Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

By Emil Y. Sidky, Jakob Heide Jørgensen, Xiaochuan Pan. Physics in Medicine and Biology, 2011

416 citations recorded by the source corpus.

The primal–dual optimization algorithm developed in Chambolle and Pock (CP) (2011 J. Math. Imag. Vis. 40 1–26) 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 this paper, 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.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9a84e3ce2da20bbf7bb251f701c63b1e7fa2dd4b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Primal–Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms (2012, background): “Solving ill-posed inverse problems in medical imaging by regularization is an example of high practical interest [1].”
Learned Primal-Dual Reconstruction (2017, methodology): “Example (Total Variation (TV) Regularized CT): The PDHG method has been widely applied to CT [19].”
The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography. (2015, methodology): “Assume that we have a MATLAB script that implements the Chambolle–Pock optimization strategy [31] for solving (7).”