NDLI: Fast variance image predictions for quadratically regularized statistical image reconstruction in fan-beam tomography

Content Provider	IEEE Xplore Digital Library
Author	Yingying Zhang Fessier, J.A. Jiang Hsieh
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA (Yingying Zhang; Fessier, J.A.)
Abstract	Accurate predictions of variance can be useful for algorithm analysis and for the design of regularization methods. Computing predicted variances at every pixel using matrix-based approximations is impractical. Even the recently adopted methods that are based on local discrete Fourier approximations are impractical since they would require two 2D FFT calculations for every pixel, particularly for shift-variant systems like fan-beam tomography. This paper describes a new analytical approach to predict the approximate variance maps of images reconstructed by penalized likelihood estimation with quadratic regularization in a fan-beam geometry. This analytical approach requires computation equivalent to one backprojection and some simple summations, so it is computationally practical even for the data sizes in X-ray CT. Simulation results show that it gives accurate predictions of the variance maps. The parallel-beam geometry is a simple special case of the fan-beam analysis.
Starting Page	4
Ending Page	1932
File Size	227773
Page Count	1929
File Format	PDF
ISBN	0780392213
ISSN	10957863
DOI	10.1109/NSSMIC.2005.1596709
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-23
Publisher Place	Puerto Rico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image reconstruction Analysis of variance Algorithm design and analysis Geometry Design methodology Image analysis X-ray imaging Computed tomography Computational modeling Analytical models
Content Type	Text
Resource Type	Article

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