Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT
This addresses the challenge of reducing radiation exposure in CT scans while maintaining image quality, which is incremental as it builds on existing diffusion models and iterative methods.
The paper tackles the problem of low-dose CT image reconstruction by introducing an iterative reconstruction algorithm regularized by a diffusion prior, which delivers exceptional reconstruction results in an unsupervised framework, as demonstrated in experiments showing potential for high-definition CT images with minimized radiation.
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.