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Titolo:
NOISE PROPERTIES OF THE EM ALGORITHM .2. MONTE-CARLO SIMULATIONS
Autore:
WILSON DW; TSUI BMW; BARRETT HH;
Indirizzi:
UNIV N CAROLINA,DEPT BIOMED ENGN CHAPEL HILL NC 27599 UNIV N CAROLINA,DEPT RADIOL CHAPEL HILL NC 27599 UNIV ARIZONA,DEPT RADIOL TUCSON AZ 85721 UNIV ARIZONA,CTR OPT SCI TUCSON AZ 85724
Titolo Testata:
Physics in medicine and biology
fascicolo: 5, volume: 39, anno: 1994,
pagine: 847 - 871
SICI:
0031-9155(1994)39:5<847:NPOTEA>2.0.ZU;2-T
Fonte:
ISI
Lingua:
ENG
Soggetto:
HOTELLING TRACE CRITERION; IMAGING-SYSTEMS; IMAGES; PET;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Citazioni:
14
Recensione:
Indirizzi per estratti:
Citazione:
D.W. Wilson et al., "NOISE PROPERTIES OF THE EM ALGORITHM .2. MONTE-CARLO SIMULATIONS", Physics in medicine and biology, 39(5), 1994, pp. 847-871

Abstract

In an earlier paper we derived a theoretical formulation for estimating the statistical properties of images reconstructed using the iterative ML-EM algorithm. To gain insight into this complex problem, two levels of approximation were considered in the theory. These techniques revealed the dependence of the variance and covariance of the reconstructed image noise on the source distribution, imaging system transfer function, and iteration number. In this paper a Monte Carlo approach was taken to study the noise properties of the ML-EM algorithm and to test the predictions of the theory. The study also served to evaluate the approximations used in the theory. Simulated data from phantoms were used in the Monte Carlo experiments. The ML-EM statistical properties were calculated from sample averages of a large number of images with different noise realizations. The agreement between the more exact form of the theoretical formulation and the Monte Carlo formulation wasbetter than 10% in most cases examined, and for many situations the agreement was within the expected error of the Monte Carlo experiments. Results from the studies provide valuable information about the noisecharacteristics of ML-EM reconstructed images. Furthermore, the studies demonstrate the power of the theoretical and Monte Carlo approachesfor investigating noise properties of statistical reconstruction algorithms.

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Documento generato il 27/01/20 alle ore 02:04:56