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Cite Details

Rick Chartrand, "Nonconvex compressive sensing for X-ray CT: an algorithm comparison", in Asilomar Conference on Signals, Systems, and Computers, 2013

Abstract

Compressive sensing makes it possible to reconstruct images from severely underdetermined linear systems. For X-ray CT, this can allow high-quality images to be reconstructed from projections along few angles, reducing patient dose, as well as enable other forms of limited-view tomography such as tomosynthesis. Many previous results have shown that using nonconvex optimization can greatly improve the results obtained from compressive sensing, and several efficient algorithms have been developed for this purpose. In this paper, we examine some recent algorithms for CT image reconstruction that solve nonconvex optimization problems, and compare their reconstruction performance and computational efficiency.

BibTeX Entry

@inproceedings{chartrand-2013-nonconvex2,
author = {Rick Chartrand},
title = {Nonconvex compressive sensing for X-ray CT: an algorithm comparison},
year = {2013},
urlpdf = {http://math.lanl.gov/Research/Publications/Docs/chartrand-2013-nonconvex2.pdf},
booktitle = {Asilomar Conference on Signals, Systems, and Computers}
}