Abstract
Diffusion-weighted Magnetic Resonance Imaging (dMRI) has long proven to be a versatile tool for the in vivo microstructural investigation of the human brain (Le Bihan 2003), the spinal cord (Clark et al. 1999), or even muscle tissue (Sinha et al. 2006). In contrast to conventional weighted MRI or functional MRI discussed in the preceding Chap. 4, it is quantitative in the sense that it directly infers on physical quantities with physical units, specifically the diffusion constant. In this chapter, we will first elaborate on the physical background before presenting experimental dMRI data and describe its processing. This includes preprocessing steps, i.e., the removal of artifacts, and the actual modeling of the data to infer on interesting and relevant quantities.
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Notes
- 1.
Due to the independent integrations for the three space directions, it is three times the variance \(2 D \tau \) for a univariate Gaussian distribution in any of the space directions.
- 2.
The diffusion-weighted data read by readDWIdata does not reflect the correct image orientation in y-direction. We therefore explicitly swap the images to follow conventions. This is achieved by setting an option for function rimage in adimpro and plot methods in dti .
- 3.
The Bessel function \(I_0\) can be accessed from R using the wrapper package gsl (Hankin 2006) for the GNU Scientific Library GSL.
- 4.
Specifically, sensitivity is larger near the coil.
- 5.
This is often referred to as minimally processed data, i.e., after image reconstruction.
- 6.
For \(L=1\), the distribution is called Rayleigh.
- 7.
... for positive definite \(\mathscr {D}\).
- 8.
This computation takes a long while for the (rather large) example dataset.
- 9.
..., where we dropped the dependence on the effective number of coils L for parallel imaging for brevity.
- 10.
The method is generally referred to as FACT (fiber assignment by continuous tracking).
- 11.
This is not yet fully implemented in package fslr , results have been obtained using the system call to FSL that is supposed to be generated here.
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Polzehl, J., Tabelow, K. (2019). Diffusion-Weighted Imaging. In: Magnetic Resonance Brain Imaging. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-030-29184-6_5
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DOI: https://doi.org/10.1007/978-3-030-29184-6_5
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