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Hi there Hedok, what scanner are you using? I used to work with a GE Signa, and we would regularly see exactly the same thing. Back at the time I used to attribute this to bulk movement occuring exactly at the "diffusion weighting period" of the sequence. Now, having a little experience of dwi's from other (Siemnes) scanners, and not seeing it any more I can only assume I was wrong in that first assumption. I honestly don't have a clue as to what causes it and I would be very interested in any feedback. It is extremly annoying though. What you can do is to look at residual error (after tensor fit) averaged over slices. Any slice that turns up an outlier is a candidate for loss of signal. What I then used to do was to discard that slice and fill it in with its expected value given a tensor fit to the remaining (good) data. I am afraid it was all hacks though so I can't really offer you any software to do it. Does anyone else have any experience of seeing these kind of "dead slices"? Jesper > 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 > > --------------------------------------------------------------------------- > Saad Jbabdi, > Postdoctoral Research Assistant, > Oxford University FMRIB Centre > > > FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK > +44 (0) 1865 222545 (fax 222717) > [log in to unmask] http://www.fmrib.ox.ac.uk/~saad > --------------------------------------------------------------------------- > > > > > > >