Part of it are "averages", but with quite different bvec rotations. Any
idea why this is a problem for the ARD?
-----Original Message-----
From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On
Behalf Of Matt Glasser
Sent: dinsdag 6 april 2010 20:59
To: [log in to unmask]
Subject: Re: [FSL] bedpostx with high angular resolution dataset
The length of time is certainly because of the large number of
directions
(even 256 takes a long time). Do you have several averages with
slightly
different bvecs rotations after eddy_correct or something like that? I
think I remember Saad saying that ARD doesn't do as well with this (it
is
better to average to improve SNR). With large number of directions it
is
possible to reconstruct at least 3 fibers in many voxels (this will
likely
be more accurate).
I think it is expected to find a lot more multi-fiber voxels in datasets
with large numbers of DW directions. Very high angular resolution data
should be more sensitive to lower strength subsidiary fibers & larger
numbers of subsidiary fibers. Also, One thing that can happen is that
broader uODFs can be split into multiple fibers (potentially
incorrectly).
The good news is that this is not necessarily bad for tractography!
Because
this is an area of active work, I hope Saad or Tim will correct anything
I
have said that is incorrect. :)
Peace,
Matt.
-----Original Message-----
From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On
Behalf
Of Ben Jeurissen
Sent: Tuesday, April 06, 2010 1:27 PM
To: [log in to unmask]
Subject: [FSL] bedpostx with high angular resolution dataset
Hi,
I'm running bedpostx on a DW dataset with 700 DW directions (after
b-matrix
rotations), b-value 1000. Apart from a very long processing time (which
is
probably due to the large amount of DW directions), everything runs
fine.
However, the automatic relevance determination doens't seem to do its
job
well. For example: bedpostx finds 2-fiber voxels almost everywhere in
the
dataset. It even reports quite some multi-fiber voxels deep in the
corpus
callosum where I would not expect them and the neighborhood does not
support them at all.
Does this mean I have to reduce the ARD weight? If so, what value do you
suggest I set it to? Or is the method not suitable for datasets with
large
numbers of images?
Kind Regards,
Ben
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