arXiv · 2102.10501
Non-convex primal-dual algorithm for image reconstruction in spectral CT
Abstract
The work seeks to develop an algorithm for image reconstruction by directly inverting the non-linear data model in spectral CT. Using the non-linear data model, we formulate the image-reconstruction problem as a non-convex optimization program, and develop a non-convex primal-dual (NCPD) algorithm to solve the program. We devise multiple convergence conditions and perform verification studies numerically to demonstrate that the NCPD algorithm can solve the non-convex optimization program and under appropriate data condition, can invert the non-linear data model. Using the NCPD algorithm, we then reconstruct monochromatic images from simulated and real data of numerical and physical phantoms acquired with a standard, full-scan dual-energy configuration. The result of the reconstruction studies shows that the NCPD algorithm can correct accurately for the non-linear beam-hardening effect. Furthermore, we apply the NCPD algorithm to simulated and real data of the numerical and physical phantoms collected with non-standard, short-scan dual-energy configurations, and obtain monochromatic images comparable to those of the standard, full-scan study, thus revealing the potential of the NCPD algorithm for enabling non-standard scanning configurations in spectral CT, where the existing indirect methods are limited.
Explore related subjects
Keep this discovery
Buxin Chen, Zheng Zhang, Dan Xia, Emil Y. Sidky, Xiaochuan Pan. 2021-02-21. Non-convex primal-dual algorithm for image reconstruction in spectral CT. https://arxiv.org/abs/2102.10501
Cite the original work for its findings. Save a collection to share your selection of sources.