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Titolo:
Variance in parametric images: direct estimation from parametric projections
Autore:
Maguire, RP; Spyrou, NM; Leenders, KL;
Indirizzi:
Univ Groningen, Groningen Neuroimaging Project, NL-9700 RB Groningen, Netherlands Univ Groningen Groningen Netherlands NL-9700 RB B Groningen, Netherlands Univ Groningen Hosp, AZG Neurol V4 121, NL-9700 RB Groningen, Netherlands Univ Groningen Hosp Groningen Netherlands NL-9700 RB ningen, Netherlands Univ Surrey, Dept Phys, Guildford GU2 5XH, Surrey, England Univ Surrey Guildford Surrey England GU2 5XH ord GU2 5XH, Surrey, England
Titolo Testata:
PHYSICS IN MEDICINE AND BIOLOGY
fascicolo: 1, volume: 45, anno: 2000,
pagine: 91 - 102
SICI:
0031-9155(200001)45:1<91:VIPIDE>2.0.ZU;2-4
Fonte:
ISI
Lingua:
ENG
Soggetto:
POSITRON EMISSION TOMOGRAPHY; NOISE PROPERTIES; EM ALGORITHM; TIME; RECONSTRUCTION; BRAIN;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Citazioni:
18
Recensione:
Indirizzi per estratti:
Indirizzo: Maguire, RP Univ Groningen, Groningen Neuroimaging Project, NL-9700 RB Groningen, Netherlands Univ Groningen Groningen Netherlands NL-9700 RB Netherlands
Citazione:
R.P. Maguire et al., "Variance in parametric images: direct estimation from parametric projections", PHYS MED BI, 45(1), 2000, pp. 91-102

Abstract

Recent work has shown that it is possible to apply linear kinetic models to dynamic projection data in PET in order to calculate parameter projections. These can subsequently be back-projected to form parametric images-maps of parameters of physiological interest. Critical to the application of these maps, to lest for significant changes between normal and pathophysiology, is an assessment of the statistical uncertainty. In this context, parametric images also include simple integral images from, e.g., [O-15]-water used to calculate statistical parametric maps (SPMs). This paper revisits the concept of parameter projections and presents a more general formulation ofthe parameter projection derivation as well as a method to estimate parameter variance in projection space, showing which analysis methods (models) can be used. Using simulated pharmacokinetic image data we show that a method based on an analysis in projection space inherently calculates the mathematically rigorous pixel variance. This results in an estimation which is asaccurate as either estimating variance in image space during model fitting, or estimation by comparison across sets of parametric images-as might be done between individuals in a group pharmacokinetic PET study. The method based on projections has, however, a higher computational efficiency, and isalso shown to be more precise, as reflected in smooth variance distribution images when compared to the other methods.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 11/08/20 alle ore 15:41:25