A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and...
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Gespeichert in:

Autor*in:	Sheng Yang [verfasserIn] Kaushik Ghosh [verfasserIn] Ken Sakaie [verfasserIn] Satya S. Sahoo [verfasserIn] Sarah J. Ann Carr [verfasserIn] Curtis Tatsuoka [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Ball-and-Stick model Bayesian dMRI crossing fibers simplified model

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 13(2019)
Übergeordnetes Werk:	volume:13 ; year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2019.00492

Katalog-ID:	DOAJ073700045

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container_title	Frontiers in Neuroscience
authorswithroles_txt_mv	Sheng Yang @@aut@@ Kaushik Ghosh @@aut@@ Ken Sakaie @@aut@@ Satya S. Sahoo @@aut@@ Sarah J. Ann Carr @@aut@@ Curtis Tatsuoka @@aut@@
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callnumber-first	R - Medicine
author	Sheng Yang
spellingShingle	Sheng Yang misc RC321-571 misc Ball-and-Stick model misc Bayesian misc dMRI misc crossing fibers misc simplified model misc Neurosciences. Biological psychiatry. Neuropsychiatry A Simplified Crossing Fiber Model in Diffusion Weighted Imaging
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topic_title	RC321-571 A Simplified Crossing Fiber Model in Diffusion Weighted Imaging Ball-and-Stick model Bayesian dMRI crossing fibers simplified model
topic	misc RC321-571 misc Ball-and-Stick model misc Bayesian misc dMRI misc crossing fibers misc simplified model misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Ball-and-Stick model misc Bayesian misc dMRI misc crossing fibers misc simplified model misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	A Simplified Crossing Fiber Model in Diffusion Weighted Imaging
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title_full	A Simplified Crossing Fiber Model in Diffusion Weighted Imaging
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title_sort	simplified crossing fiber model in diffusion weighted imaging
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title_auth	A Simplified Crossing Fiber Model in Diffusion Weighted Imaging
abstract	Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.
abstractGer	Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.
abstract_unstemmed	Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models.
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title_short	A Simplified Crossing Fiber Model in Diffusion Weighted Imaging
url	https://doi.org/10.3389/fnins.2019.00492 https://doaj.org/article/9d5741c3974f4c5ca9f0cead6f252f76 https://www.frontiersin.org/article/10.3389/fnins.2019.00492/full https://doaj.org/toc/1662-453X
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