Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm
For researchers in CT imaging, this work provides a flexible prototyping framework for iterative reconstruction algorithms, though it is incremental as it applies an existing algorithm to known problems.
The paper applies the Chambolle-Pock primal-dual algorithm to convex optimization problems for CT image reconstruction, enabling rapid prototyping. In a breast CT example with low-intensity X-rays, the method demonstrates effective reconstruction.
The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented.