Here is a basic summary of what DTI is all about, and what some of those DTI parametric maps represent.
A one-page cheat sheet is at the end.
What is diffusion weighting?
We use magnetic field gradients to do useful things like encoding. But they also cause dephasing of signal, which we don’t want to happen (this is because when a gradient is on, there is a range of precessional frequencies along the gradient, even within a voxel). Diffusion weighting uses this dephasing effect to our advantage, to show where diffusion occurs.
How does diffusion weighting work?
Simple. A gradient is switched on—a big one to cause lots of dephasing. Then another gradient is used to completely undo all of the dephasing caused by the first gradient. We should end up with no effect, right? Right—but only if tissues and fluids are stationary. If there is motion—including microscopic diffusion of water molecules—then the dephasing caused by the first gradient is not “undone” by the second because the water molecules experienced different a gradient strength from the first gradient to the second, because they moved. As a result, the dephasing stays and we get signal loss on a diffusion weighted image.
So what are b values?
b values are actually a neat way of summing up into one parameter how much dephasing we are going to allow (size of diffusion weighting gradients etc). We need images with different b values to be able to work out diffusion-related parameters such as the ADC (see below). Note that we can’t say what diffusion is from the signal intensity alone, but from the signal intensity loss, which is why we usually acquire a b=0 (baseline) image to compare with.
So what is DTI?
We can only see that good diffusion exists (because of signal loss) along the direction in which a gradient is applied. So if we want to know the diffusion in all directions, we have to get many diffusion weighted images with diffusion weighting gradients in different directions. Ideally “all directions” would mean every possible direction on a sphere, but in practice we do, say, 12, 16, or 32 gradient directions (or more). The actual choice is up to the you. The minimum number of directions we can get away with is six—for example, one diffusion weighting anteriorly, one posteriorly, one superiorly, one inferiorly, one to the right and one to the left. That just about covers 3D space. Of course the diffusion in the brain is not always going to be exactly along one of these directions, so that’s why more directions are often used.
Showing DTI Data
Diffusion Tensor Imaging (DTI) collects information from all the diffusion weighted images (in however many directions was chosen) and tries to sum up all that information about where water can “diffuse to” in each voxel. DTI uses an ellipsoid (a stretched-out sphere, if you like) to represent where water can go. A long thin ellipsoid means very good diffusion for water along the long axis of that ellipsoid. A sphere (not an ellipsoid any more) means the same diffusivity in all directions. The mathematical way of describing the ellipsoid for each voxel is the tensor.
Sorry, what? A tensor?
The tensor is the maths part of DTI. If you like, just think of the diffusion ellipsoid when someone refers to the diffusion tensor. However, it is useful to know that the parametric maps which we produce in DTI (which are images where the pixel values represent some parameter other than signal intensity) are derived from the maths that are used to describe the tensor/ellipsoid at each voxel.
DTI Parametric Maps
It would be nice to draw little 3D ellipsoids at each pixel location, right? Unfortunately that wouldn’t make clinically readable images! So a number of parameters are used which relate to diffusion. There is a choice because the best one for every clinical situation isn’t yet determined. So you can choose. Some examples of parametric maps are now discussed.
If we simply average all the diffusion weighted images which were acquired in all the directions, an image is produced which gives some sense of the total diffusion taking into account all directions. But this Isotropic Image is not generally used clinically, because the ADC is a better map of average diffusion, because the Isotropic Image is affected by T2 shine-through.
ADC: Apparent Diffusion Coefficient (or Constant)
The ADC map shows the average diffusion-freedom water molecules have in each voxel. This parameter can be calculated from all the diffusion weighted images which are acquired in DTI. Note that it is an average of all directions acquired, when performing DTI. The “A” for Apparent is there because the ADC is affected by partial volume averaging, perfusion, and some measurement errors. The average ADC map is sometimes called the trace map, which is to do with the mathematics of how it is calculated. It is not the same as the Isotropic Image; the ADC uses the mathematics of the tensor (the sum of the scalar values of the eigenvalues of the tensor, divided by three (which is the trace/3), sometimes called simply the “trace” image, but let’s not get into the maths now). The ADC is a useful DTI map.
On an ADC map, good diffusion is bright. This is opposite to the diffusion weighted images, where good diffusion is dark. The ADC removes the effect of T2 shine-through.
eADC: enhanced (or exponential) Apparent Diffusion Coefficient (or Constant)
The eADC shows the attenuation of the signal due to diffusion. On an eADC map, good diffusion is dark, just like the diffusion weighted source images. This is opposite to the ADC. Like the ADC, the eADC also removes the effect of T2 shine-through. Clinicians can choose to use ADC or eADC maps depending on whether they want contrast to match (or be opposite to) the diffusion weighted source images.
FA: Fractional Anisotropy
“Anisotropy” refers to how restricted diffusion is. An = not; iso = the same; tropic = direction (from Greek tropos “turn”). So anisotropy means “not the same in all directions”, which is what we are trying to find out about the diffusion of water molecules in each voxel. The ADC and eADC just communicate information about the diffusion in a voxel, whereas anisotropy maps go one step further and communicate information about the orientation of the underlying structure of the fiber tracts in the brain.
There are a number of ways of calculating (and thus, describing) anisotropy. FA is the main one. FA (and RA and VR, below) are rotationally invariant, which is important, because it means that the FA values produced wouldn’t be different if all your diffusion weighting gradients were rotated a bit, or if the patient was in a different position.
RA: Relative Anisotropy
RA is similar to FA, but it is a slightly different calculation (like FA it uses the scalar values from the tensor eigenvectors, but never mind about that now).
FA gives better detail. Use FA.
VR: Volume Ratio
VR is another calculated measure of anisotropy. The SNR and detail of VR is lower than FA and RA. The one thing VR has going for it is that the contrast between regions of low and high anisotropy is stronger than FA or RA.