Tracking and validation techniques for topographically organized tractography

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Tracking and validation techniques for topographically organized tractography. / Aydogan, Dogu Baran; Shi, Yonggang.

In: NeuroImage, Vol. 181, 01.11.2018, p. 64-84.

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@article{5f5a5c85a44147df98dc89248de9a85d,
title = "Tracking and validation techniques for topographically organized tractography",
abstract = "Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.",
keywords = "Connectome, Corticospinal tract, dMRI, Retinotopy",
author = "Aydogan, {Dogu Baran} and Yonggang Shi",
year = "2018",
month = "11",
day = "1",
doi = "10.1016/j.neuroimage.2018.06.071",
language = "English",
volume = "181",
pages = "64--84",
journal = "NeuroImage",
issn = "1053-8119",

}

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TY - JOUR

T1 - Tracking and validation techniques for topographically organized tractography

AU - Aydogan, Dogu Baran

AU - Shi, Yonggang

PY - 2018/11/1

Y1 - 2018/11/1

N2 - Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

AB - Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

KW - Connectome

KW - Corticospinal tract

KW - dMRI

KW - Retinotopy

UR - http://www.scopus.com/inward/record.url?scp=85049447172&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2018.06.071

DO - 10.1016/j.neuroimage.2018.06.071

M3 - Article

VL - 181

SP - 64

EP - 84

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

ID: 29134907