Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
Journal:
arXiv
Published Date:
Apr 10, 2025
Abstract
Sparse-view sampling in dual-energy computed tomography (DECT) significantly
reduces radiation dose and increases imaging speed, yet is highly prone to
artifacts. Although diffusion models have demonstrated potential in effectively
handling incomplete data, most existing methods in this field focus on the
image do-main and lack global constraints, which consequently leads to
insufficient reconstruction quality. In this study, we propose a dual-domain
virtual-mask in-formed diffusion model for sparse-view reconstruction by
leveraging the high inter-channel correlation in DECT. Specifically, the study
designs a virtual mask and applies it to the high-energy and low-energy data to
perform perturbation operations, thus constructing high-dimensional tensors
that serve as the prior information of the diffusion model. In addition, a
dual-domain collaboration strategy is adopted to integrate the information of
the randomly selected high-frequency components in the wavelet domain with the
information in the projection domain, for the purpose of optimizing the global
struc-tures and local details. Experimental results indicated that the present
method exhibits excellent performance across multiple datasets.
