Estimation of Input Function from Dynamic PET Brain Data Using Bayesian Blind Source Separation

Ondřej Tichý1 and Václav Šmídl1

  1. Institute of Information Theory and Automation,
    Pod Vodárenskou věží 4, Prague, Czech republic
    {otichy,smidl}@utia.cas.cz

Abstract

Selection of regions of interest in an image sequence is a typical prerequisite step for estimation of time-activity curves in dynamic positron emission tomography (PET). This procedure is done manually by a human operator and therefore suffers from subjective errors. Another such problem is to estimate the input function. It can be measured from arterial blood or it can be searched for a vascular structure on the images which is hard to be done, unreliable, and often impossible. In this study, we focus on blind source separation methods with no needs of manual interaction. Recently, we developed sparse blind source separation and deconvolution (S-BSS-vecDC) method for separation of original sources from dynamic medical data based on probability modeling and Variational Bayes approximation methodology. In this paper, we extend this method and we apply the methods on dynamic brain PET data and application and comparison of derived algorithms with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly available for download.

Key words

blind source separation, variational Bayes method, dynamic PET, input function, deconvolution

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS141201051T

Publication information

Volume 12, Issue 4 (November 2015)
Special Issue on Recent Advances in Information Processing, Parallel and Distributed Computing
Year of Publication: 2015
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Tichý, O., Šmídl, V.: Estimation of Input Function from Dynamic PET Brain Data Using Bayesian Blind Source Separation. Computer Science and Information Systems, Vol. 12, No. 4, 1273–1287. (2015)