Estimation of Input Function from Dynamic PET Brain Data Using Bayesian Blind Source Separation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00450509" target="_blank" >RIV/67985556:_____/15:00450509 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.2298/CSIS141201051T" target="_blank" >http://dx.doi.org/10.2298/CSIS141201051T</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.2298/CSIS141201051T" target="_blank" >10.2298/CSIS141201051T</a>
Alternative languages
Result language
angličtina
Original language name
Estimation of Input Function from Dynamic PET Brain Data Using Bayesian Blind Source Separation
Original language description
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 focuson 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.
Czech name
—
Czech description
—
Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—
Result continuities
Project
<a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computer Science and Information Systems
ISSN
1820-0214
e-ISSN
—
Volume of the periodical
12
Issue of the periodical within the volume
4
Country of publishing house
RS - THE REPUBLIC OF SERBIA
Number of pages
15
Pages from-to
1273-1287
UT code for WoS article
000366127000008
EID of the result in the Scopus database
2-s2.0-84947214904