@inproceedings{1301aa8e83644115b797e0d873c26940,
title = "Maximum cross-entropy generalized series reconstruction",
abstract = "This article addresses the classical image reconstruction problem from limited Fourier data. In particular, we deal with the issue of how to incorporate constraints provided in the form of a high-resolution reference image which approximates the desired image. A new algorithm is described which represents the desired image using a family of basis functions derived from translated and rotated versions of the reference image. The selection of the most efficient basis function set from this family is guided by the principle of maximum cross-entropy. Simulation and experimental results have shown that the algorithm can achieve high resolution with a small number of data points while accounting for relative misregistration between the reference and measured data. The technique proves to be useful for a number of time-sequential magnetic resonance imaging applications, for which significant improvement in temporal resolution can be obtained, even as the object undergoes bulk motion during the acquisition.",
keywords = "generalized series, image representation, maximum cross-entropy",
author = "Hess, {C. P.} and Liang, {Zhi Pei} and Lauterbur, {P. C.}",
note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 5th IEEE EMBS International Summer School on Biomedical Imaging, SSBI 2002 ; Conference date: 15-06-2002 Through 23-06-2002",
year = "2002",
doi = "10.1109/SSBI.2002.1233979",
language = "English (US)",
series = "Biomedical Imaging V - Proceedings of the 5th IEEE EMBS International Summer School on Biomedical Imaging, SSBI 2002",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "258--265",
editor = "Jean-Louis Coatrieux and Christian Roux",
booktitle = "Biomedical Imaging V - Proceedings of the 5th IEEE EMBS International Summer School on Biomedical Imaging, SSBI 2002",
address = "United States",
}