Data reduction techniques for the analysis and interpretation of dynamic FDG-PET oncological studies

Kontaxakis, G; Thireou, T; Pavlopoulos, S; Santos, A

dc.contributor.author	Kontaxakis, G	en
dc.contributor.author	Thireou, T	en
dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Santos, A	en
dc.date.accessioned	2014-03-01T02:42:33Z
dc.date.available	2014-03-01T02:42:33Z
dc.date.issued	2004	en
dc.identifier.issn	10957863	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31045
dc.subject	Data Reduction	en
dc.subject	Data Reduction Techniques	en
dc.subject	Image Sequence	en
dc.subject	Kinetic Model	en
dc.subject	Kinetics	en
dc.subject	Positron Emission Tomography	en
dc.subject	Principal Component Analysis	en
dc.subject	Similarity Measure	en
dc.subject	Standardized Uptake Value	en
dc.subject	Visual Analysis	en
dc.subject	Arterial Input Function	en
dc.subject	Time Activity Curve	en
dc.subject.other	Image sequences	en
dc.subject.other	Standardized uptake values (SUV)	en
dc.subject.other	Visual analysis	en
dc.subject.other	Diagnosis	en
dc.subject.other	Eigenvalues and eigenfunctions	en
dc.subject.other	Image reconstruction	en
dc.subject.other	Information retrieval	en
dc.subject.other	Mapping	en
dc.subject.other	Matrix algebra	en
dc.subject.other	Oncology	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Positron emission tomography	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Tumors	en
dc.subject.other	Data reduction	en
dc.title	Data reduction techniques for the analysis and interpretation of dynamic FDG-PET oncological studies	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NSSMIC.2004.1466678	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NSSMIC.2004.1466678	en
heal.identifier.secondary	M9-339	en
heal.publicationDate	2004	en
heal.abstract	Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted with the use of tracer kinetic methods. In order to facilitate the initial interpretation and visual analysis of these large image sequences, data reduction methods can be applied which at the same time maintain important information and allow basic feature characterization. We show here that the application of principal component analysis can provide high-contrast parametric image sets of lesser dimension than the original ones separating structures with different kinetic characteristics. This method has been shown to be an alternative quantification method, independent of any kinetic model and particularly useful when the retrieval of the arterial input function is complicated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporary properties of the whole image sequence according to a reference region. Based on the newly introduced cubed sum coefficient similarity measure, we show that structures with similar time activity curves similar to the tumor's ones can be identified, thus facilitating the detection of lesions not easily discriminated with the conventional method using standardized uptake values. © 2004 IEEE.	en
heal.journalName	IEEE Nuclear Science Symposium Conference Record	en
dc.identifier.doi	10.1109/NSSMIC.2004.1466678	en
dc.identifier.volume	6	en
dc.identifier.spage	3673	en
dc.identifier.epage	3677	en