Hi,
If I may suggest, the robust tensor
fitting is a good solution to your problem.
The outliers will be detected and removed
from the fitting automatically in a voxel by voxel basis.
http://dir2.nichd.nih.gov/nichd/stbb/restore_rob_est05.pdf
Lin-Ching Chang
From: Saad Jbabdi
[mailto:[log in to unmask]]
Sent: Thursday, October 19, 2006
4:54 AM
To: [log in to unmask]
Subject: Re: [FSL] DTI quality
assurance
Hi,
It is hard to tell how good is good in DWI. Generally, it depends on
the question you are addressing. You might want to optimise your sequence
differently if your goal is to study FA and ADC or if you want to do
tractography.
In your specific case, it is effectively strange to have slices with
almost no signal, and could explain a higher FA in these slices if this drop
occurs in one of the volumes and not in the others. Do you have any idea about
why this happens ?
The mean intensity is not supposed to be the same over slices, but it
is generally close across volumes within the same slice (except for b=0 of
course). So you might be able to detect a "strange" slice by
computing the mean and std across volumes of each slice (inside the brain) and
checking for slices that diverge by n*std (depending on your experience). You
might determine this by having a quick look at the distribution of mean signal
and see if outliers are easy to detect. This is faster than looking at each slice
separately.
cheers,
saad
On 19 Oct 2006, at 05:55, Hedok Lee wrote:
Dear FSLers.
My apology for being slightly off the topic.
I’m writing regarding a quality assurance of DWI images in
DTI(b=0 and
b=1000) sequence. We’ve
been collecting DTI along 25 directions+1 b0 with
26 slices(1mmx1mmx5mm) in each volume (676 images total) using GE 1.5T. We
recently observed relatively high FA in one slice compare to the
adjacent
slices, so I began looking at individual images. It turns out this is due
to a few slices of almost no signal in DWI. After checking 676 slices, I
was wondering if there is criteria, or diagnostic tool, to detect bad
slices. Really high, or
low, intensity DWIs are easy to detect.
Some of
the slices look suspicious but I don’t have enough experience to
tell
whether it’s good or bad.
I appreciate if someone has an experience, or a
tool, to set a criteria on this.
How do people trust what they get from
DTI is of decent quality?
More specific questions are
Within the same volume, do mean intensity over slices supposed to be
close? If so,
what’s the reasonable standard deviation. How about the
mean intensities of volumes over different diffusion gradients.
Basically, I ‘m trying to write a simple script to detect bad
slices so
that I don’t have to eyeball 676 images every time. If there is a script
for this, please let me know.
Thanks,
Hedok
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Saad Jbabdi,
Postdoctoral Research
Assistant,
FMRIB, JR Hospital,
Headington,
+44 (0) 1865
222545 (fax 222717)
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