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Titolo:
MEAN-VARIANCE ANALYSIS OF BLOCK-ITERATIVE RECONSTRUCTION ALGORITHMS MODELING 3D DETECTOR RESPONSE IN SPECT
Autore:
LALUSH DS; TSUI BMW;
Indirizzi:
UNIV N CAROLINA,DEPT BIOMED ENGN CHAPEL HILL NC 27514 UNIV N CAROLINA,DEPT RADIOL CHAPEL HILL NC 00000
Titolo Testata:
IEEE transactions on nuclear science
fascicolo: 3, volume: 45, anno: 1998,
parte:, 2
pagine: 1280 - 1287
SICI:
0018-9499(1998)45:3<1280:MAOBRA>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
IMAGE-RECONSTRUCTION; TOMOGRAPHY; EMISSION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
10
Recensione:
Indirizzi per estratti:
Citazione:
D.S. Lalush e B.M.W. Tsui, "MEAN-VARIANCE ANALYSIS OF BLOCK-ITERATIVE RECONSTRUCTION ALGORITHMS MODELING 3D DETECTOR RESPONSE IN SPECT", IEEE transactions on nuclear science, 45(3), 1998, pp. 1280-1287

Abstract

We study the statistical convergence properties of two Cast iterativereconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical Tl-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, we generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results thatwere close to the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the RBI-EM provided a small improvement in the tradeoff between noise level and resolution recovery, primarily in the axial direction, while OS required about half the number of iterations of RBI to reach the same resolution. We conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the ML-EM algorithm, but noise level and speed of convergence dependon the projection model used.

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Documento generato il 20/01/20 alle ore 16:08:38