[Mristudio-users] second and tertiary eigenvalue equal to 0

[susumu mori]

Hi Yi,

I want to hear from Hangyi for detailed response, but there are known issues
in tensor fitting. In the linear tensor fitting, we assume that the tensor
has positive diagonal elements. This means, for example, when gradients are
applied (diffusion-weighted), signal becomes lower than non-weighted images
(b0). However, there are situation when this assumption is violated. At the
edge of the brain, subject motion (non-reproducible) and eddy current
(reproducible) causes pixel mis-registration, meaning, the pixel is
sometimes inside and sometime outside the brain. Then the fitting blows up.
Also, for the very highly anisotropic area such as CC, DWIs with the
gradients applied perpendicular to the fiber (say, we expect only 5% signal
loss) could have higher intensity than b0 due to SNR (say more than 5%
signal fluctuation). In this case, the fitting blows up again.

In these situations, it is not uncommon to get negative diagonal elements or
FA higher than 1.0. To prevent this, there are several proposed methods. I
believe DtiStudio simply replace the negative diagonal elements with 0.

The issues of the CC should be reduced if you have higher SNR (or more
DWIs). What is the resolution and the number of DWIs you are using?
For the brain edge, please check the fitting error map based on the
DtiStudio Updage file I recently distributed.

Susumu

On Wed, Apr 7, 2010 at 5:21 PM, Yi Jiang <yj3 at duke.edu> wrote:

>  Dear All,
>
>
>
> I computed diffusion tensors and FA, va0 (primary eigenvalue), va1
> (secondary eigenvalue), and va2 (tertiary eigenvalue) by linear fitting in
> DTIStudio. Then I save them into raw matrix and read them in matlab.
>
>
>
> I found the number of non-zero (the background is all zero) voxels for va1
> and va2 is less than that of va0 or FA. Such as:
>
>
>
> number of non-zeros voxels for va0 =     6311454
> number of non-zeros voxels for va1 =     6310767
> number of non-zeros voxels for va2 =     6248757
> number of non-zeros voxels for va0 =     6311454
>
>
>
> This happens in all my datasets, consistently.
>
>
>
> The voxels where va0~=0 & va1==0 are not a lot (a few hundred), and they
> mostly are at the edge of the brain, not inside white mattter.
>
>
>
> But the voxels where va0~=0 & va2==0 are quite a lot (tens of thousand),
> and they resides mostly inside white matter, such as corpus callosum.
>
>
>
> I would like to know why this happens.
>
>
>
> Moreover, when I try to, for example, calculate the mean va2 within corpus
> callosum, how can I deal with those voxels where va2==0 ? I think to treat
> them as equal to 0 sounds not correct physiologically. But because there are
> a lot, I want to count those points in, especially for small white matter
> structures (sometimes those va2==0 voxels can take up to 10% of a white
> matter structure).
>
>
>
> Also, when I calculate radial diffusivity (va1+va2)/2, the va2==0 points
> generate incorrect radial diffusivity.
>
>
>
> Thank you very much!
>
>
>
> Best,
>
> Yi
>
>
>
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