Spatially Regularizing High Angular Resolution Diffusion Imaging Public Deposited

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  • March 19, 2019
Creator
  • Rao, Shangbang
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
Abstract
  • Many recent high angular resolution diffusion imaging (HARDI) reconstruction techniques have been introduced to infer ensemble average propagator (EAP),describing the three-dimensional (3D) average diffusion process of water molecules or the angular structure information contained in EAP, orientation distribution function (ODF). Most of these methods perform reconstruction independently at each voxel, which essentially ignoring the functional nature of the HARDI data at different voxels in space. The aim of my thesis is to develop methods which can spatially and adaptively infer the EAP, or ODF of water diffusion in regions with complex fiber configurations. In Chapter 3, we propose a penalized multi-scale adaptive regression model (PMARM) framework to spatially and adaptively infer the ODF of water diffusion in regions with complex fiber configurations. We first represent DW-MRI signals using Spherical Harmonic (SH) basis, then apply PMARM on advanced statistical methods to calculate the coefficients of SH representation, from which ODF representation is calculated using Funk-Radon transformation. PMARM reconstructs the ODF at each voxel by adaptively borrowing the spatial information from the neighboring voxels. We show in the real and simulated data sets that PMARM can substantially reduce the noise level, while improving the ODF reconstruction. In Chapter 4, we propose a robust multi-scale adaptive and sequential smoothing (MASS) method framework to robustly, spatially and adaptively infer the EAP of water diffusion in regions with complex fiber configurations. We first calculate spherical polar Fourier basis representation of the DW-MRI signals, and then apply MASS adaptively and sequentially updating SPF representation by borrowing the spatial information from the neighboring voxels. We show in the real and simulated data sets that MASS can reduce the angle detection errors on fiber crossing area and provides more accurate reconstructions than standard voxel-wise methods and robust MASS performs very well with the presence of outliers. In Chapter 5, we extend multi-scale adaptive method framework to dictionary learning methods, and show that by adding smoothing technique, we can significantly improve the accuracy of EAP reconstruction and reduce the angle detection errors on fiber crossing, even in very low signal-to-noise ratio situation.
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  • In Copyright
Advisor
  • Zeng, Donglin
  • Yap, Pew-Thian
  • Ibrahim, Joseph
  • Sun, Wei
  • Zhu, Hongtu
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2014
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  • Chapel Hill, NC
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