Next Article in Journal
Stable Multicommodity Flows
Previous Article in Journal
A Polynomial-Time Algorithm for Computing the Maximum Common Connected Edge Subgraph of Outerplanar Graphs of Bounded Degree
Previous Article in Special Issue
Edge Detection from MRI and DTI Images with an Anisotropic Vector Field Flow Using a Divergence Map
Algorithms 2013, 6(1), 136-160; doi:10.3390/a6010136
Review

Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging

Received: 28 November 2012 / Revised: 18 February 2013 / Accepted: 19 February 2013 / Published: 12 March 2013
(This article belongs to the Special Issue Machine Learning for Medical Imaging)
Download PDF [297 KB, uploaded 12 March 2013]

Abstract

Image reconstruction is a key component in many medical imaging modalities. The problem of image reconstruction can be viewed as a special inverse problem where the unknown image pixel intensities are estimated from the observed measurements. Since the measurements are usually noise contaminated, statistical reconstruction methods are preferred. In this paper we review some non-negatively constrained simultaneous iterative algorithms for maximum penalized likelihood reconstructions, where all measurements are used to estimate all pixel intensities in each iteration.
Keywords: tomographic imaging; penalized likelihood; algorithms; constrained optimization tomographic imaging; penalized likelihood; algorithms; constrained optimization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Ma, J. Algorithms for Non-Negatively Constrained Maximum Penalized Likelihood Reconstruction in Tomographic Imaging. Algorithms 2013, 6, 136-160.

View more citation formats

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert