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Titolo:
Noise characterization of block-iterative reconstruction algorithms: I. Theory
Autore:
Soares, EJ; Byrne, CL; Glick, SJ;
Indirizzi:
Coll Holy Cross, Dept Math & Comp Sci, Worcester, MA 01610 USA Coll Holy Cross Worcester MA USA 01610 Comp Sci, Worcester, MA 01610 USA Univ Massachusetts, Dept Math Sci, Lowell, MA USA Univ Massachusetts Lowell MA USA chusetts, Dept Math Sci, Lowell, MA USA Univ Massachusetts, Sch Med, Div Nucl Med, Worcester, MA USA Univ Massachusetts Worcester MA USA Med, Div Nucl Med, Worcester, MA USA
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 4, volume: 19, anno: 2000,
pagine: 261 - 270
SICI:
0278-0062(200004)19:4<261:NCOBRA>2.0.ZU;2-6
Fonte:
ISI
Lingua:
ENG
Soggetto:
EXPONENTIAL RADON-TRANSFORM; IMAGE-RECONSTRUCTION; MAXIMUM-LIKELIHOOD; FILTERED-BACKPROJECTION; EM ALGORITHM; ATTENUATION; TOMOGRAPHY; EMISSION; SPECT; RESOLUTION;
Keywords:
block-iterative image reconstruction; ML-EM; noise properties; ordered subsets; SMART;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
36
Recensione:
Indirizzi per estratti:
Indirizzo: Soares, EJ Coll Holy Cross, Dept Math & Comp Sci, Worcester, MA 01610 USA Coll Holy Cross Worcester MA USA 01610 Worcester, MA 01610 USA
Citazione:
E.J. Soares et al., "Noise characterization of block-iterative reconstruction algorithms: I. Theory", IEEE MED IM, 19(4), 2000, pp. 261-270

Abstract

Researchers have shown increasing interest in block-iterative image reconstruction algorithms due to the computational and modeling advantages they provide. Although their convergence properties have been well documented, little is known about how they behave in the presence of noise. In this work,we fully characterize the ensemble statistical properties of the rescaled block-iterative expectation-maximization (RBI-EM) reconstruction algorithm and the rescaled block-iterative simultaneous multiplicative algebraic reconstruction technique (RBI-SMART). Also included in the analysis are the special cases of RBI-EM, maximum-likelihood EM (ML-EM) and ordered-subset EM (OS-EM), and the special case of RBI-SMART, SMART, A theoretical formulationstrategy similar to that previously outlined for ML-EM is followed for theRBI methods. The theoretical formulations in this paper rely on one approximation, namely, that the noise in the reconstructed image is small compared to the mean image. In a second paper, the approximation will be justifiedthrough Monte Carlo simulations covering a range of noise levels, iteration points, and subset orderings. The ensemble statistical parameters could then be used to evaluate objective measures of image quality.

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Documento generato il 28/03/20 alle ore 23:15:31