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Improved Quantification of Connectivity in Human Brain Mapping

Pathak, Sudhir (2016) Improved Quantification of Connectivity in Human Brain Mapping. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Pathak, Sudhirskpathak@pitt.eduSKPATHAK
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairSchneider, Walterwws@pitt.eduWWS
Committee MemberStetten, Georgestetten@pitt.eduSTETTEN
Committee MemberAizenstein,
Committee MemberGaleotti,
Committee MemberFernández-Miranda, Juan Cfernandezmirandajc@upmc.eduJCF51
Committee MemberBasser,
Date: 25 January 2016
Date Type: Publication
Defense Date: 22 October 2015
Approval Date: 25 January 2016
Submission Date: 29 November 2015
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 184
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Bioengineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: diffusion spectrum imaging, diffusion tensor imaging, Magnetic Resonance Imaging, textile diffusion phantom, white matter quantification, fiber tractography
Date Deposited: 25 Jan 2016 18:55
Last Modified: 19 Dec 2016 14:42


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