Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

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"
}