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Titolo:
Fast maximum entropy approximation in SPECT using the RBI-MAP algorithm
Autore:
Lalush, DS; Frey, EC; Tsui, BMW;
Indirizzi:
Univ N Carolina, Dept Biomed Engn, Chapel Hill, NC 27599 USA Univ N Carolina Chapel Hill NC USA 27599 Engn, Chapel Hill, NC 27599 USA Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA Univ N Carolina Chapel Hill NC USA 27599 adiol, Chapel Hill, NC 27599 USA
Titolo Testata:
IEEE TRANSACTIONS ON MEDICAL IMAGING
fascicolo: 4, volume: 19, anno: 2000,
pagine: 286 - 294
SICI:
0278-0062(200004)19:4<286:FMEAIS>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Soggetto:
ITERATIVE RECONSTRUCTION ALGORITHMS; IMAGE-RECONSTRUCTION; EMISSION TOMOGRAPHY; EM ALGORITHM; BAYESIAN RECONSTRUCTION; SCATTER COMPENSATION; RESPONSE CORRECTION; DETECTOR RESPONSE; FEASIBLE IMAGES; LEAST-SQUARES;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Clinical Medicine
Engineering, Computing & Technology
Citazioni:
39
Recensione:
Indirizzi per estratti:
Indirizzo: Lalush, DS Univ N Carolina, Dept Biomed Engn, Chapel Hill, NC 27599 USA Univ N Carolina Chapel Hill NC USA 27599 el Hill, NC 27599 USA
Citazione:
D.S. Lalush et al., "Fast maximum entropy approximation in SPECT using the RBI-MAP algorithm", IEEE MED IM, 19(4), 2000, pp. 286-294

Abstract

In this work, we present a method for approximating constrained maximum entropy (ME) reconstructions of SPECT data with modifications to a block-iterative maximum a posteriori (MAP) algorithm, Maximum likelihood (ML)-based reconstruction algorithms require some form of noise smoothing. Constrained ME provides a more formal method of noise smoothing without requiring the user to select parameters. In the context of SPECT, constrained ME seeks theminimum-information image estimate among those whose projections are a given distance from the noisy measured data, with that distance determined by the magnitude of the Poisson noise. Images that meet the distance criterionare referred to as feasible images, We find that modeling of all principaldegrading factors (attenuation, detector response, and scatter) in the reconstruction is critical because feasibility is not meaningful unless the projection model is as accurate as possible. Because the constrained ME solution is the same as a MAP solution for a particular value of the MAP weighting parameter, beta, the constrained ME solution can be found with a MAP algorithm if the correct value of beta is found. We show that the RBI-MAP algorithm, if used with a dynamic scheme for estimating beta, can approximate constrained ME solutions in 20 or fewer iterations, We compare results for various methods of achieving feasible images on a simulation of Tl-201 cardiac SPECT data. Results show that the RBI-MAP ME approximation provides images and quantitative estimates close to those from a slower algorithm that gives the true ME solution, Also, we find that the ME results have higher spatial resolution and greater high-frequency noise content than a feasibility-based stopping rule, feasibility-based tow-pass filtering, and a quadratic Gibbs prior with beta selected according to the feasibility criterion. Weconclude that fast ME approximation is possible using either RBI-MAP with the dynamic procedure or a feasibility-based stopping rule, and that such reconstructions may be particularly useful in applications where resolution is critical.

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Documento generato il 17/01/20 alle ore 20:39:35