Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques

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

dc.contributor.author	Pavlopoulos, S	en
dc.contributor.author	Thireou, T	en
dc.contributor.author	Kontaxakis, G	en
dc.contributor.author	Santos, A	en
dc.date.accessioned	2014-03-01T01:25:54Z
dc.date.available	2014-03-01T01:25:54Z
dc.date.issued	2007	en
dc.identifier.issn	1475-925X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17809
dc.subject	Data Reduction	en
dc.subject	Data Reduction Techniques	en
dc.subject	Image Sequence	en
dc.subject	Independent Component Analysis	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	Temporal Properties	en
dc.subject	Visual Analysis	en
dc.subject	Arterial Input Function	en
dc.subject	Time Activity Curve	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.other	Cubed sum coefficient similarity measure	en
dc.subject.other	Feature characterization	en
dc.subject.other	Tracer kinetic method	en
dc.subject.other	Data reduction	en
dc.subject.other	Edge detection	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image analysis	en
dc.subject.other	Image retrieval	en
dc.subject.other	Mapping	en
dc.subject.other	Oncology	en
dc.subject.other	Principal component analysis	en
dc.subject.other	Positron emission tomography	en
dc.subject.other	fluorodeoxyglucose f 18	en
dc.subject.other	tracer	en
dc.subject.other	diagnostic agent	en
dc.subject.other	radiopharmaceutical agent	en
dc.subject.other	article	en
dc.subject.other	clinical article	en
dc.subject.other	clinical trial	en
dc.subject.other	colorectal tumor	en
dc.subject.other	contrast enhancement	en
dc.subject.other	diagnostic accuracy	en
dc.subject.other	diagnostic imaging	en
dc.subject.other	diagnostic value	en
dc.subject.other	histopathology	en
dc.subject.other	human	en
dc.subject.other	image analysis	en
dc.subject.other	image enhancement	en
dc.subject.other	image quality	en
dc.subject.other	mathematical computing	en
dc.subject.other	positron emission tomography	en
dc.subject.other	quantitative analysis	en
dc.subject.other	algorithm	en
dc.subject.other	artificial intelligence	en
dc.subject.other	computer assisted diagnosis	en
dc.subject.other	methodology	en
dc.subject.other	neoplasm	en
dc.subject.other	principal component analysis	en
dc.subject.other	reproducibility	en
dc.subject.other	scintiscanning	en
dc.subject.other	sensitivity and specificity	en
dc.subject.other	statistical analysis	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Data Interpretation, Statistical	en
dc.subject.other	Fluorodeoxyglucose F18	en
dc.subject.other	Humans	en
dc.subject.other	Image Enhancement	en
dc.subject.other	Image Interpretation, Computer-Assisted	en
dc.subject.other	Neoplasms	en
dc.subject.other	Positron-Emission Tomography	en
dc.subject.other	Principal Component Analysis	en
dc.subject.other	Radiopharmaceuticals	en
dc.subject.other	Reproducibility of Results	en
dc.subject.other	Sensitivity and Specificity	en
dc.title	Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques	en
heal.type	journalArticle	en
heal.identifier.primary	10.1186/1475-925X-6-36	en
heal.identifier.secondary	http://dx.doi.org/10.1186/1475-925X-6-36	en
heal.identifier.secondary	36	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Background: Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization. Methods: We have applied principal component analysis to provide high-contrast parametric image sets of lower dimensions than the original data set separating structures based on their kinetic characteristics. Our method has the potential to constitute an alternative quantification method, independent of any kinetic model, and is particularly useful when the retrieval of the arterial input function is complicated. In independent component analysis images, structures that have different kinetic characteristics are assigned opposite values, and are readily discriminated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporal properties of the entire image sequence according to a reference region. Results: Using our new cubed sum coefficient similarity measure, we have shown that structures with similar time activity curves can be identified, thus facilitating the detection of lesions that are not easily discriminated using the conventional method employing standardized uptake values. © 2007 Pavlopoulos et al; licensee BioMed Central Ltd.	en
heal.publisher	BIOMED CENTRAL LTD	en
heal.journalName	BioMedical Engineering Online	en
dc.identifier.doi	10.1186/1475-925X-6-36	en
dc.identifier.isi	ISI:000252922700001	en
dc.identifier.volume	6	en