Kinetic Modeling of the Dynamic PET Brain Data Using Blind Source Separation Methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F14%3A00221052" target="_blank" >RIV/68407700:21340/14:00221052 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985556:_____/14:00433424

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Kinetic Modeling of the Dynamic PET Brain Data Using Blind Source Separation Methods

Popis výsledku v původním jazyce

Image-based definition of regions of interest 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. We apply the methods on dynamic brain PET data and application and comparison of our S-BSS-vecDC algorithm with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly avai

Název v anglickém jazyce

Kinetic Modeling of the Dynamic PET Brain Data Using Blind Source Separation Methods

Popis výsledku anglicky

Image-based definition of regions of interest 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. We apply the methods on dynamic brain PET data and application and comparison of our S-BSS-vecDC algorithm with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly avai

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-29225S" target="_blank" >GA13-29225S: Slepá dekonvoluce obrazu v limitních podmínkách</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

The 2014 7th International Conference on BioMedical Engineering and Informatics

ISBN

978-1-4799-5837-5

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

244-249

Název nakladatele

IEEE

Místo vydání

Beijing

Místo konání akce

Dalian

Datum konání akce

14. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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