Diffusion MRI - HARDI: High-angular-resolution Diffusion Imaging and Q-ball Vector Analysis

HARDI: High-angular-resolution Diffusion Imaging and Q-ball Vector Analysis

Early in the development of DTI based tractography, a number of researchers pointed out a flaw in the diffusion tensor model. The tensor analysis assumes that there is a single ellipsoid in each imaging voxel—as if all of the axons traveling through a voxel traveled in exactly the same direction. This is often true, but it can be estimated that in more than 30% of the voxels in a standard resolution brain image, there are at least two different neural tracts traveling in different directions that pass through each other. In the classic diffusion ellipsoid tensor model, the information from the crossing tract just appears as noise or unexplained decreased anisotropy in a given voxel. David Tuch was among the first to describe a working solution to this problem.

The idea is best understood by conceptually placing a kind of geodesic dome around each image voxel. This icosahedron provides a mathematical basis for passing a large number of evenly spaced gradient trajectories through the voxel—each coinciding with one of the apices of the icosahedron. Basically, we are now going to look into the voxel from a large number of different directions (typically 40 or more). We use "n-tuple" tessellations to add more evenly spaced apices to the original icosahedron (20 faces)—an idea that also had its precedents in paleomagnetism research several decades earlier. We just want to know which direction lines turn up the maximum anisotropic diffusion measures. If there is a single tract, there will be just two maxima pointing in opposite directions. If two tracts cross in the voxel, there will be two pairs of maxima, and so on. We can still use tensor math to use the maxima to select groups of gradients to package into several different tensor ellipsoids in the same voxel, or use more complex higher rank tensors analyses, or we can do a true "model free" analysis that just picks the maxima and goes on about doing the tractography. We could use very high angular resolution (256 different directions) but it is often necessary to do ten or fifteen complete runs to get the information correct and this could mean 2,000 or more images—it gets to be over an hour to do the image and so becomes impossible. At forty angles, we can do 10 repetitions and get done in ten minutes. Also, in order to make this work, the gradient strengths have to be considerably higher than for standard DTI. This is because we can reduce the apparent noise (non-diffusion contributions to signal) at higher b values (a combination of gradient strength and pulse duration) and improve the spatial resolution.

The Q-Ball method of tractography is an implementation of the HARDI approach in which David Tuch provides a mathematical alternative to the tensor model. Instead of forcing the diffusion anisotropy data into a group of tensors, the mathematics used deploys both probability distributions and a classic bit of geometric tomography and vector math developed nearly 100 years ago—the Funk Radon Transform.