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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,  

Oxford University FMRIB Centre

 

FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK

+44 (0) 1865 222545  (fax 222717)

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