diffusion sampling in fewer steps. As demonstrated in our experiments, our
method offers a potential pathway to high-definition CT image reconstruction
with minimized radiation.
