Hi Michael, The aseg lookup table should be generated from all the subjects in the dataset you are working with initially, then passed with the -asegLUT flag along with any other QA options. I believe the aseg outliers are just any subject measure which is 2 SD above/below the mean for your dataset. If that's the main thing you're interested in, than it may be quicker to compute the outliers with the dataset aseg table using excel or matlab.
For the most part the snapshots and wm-anat-snr results are the most useful. With the wm-anat-snr results we haven't found any reliable cut-offs across datasets. Your best bet is to examine a couple cases and their corresponding wm-anat-snr measures to determine a cut-off. We have had some success using the SNR, stdWM intensity, and # of WM voxels measures to identify cases which are candidates for exclusion from large datasets (~1000 recons).
Thanks for the note on the eTIV name change.
-Louis
On Thu, 9 Jan 2014, Harms, Michael wrote:
Hi Vinke, In the QAtools_v1.1 package, should the supplied DefaultAsegMeans.txt file have "0 +/- 0" as all its entries?
More generally, I was wondering what aspects of the QAtools package you've found most useful? In particular, do the results from wm-anat-snr relate to data quality in a meaningful way?
P.S. For the benefit of others, to get the "-gen-asegLUT" flag of 'recon_checker' to work on FS 5.3 data, you need to change the helper 'gnicv' script to grep on "EstimateTotalIntraCranialVol" rather than "ICV" (due to the name change of that variable in the stats/aseg.stats file relative to FS 5.1).
thanks, -MH
-- Michael Harms, Ph.D.
Conte Center for the Neuroscience of Mental Disorders Washington University School of Medicine Department of Psychiatry, Box 8134 660 South Euclid Ave. Tel: 314-747-6173 St. Louis, MO 63110 Email: mharms@wustl.edu
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