Diffusion Tensor Imaging: A Practical Handbook

This book provides an overview of the practical aspects of diffusion tensor imaging (DTI), from understanding the basis of the technique through selection of the right protocols, trouble-shooting data quality, and analyzing DTI data optimally. DTI is a non-invasive magnetic resonance imaging (MRI) technique for visualizing and quantifying tissue microstructure based on diffusion. The book discusses the theoretical background underlying DTI and advanced techniques based on higher-order models and multi-shell diffusion imaging. It covers the practical implementation of DTI; derivation of information from DTI data; and a range of clinical applications, including neurosurgical planning and the assessment of brain tumors. Its practical utility is enhanced by decision schemes and a fully annotated DTI brain atlas, including color fractional anisotropy maps and 3D tractography reconstructions of major white matter fiber bundles. Featuring contributions from leading specialists in the field of DTI, Diffusion Tensor Imaging: A Practical Handbook is a valuable resource for radiologists, neuroradiologists, MRI technicians and clinicians.

Diffusion Tensor Imaging: A Practical Handbook

The workflow is an end-to-end tool that supports a variety of ways to analyzediffusion MRI data, in which you start from raw imaging data and producegeometric models and quantitative summaries of tissue microstructure,morphometry, and connectivity. Specifically, you can perform region-based andtractography-based analysis and combine these with tissue parameter mappingusing diffusion tensor imaging and advanced multi-shell approaches. Inaddition to diffusion MRI, the workflow has components that streamline the useof state-of-the-art packages for morphometric analysis using T1-weighted MRIdata, and further, allow them to be combined with diffusion MRI data. If youare new to diffusion MRI and would like to learn more, Diffusion TensorImaging: a Practical Handbookand NMR in Biomedicine: Special Issue on Diffusion MRI of thebrain are both goodstarting points.

Furthermore, there are many types of diffusion parameters that can be usedin an ROI analysis. The examples listed above used diffusion tensor imaging(DTI) parameters, but if you have multi-shell data, there are otherpossibilities supported by qitdiff. The table below lists these otherpossibilities, and you can create targets for them by substituting dti withthe appropriate model identifier.

Alterations in the white matter (WM) microstructure have been linked to antidepressant treatment response and remission in studies using diffusion tensor imaging (DTI). DTI indirectly assesses the WM microstructure properties using simple quantitative measures of the rate and directionality of the water molecule diffusion (Van Hecke et al., 2016). The measures, commonly derived from the DTI tensor, are fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD). FA, the most popular measure, provides information about the degree of anisotropic diffusion. Increased FA values indicate higher WM integrity (Alexander et al., 2011). MD measures the average diffusion rate, and lower values are associated with higher WM integrity (Alexander et al., 2011). AD and RD are defined as the parallel and perpendicular diffusivity to the main direction of the tract, respectively. The former might indicate axonal integrity (Song et al., 2002, 2003; Budde et al., 2007), whereas the latter is associated with the degree of myelination (Song et al., 2002, 2003, 2005; Klawiter et al., 2011).

In a study of magnetic resonance diffusion tensor imaging (MR-DTI) to detect DAI after traumatic brain injury (TBI), a significant reduction in fractional anisotropy (FA) in the corpus callosum was seen in the acute phase. No significant changes were identified in the parallel or perpendicular eigenvalues or trace. At 6 months, a significant reduction in FA and a significant increase in trace and perpendicular eigenvalues were seen. [7] 041b061a72