This presentation will touch upon the following aspects: general properties of diffusion, acquisition methods, the diffusion tensor model and diffusion indices,
correlations of these indices with other MRI parameters and histology-derived quantities, data sampling strategies, validation strategies, limitations of DTI and applications
Molecular diffusion refers to the random translational motion of molecules moving with thermal velocities, also called Brownian motion. During their random motion, molecules probe tissue structure at a microscopic scale much smaller than the usual image resolution. For a typical diffusion time of about 50 msec, as sampled by the MRI measurement, water molecules move in the brain on average over distances of around 10 um.
At cellular scale, this is a considerably large distance, and allows molecules to interact with fundamental tissue components such as cell membranes, fibers, or macromolecules. The NMR-observed diffusion properties of water protons are related to the presence and properties of displacement barriers (hindrances and restrictions) on the diffusion 1-10um length scale.
In principle, diffusion-provided information holds great promise for quantifying the microstructural architecture of living systems and its changes caused by pathology or ageing. However, and despite significant progress made by modeling of white matter in the last decade, extracting quantitative microstructural information and changes therein remains challenging. Nevertheless, the MR diffusion signal is often of empirical value, for example, in detecting regions of brain injury (stroke), or even to some extent characterizing cellularity in brain tumours.
Since information is read out from large voxels accessible to in vivo MRI, multiple tissue compartments having different self-diffusion are averaged together - hence the commonly used name “apparent diffusion coefficient” or ADC.
As a consequence of the presence of barriers to free diffusion, the ADC of water in tissue is reduced typically by a factor of 2–5 in comparison to the ADC in bulk water. Part of this reduction could also be due to changes in the local diffusion properties of water in a cellular environment, a topic not yet unequivocally resolved.
Diffusion is a three-dimensional process and molecular mobility in tissues such as white matter in the brain was observed to be different for different directions. The presence of fibres in the white matter of the brain is well known from studies on post mortem brains and constitute a plausible source for the origin of the observed diffusion anisotropy in the brain. Diffusion in the direction parallel to fibres is faster than in directions perpendicular to them.
As diffusion is encoded in the MRI signal by using magnetic field gradient pulses, only molecular displacements that occur along the direction of the gradient are visible and diffusion anisotropy can be detected by observing variations in the diffusion measurements when the direction of the gradient pulses is changed. This is a feature quite specific to diffusion, not found with commonly used MRI parameters such as T1, although at very high fields and quite recently anisotropy of T2, T2* and magnetic susceptibility was also observed and related to the presence and properties of fibres. Diffusion anisotropy remains, however, the easiest property to use for producing maps of the fibre orientation in brain white matter. This can be done by use of models, the simplest of which is the diffusion tensor formalism introduced by Basser et al. With diffusion tensor imaging (DTI), diffusion anisotropy effects in diffusion MRI data can be characterized on a voxel by voxel basis starting from as little as seven different diffusion weighted images. The properties of the diffusion tensor in each voxel can be used, for example, to characterize the most likely path of water molecules, along the direction of highest diffusivity - fibre tracking. A minimum of six non-collinear diffusion encoding directions are required to measure the full diffusion tensor, in addition to a non-diffusion-weighted image. The selection of the six or more tensor encoding directions is critical for accurate and unbiased assessment of diffusion tensor measures. Encoding sets with uniform angular sampling are found to yield the most accurate diffusion tensor estimates. The use of a considerable number of diffusion encoding directions, of the order of 30 or more, causes the measurement errors to be independent of the tensor orientation.
Several isotropic and anisotropic diffusion indices can be constructed to characterize the tensor properties in each voxel and to be visualized as parameter maps.
This presentation will address in greater or lesser detail the following aspects:
- general properties of diffusion (free/restricted/hindered, temperature dependence, field strength effects) - acquisition method
- the diffusion tensor model and diffusion indices (ADC, FA, axial and radial diffusivity,…)
- correlations of these indices with other MRI parameters (myelin water, magnetization transfer) and histology-derived quantities (e.g. cellularity)
- data sampling strategies (directions, b-value, noise)
- validation strategies (histology, phantoms)
- limitations of DTI
- applications: tractography in neurosurgery, brain connectivity in vivo, gray matter structure and connectivity in fixed tissue
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