Interesting. I have seen the vibration artifacts from two systems. The
artifacts from these systems were very similar to each other and nowhere as
severe as these shown here. However, our data were collected with 89
directions, reading Dan's post I think the higher number of directions was
the reason why these artifacts were less prominent on our end. 
Cheers
peter

-----Original Message-----
From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf
Of Daniel Gallichan
Sent: Monday, March 28, 2011 4:51 AM
To: [log in to unmask]
Subject: Re: [FSL] Siemen's vibration artifact and Gallichan correction

Hi Darren and others

Sorry to join the conversation late - being more involved in MR physics I
have to confess I don't actually subscribe to the FSL list...  

I have just  a few comments to add.

Firstly, I definitely agree with Darren that you are seeing the same
vibration artefact as we observed on the system in Oxford prior to the
Siemens hardware adjustment. However, if you see an effect with |x| as low
as 0.45 then I imagine that you have an even bigger effect than we observed
- which could be due to small differences in hardware, or even in how you
restrain the head of the subject (we didn't test it thoroughly, but it
certainly appeared to be the case that by holding the subject in place on
the sides by applying pressure to the headphones it also creates a very good
mechanical connection for transmitting the vibrations...). Unfortunately, a
bigger effect also means you are less likely to be able to get a reasonable
correction.

The correction method described in our paper is unfortunately also only an
approximation. The Tukey windowing function then approximates the effect of
the k-space filter which is automatically applied to the 'smaller half' of
the 3/4 Fourier data. I tried applying the technique to a couple of
30-direction datasets and observed a similarly incomplete correction to that
which you show in this thread. Having more directions effectively means that
you are able to sample more of this curve arising from the filter, and are
likely to get better results. It is also worth considering that even if the
FA maps look much better after the correction, the resulting diffusion
tensor in these regions will be less reliable than if the artefact were not
present - as the extra parameter will necessarily increase the confidence
interval on all estimated parameters. It is certainly preferable to not have
the artefact in the first place...

If you don't need to do direct comparisons of tensor values within the
affected regions, then the simplest and most reliable method to deal with
the artefact would be to identify affected regions by their residuals from a
normal tensor fit, and then exclude these regions from the datasets.
Subjects where the affect region overlaps with the region of interest would
sadly then need to be discarded - but that may be the only option left...  

You mentioned doing a voxel-by-voxel fitting approach - do you mean
effectively having different Tukey filter parameters for each voxel? If so,
I don't know how you would choose which values to use. Ideally you need to
know how the signal varies with |x| when there is no diffusion present (or
isotropic diffusion) which without vibration should be constant. With
vibration, however, this will become location-dependent. The method we
presented makes a guess at this function by averaging over all the affected
voxels which have been identified. I don't see how you could do this on a
voxel-by-voxel basis.
 
My general advice for avoiding the artefact is, as Arman mentioned, using a
full-Fourier acquisition with the necessarily longer TE. When we tried this
with iPAT=2 the TE was only marginally increased, and the SNR appeared to be
quite similar (getting a precise SNR comparision is pretty tricky -
especially where parallel imaging is involved...).

Hope this is of some use - and I hope you can still get something useful
from your data!

Cheers

Dan