Blind source separation for the computational analysis of dynamic oncological PET studies

  • Authors:
    • Trias Thireou
    • Sotiris Pavlopoulos
    • George Kontaxakis
    • Andres Santos
  • View Affiliations

  • Published online on: April 1, 2006     https://doi.org/10.3892/or.15.4.1007
  • Pages: 1007-1012
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Abstract

The analysis of dynamic positron emission tomography (PET) studies provides clinically useful parametric information, but often requires complex and time-consuming compartmental or non-compartmental techniques. Independent component analysis (ICA), a statistical method used for feature extraction and signal separation, is applied to dynamic PET studies to facilitate the initial interpretation and visual analysis of these large image sequences. ICA produces parametric images, where structures with different kinetic characteristics are assigned opposite values and readily discriminated, improving the identification of lesions and facilitating the posterior detailed kinetic analysis.

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April 2006
Volume 15 Issue 4

Print ISSN: 1021-335X
Online ISSN:1791-2431

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Spandidos Publications style
Thireou T, Pavlopoulos S, Kontaxakis G and Santos A: Blind source separation for the computational analysis of dynamic oncological PET studies. Oncol Rep 15: 1007-1012, 2006
APA
Thireou, T., Pavlopoulos, S., Kontaxakis, G., & Santos, A. (2006). Blind source separation for the computational analysis of dynamic oncological PET studies. Oncology Reports, 15, 1007-1012. https://doi.org/10.3892/or.15.4.1007
MLA
Thireou, T., Pavlopoulos, S., Kontaxakis, G., Santos, A."Blind source separation for the computational analysis of dynamic oncological PET studies". Oncology Reports 15.4 (2006): 1007-1012.
Chicago
Thireou, T., Pavlopoulos, S., Kontaxakis, G., Santos, A."Blind source separation for the computational analysis of dynamic oncological PET studies". Oncology Reports 15, no. 4 (2006): 1007-1012. https://doi.org/10.3892/or.15.4.1007