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Dear Jesper, Could you please explain how to " set any negative value to some very small positive value " using FSL commands? Thank you! Mahmoud On Wed, Feb 1, 2017 at 9:19 AM, Jesper Andersson < [log in to unmask]> wrote: > Dear Claire. > > > I've been using topup and eddy to correct my diffusion images. I noticed > that the eddy corrected images contain some negative values. I decided to > mask out (zero) any voxel that has a negative value in any of the diffusion > image volumes (my data includes 11 b=0 images, 30 directions at b=1000 and > 45 directions at b=3000). I then ran the images through DTI and NODDI (I > used just the 30 directions at b=1000 for DTI, and all the directions for > NODDI). > > Many of the negative values introduced by topup and eddy were outside the > brain, but some were inside the brain, so my FA and NODDI images contain > some 0 values in regions that should have high values, e.g. the corpus > callosum (there are more 0 values in the NODDI images than the FA images, > as I used more directions for the NODDI images). > > I just wanted to ask if masking out the negative values is the best > option? I noticed another suggestion on the topup website ( > http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/topup/ApplyTopupUsersGuide) was to > make the negative values positive (using fslmaths -abs). Or, I believe the > negative values are introduced by spline interpolation, so I was wondering > if I should try a different type of interpolation? > > > The negative values are caused by the spline interpolation. However, I > would strongly advice against shifting to tri-linear as that introduces > lots of smoothing to your data. > > A negative value occurs when the “true” value is close to zero and where > the associated uncertainty of that value will add/subtract some small value > that may cause it to go negative. My suggested strategy is to simply set > any negative value to some very small positive value, as that would be the > closest “valid” value. That does not mean that you have to set that pixel > to “some very small value” in all your volumes. If you for example have a > voxel with highly anisotropic diffusion you may find that in a volume > acquired with the diffusion gradient along the principal diffusion > direction you have a negative value. But in many of the other directions > you will have values well into the positive domain, and these are all > valid. The consequence of setting the negative value to “some very small > value” will be a small underestimation of the true anisotropy in that > particular voxel. But that is much preferable to smoothing all you your > data with tri-linear interpolation. > > Jesper > > > > Any help would be greatly appreciated. > > Kind regards, > > Claire > > *Claire Kelly *BSc (Hons) > Research Assistant > Victorian Infant Brain Studies (VIBeS), Clinical Sciences > > *Murdoch Childrens Research Institute *The Royal Children’s Hospital > Flemington Rd Parkville, Victoria 3052 AUS > E: [log in to unmask] > www.mcri.edu.au > > ______________________________________________________________________ > This email has been scanned by the Symantec Email Security.cloud service. > For more information please visit http://www.symanteccloud.com > ______________________________________________________________________ > > >