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Diffusion-Weighted Imaging Analysis

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Functional Magnetic Resonance Imaging Processing

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Abstract

In this chapter, we present methods to analyze three types of diffusion-weighted imaging (DWI), i.e., diffusion tensor imaging (DTI), Q-ball imaging (QBI), and diffusion spectrum imaging (DSI). DWI is one of the methods to measure water diffusion in tissues. It provides unique biologically and clinically relevant information that is not available from other imaging modalities. Because of its noninvasive nature, DWI has been applied widely in many studies, including but not limited to fiber tracts in cerebral cortex. We will begin with the physical background of diffusion MRI (dMRI), and then we will present method to compute the apparent diffusion coefficient (ADC) map and the eigenvalues for DTI study. After that, we introduce the measures for the water diffusion anisotropy and give one example fiber tractography method using DTI.

However, DTI method cannot resolve the crossing fiber issue; to overcome this limitation, Q-ball imaging (QBI) and diffusion spectrum imaging (DSI) with high angular resolution diffusion imaging (HARDI) acquisition have been proposed. QBI has the advantage of model-free and can reconstruct water diffusion orientation distribution function (ODF) from single-shell HARDI acquisition with relative small b value. To analyze QBI dataset, we introduce the concept of water molecule diffusion probability distribution function (PDF) and its relationship with ODF. Next, we show how to compute ODF from QBI using spherical harmonic approximation. Finally, we present generalized cross-validation (GCV) algorithm to regularize ODF for QBI analysis.

Although it takes longer time to acquire hundreds of DWI images for diffusion spectrum imaging (DSI) study, due to its high accuracy, DSI becomes more and more popular for studying fiber tracking and water diffusion in tissue. Like QBI, it is a model-free method and ODF can be estimated using 3D Fourier transformation method. Unlike QBI, DSI employs more than one b value/factor for data acquisition, while single-shell QBI adopts only one b factor. We briefly compare DSI with QBI, and then we apply fixed regularization parameter method and GCV regularization method for DSI ODF reconstruction.

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Authors and Affiliations

  1. Intelligent Systems Research Centre, University of Ulster, Londonderry, UK

    Xingfeng Li

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  1. Xingfeng Li
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Li, X. (2014). Diffusion-Weighted Imaging Analysis. In: Functional Magnetic Resonance Imaging Processing. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7302-8_5

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