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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

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

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Computer Science and Information Systems

ISSN

1820-0214

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

15

Strana od-do

1273-1287

Kód UT WoS článku

000366127000008

EID výsledku v databázi Scopus

2-s2.0-84947214904