Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniquesReport as inadecuate

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BioMedical Engineering OnLine

, 6:36

First Online: 03 October 2007Received: 21 March 2007Accepted: 03 October 2007


BackgroundDynamic 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.

MethodsWe 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.

ResultsUsing 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1475-925X-6-36 contains supplementary material, which is available to authorized users.

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Author: Sotiris Pavlopoulos - Trias Thireou - George Kontaxakis - Andres Santos


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