This is one of the reasons why the smoothing now includes the option of
changing the datatype of the resulting images. You can start with 8 bit, and
generate smoothed images with eg floating point.
All the best,
-John
On Tuesday 01 April 2008 07:24, Christian Gaser wrote:
> Hi John and Nikos,
>
> the modulated images of the RAVENS and the DARTEL methods seem to be very
> similiar, which is not surprising because both approaches are based on a
> diffeomorphic high-dimensional spatial registration. However, DARTEL is
> using a more tricky approach to create a population template based on the
> given sample.
> The erroneous smoothness estimation of the RAVENS maps was caused by an
> undefined scaling factor in the RAVENS maps (and SPM5 is then using the
> default value of 1). This caused rounding errors for the uint8 data,
> because the maximum values were around 30-50 and after smoothing there were
> only a few different (integer) values existent.
>
> Regards,
>
> Christian
>
>
> ___________________________________________________________________________
>_
>
> Christian Gaser, Ph.D.
> Assistant Professor of Computational Neuroscience
> Department of Psychiatry
> Friedrich-Schiller-University of Jena
> Jahnstrasse 3, D-07743 Jena, Germany
> Tel: ++49-3641-934752 Fax: ++49-3641-934755
> e-mail: [log in to unmask]
> http://dbm.neuro.uni-jena.de
>
> On Mon, 31 Mar 2008 13:40:56 +0000, John Ashburner <[log in to unmask]>
wrote:
> >The RAVENS registration is more precise than the older spatial
> > normalisation routines of SPM. In SPM, there are only about 1000
> > parameters to describe the warps, whereas RAVENS uses millions.
> > Generally, if the residuals are very smooth, then it could imply that the
> > model is underfitting the data. The original concept behind VBM was to
> > treat the spatial normalisation a bit like high pass filtering in an SPM
> > analysis (ie to remove "macroscopic" differences) so that "mesoscopic"
> > differences could be detected.
> >
> >Although the introduction of "modulation" (Jacobian transformation)
> > confused a lot of people, it meant that the values actually had a more
> > meaningful interpretation, and that a VBM analysis was not simply an
> > examination of registration error. "Morphometry" is the study of
> > variation and change in the form (size and shape) of organisms. With the
> > introduction of the Jacobian transform, the use of the term became
> > slightly closer to its dictionary definition, as it involved comparing
> > the regional volumes of grey matter. This also meant that more accurate
> > spatial normalisation could be used. RAVENS maps have always implicitly
> > preserved the tissue volumes in the warped images.
> >
> >I eventually got around to improving the inter-subject registration model
> > in SPM. In the latest updates of SPM5, there is a DARTEL toolbox, which
> > can be used to obtain more precise inter-subject alignment for VBM
> > studies. Experience of DARTEL in the FIL is generally positive. VBM
> > results produce higher t stats for the difference that are detected than
> > for the other SPM spatial normalisation approaches. I would expect the
> > RAVENS results to be similar.
> >
> >Best regards,
> >-John
> >
> >On Sunday 30 March 2008 09:54, Nikolaos Koutsouleris wrote:
> >> Dear SPMers,
> >>
> >> recently, I tried the HAMMER normalization algorithm of Davatzikos et
> >> al. on data that had been segmented with SPM5. I observed that the
> >> smoothness of the obtained RAVENS maps is significantly lower compared
> >> the modulated, low-dimensionally normalized volumes of SPM5 (see example
> >> of the same subject in the attachments). Correct me if I am wrong, but
> >> this may be due to the fact that the HAMMER algorithm uses
> >> high-dimensional elastic warping which retains a higher degree of
> >> anatomical information compared to the SPM normalization.
> >>
> >> When I did cluster-level statistical inference on the 10mm-smoothed
> >> RAVENS maps (using Satoru's non-stationarity correction toolbox), I
> >> noticed that the resulting smoothness estimate was about 5 mm, compared
> >> to the SPM-normalized data which was about 11 mm. In the RAVENS analysis
> >> a primary threshold of p<0.05 together with FWE-corrected extent
> >> threshold of p<0.05 produced a minimum number of 1500 voxels, whereas
> >> the extent threshold for the SPM -normalized data was around 20000
> >> voxels. This is a dramatic difference which make clusters significant
> >> at a much lower spatial threshold. So, I wonder if this difference is
> >> really due to the "roughness" of the RAVENS maps or if the smoothness
> >> estimation in SPM is not valid for RAVENS data. Does anybody have
> >> similar experiences or possible
> >> explanations?
> >>
> >> Thanks in advance and sorry for this lengthy email!
> >>
> >> Cheers,
> >>
> >> Nikos Koutsouleris
> >>
> >> Imaging Workgroup
> >> Department of Psychiatry and Psychotherapy,
> >> Ludwig-Maximilians-University,
> >> Munich
> >
> >===========================================================
>
> ==============
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