Publication detail

Could it be possible to distinguish bending and crossing fibers in diffusion MRI data?

LABOUNEK, R. MIKL, M. JAKUBÍČEK, R. CHMELÍK, J. JAN, J.

Original Title

Could it be possible to distinguish bending and crossing fibers in diffusion MRI data?

Czech Title

Mohlo by být možné rozlišit ohýbající se a křížící se vlákna v difuzních MRI datech?

English Title

Could it be possible to distinguish bending and crossing fibers in diffusion MRI data?

Type

abstract

Language

en

Original Abstract

After diffusion tensor imaging (DTI) model (Basser et al., 1994), several approaches which are able to detect two or more crossing fibers in diffusion MRI (dMRI) data have been invented (e.g. Q-ball imaging, ball and stick model) (Tuch, 2004 and Behrens et al., 2003). After that, some fiber bundles which had not been seen with DTI model were suddenly observed (e.g. in corpus callosum). Although it brought an improvement it seems that about 50% of detected fiber bundles are false positive results after tractography (Ciccarelli et al., 2008). One crucial problem is that tractography cannot decide if the bundles are crossing or bending because models are not estimating bending-tensor. For 2 crossing fibers, the tractography algorithm can trace from one point to three different places. For 2 bending fibers, there is only one possible way. We would like to introduce how the difference between dMRI data coming from crossing or bending fibers could be detected. Imagine a population of water molecules in the centre of crossing or bending and some applied gradient of diffusion measurement. For crossing fibers, the population of molecules can diffuse in all directions of fiber spreading, thus the phase of molecules can be affected by the whole gradient range. Contrary for one bending fiber, the population can diffuse only in directions of the fiber, thus the phase can be affected only by the narrower gradient range. It applies similarly for second bended fiber. From this point of view, phase distributions should differ for crossing and bending fibers respectively also resulting dMRI data should differ. For this statement testing, the dMRI data simulator which generates dMRI data based on Brownian motion of water molecules inside and outside axons per one voxel volume was created. Although there is several technical problems and aspects (e.g. periodic character of gradient space phase distribution) we are looking for sequence settings of dMRI measurement where the dMRI data would be statistically significantly different for crossing and bending fiber geometries.

Czech abstract

Po difúzně tentorovém modelu (DTI) (Basser et al., 1994) bzlo vyvinuto několik metod (např. Q-ball zobrazování, ball and stick model), které jsou schopny detekovat dvě nebo více křížících se vláken v difúzních MRI datech (Tuch, 2004 and Behrens et al., 2003). Díky čemuž byly najednou pozorovány svazky bílé hmoty, které nebylo možné vidět s DTI modelem (např. v corpus callosum). Ačkoliv to přineslo značné zlepšení, zdá se, že přibližně 50% detekovaných svazků vláken jsou falešně pozitivní výsledky traktografie (Ciccarelli et al., 2008). Jedním z klíčových omezení je, že traktografie není schopná rozhodnout, zda se svazky kříží nebo ohýbají, protože dněšné modely nedokáží odhadnout tenzor ohybu. Pro 2 křížící se vlákna, traktigrafický algoritmus může trasovat z jednoho bodu do tří odlišných míst. Pro dvě ohábající se vlákna existuje jen jedna možná cesta. Chtěli bychom ukázat, jak by rozdíl dMRI dat pocházejících z křížících se čí ohýbajících se vláken mohl být detekován. Představte si populaci molekul vody ve středu křížení nebo ohybu vláken a libovolný gradient magnetického pole aplikovaný během difuzního MR měření. Pro křížící se vlákna, populace molekul vody má možnost difundovat do všech směrů šíření vláken, proto fáze molekul vody může být vlivněna plným rozsahem nehomogenního gradientního magnetického pole. Naproti tomu pro 1 ohýbající se vlákno, populace může difundovat pouze ve 2 směrech určených vláknem. a proto fáze molekul může být ovlivněna pouze užším rozsahem nehomogenního gradientního magnetického pole. Toto samozřejmě obdobně platí i pro druhé ohnuté vlákno. Z tohoto pohledu se dá předpokládat, že výsledné distribuce fáze molekul vody by se měly lišit pro křížící se a ohýbající se vlákna, respektive i náslědně výsledná dMRI data. Pro ověření tohoto tvrzení byl vytvořen simulátor dMRI dat, který je generuje na základě simulace Brownova pohybu molekul vody uvnitře a vně axonů pro každý objem jednoho voxelu. Přestože je zde stále několik technických problémů a aspektů (např. periodický charakter fázové charakteristiky gradientního prostoru), hledáme parametry nastavení dMRI sekvence, kde dMRI data by mohla být statisticky signifikantně odlišná pro geometrie křížících se a ohýbyjících se vláken.

