Evaluation of data reduction methods for dynamic PET series based on Monte Carlo techniques and the NCAT phantom

Thireou, T; Rubio Guivernau, JL; Atlamazoglou, V; Ledesma, MJ; Pavlopoulos, S; Santos, A; Kontaxakis, G

dc.contributor.author	Thireou, T	en
dc.contributor.author	Rubio Guivernau, JL	en
dc.contributor.author	Atlamazoglou, V	en
dc.contributor.author	Ledesma, MJ	en
dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Santos, A	en
dc.contributor.author	Kontaxakis, G	en
dc.date.accessioned	2014-03-01T01:24:20Z
dc.date.available	2014-03-01T01:24:20Z
dc.date.issued	2006	en
dc.identifier.issn	0168-9002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17222
dc.subject	Dynamic positron-emission tomography	en
dc.subject	Independent component analysis	en
dc.subject	Principal component analysis	en
dc.subject	Similarity mapping	en
dc.subject.classification	Instruments & Instrumentation	en
dc.subject.classification	Nuclear Science & Technology	en
dc.subject.classification	Physics, Particles & Fields	en
dc.subject.classification	Spectroscopy	en
dc.subject.other	Independent component analysis	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Positron emission tomography	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Respiratory system	en
dc.subject.other	Dynamic positron emission tomography	en
dc.subject.other	Lesions	en
dc.subject.other	Similarity mapping	en
dc.subject.other	Visual inspection	en
dc.subject.other	Data reduction	en
dc.title	Evaluation of data reduction methods for dynamic PET series based on Monte Carlo techniques and the NCAT phantom	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.nima.2006.08.112	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.nima.2006.08.112	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A realistic dynamic positron-emission tomography (PET) thoracic study was generated, using the 4D NURBS-based (non-uniform rational B-splines) cardiac-torso (NCAT) phantom and a sophisticated model of the PET imaging process, simulating two solitary pulmonary nodules. Three data reduction and blind source separation methods were applied to the simulated data: principal component analysis, independent component analysis and similarity mapping. All methods reduced the initial amount of image data to a smaller, comprehensive and easily managed set of parametric images, where structures were separated based on their different kinetic characteristics and the lesions were readily identified. The results indicate that the above-mentioned methods can provide an accurate tool for the support of both visual inspection and subsequent detailed kinetic analysis of the dynamic series via compartmental or noncompartmental models. (c) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment	en
dc.identifier.doi	10.1016/j.nima.2006.08.112	en
dc.identifier.isi	ISI:000243241300053	en
dc.identifier.volume	569	en
dc.identifier.issue	2 SPEC. ISS.	en
dc.identifier.spage	389	en
dc.identifier.epage	393	en