English abstract

After diffusion tensor imaging (DTI) model (Basser et al., 1994), several approaches which are able to detect two or more crossing fibers in diffusion MRI (dMRI) data have been invented (e.g. Q-ball imaging, ball and stick model) (Tuch, 2004 and Behrens et al., 2003). After that, some fiber bundles which had not been seen with DTI model were suddenly observed (e.g. in corpus callosum). Although it brought an improvement it seems that about 50% of detected fiber bundles are false positive results after tractography (Ciccarelli et al., 2008). One crucial problem is that tractography cannot decide if the bundles are crossing or bending because models are not estimating bending-tensor. For 2 crossing fibers, the tractography algorithm can trace from one point to three different places. For 2 bending fibers, there is only one possible way. We would like to introduce how the difference between dMRI data coming from crossing or bending fibers could be detected. Imagine a population of water molecules in the centre of crossing or bending and some applied gradient of diffusion measurement. For crossing fibers, the population of molecules can diffuse in all directions of fiber spreading, thus the phase of molecules can be affected by the whole gradient range. Contrary for one bending fiber, the population can diffuse only in directions of the fiber, thus the phase can be affected only by the narrower gradient range. It applies similarly for second bended fiber. From this point of view, phase distributions should differ for crossing and bending fibers respectively also resulting dMRI data should differ. For this statement testing, the dMRI data simulator which generates dMRI data based on Brownian motion of water molecules inside and outside axons per one voxel volume was created. Although there is several technical problems and aspects (e.g. periodic character of gradient space phase distribution) we are looking for sequence settings of dMRI measurement where the dMRI data would be statistically significantly different for crossing and bending fiber geometries.

Keywords

diffusion MRI, crossing and bending fibers

Released

02.03.2015

Publisher

Elsevier B.V.

Location

Netherlands

Pages from

e46

Pages to

e46

Pages count

1

URL

BibTex


@misc{BUT118243,
  author="René {Labounek} and Michal {Mikl} and Roman {Jakubíček} and Jiří {Chmelík} and Jiří {Jan}",
  title="Could it be possible to distinguish bending and crossing fibers in diffusion MRI data?",
  annote="After diffusion tensor imaging (DTI) model (Basser et al., 1994), several approaches which are able to detect two or more crossing fibers in diffusion MRI (dMRI) data have been invented (e.g. Q-ball imaging, ball and stick model) (Tuch, 2004 and Behrens et al., 2003). After that, some fiber bundles which had not been seen with DTI model were suddenly observed (e.g. in corpus callosum). Although it brought an improvement it seems that about 50% of detected fiber bundles are false positive results after tractography (Ciccarelli et al., 2008). One crucial problem is that tractography cannot decide if the bundles are crossing or bending because models are not estimating bending-tensor. For 2 crossing fibers, the tractography algorithm can trace from one point to three different places. For 2 bending fibers, there is only one possible way. We would like to introduce how the difference between dMRI data coming from crossing or bending fibers could be detected. Imagine a population of water molecules in the centre of crossing or bending and some applied gradient of diffusion measurement. For crossing fibers, the population of molecules can diffuse in all directions of fiber spreading, thus the phase of molecules can be affected by the whole gradient range. Contrary for one bending fiber, the population can diffuse only in directions of the fiber, thus the phase can be affected only by the narrower gradient range. It applies similarly for second bended fiber. From this point of view, phase distributions should differ for crossing and bending fibers respectively also resulting dMRI data should differ. For this statement testing, the dMRI data simulator which generates dMRI data based on Brownian motion of water molecules inside and outside axons per one voxel volume was created. Although there is several technical problems and aspects (e.g. periodic character of gradient space phase distribution) we are looking for sequence settings of dMRI measurement where the dMRI data would be statistically significantly different for crossing and bending fiber geometries.",
  address="Elsevier B.V.",
  chapter="118243",
  doi="10.1016/j.clinph.2014.10.205",
  howpublished="print",
  institution="Elsevier B.V.",
  number="3",
  year="2015",
  month="march",
  pages="e46--e46",
  publisher="Elsevier B.V.",
  type="abstract"
}