Hi, Eugenio
Just noticed the first lines of aseg.stats. From the command line
recon-all pipeline uses to make the original aseg.stats file it uses
norm.mgz so I think it is the best file to use for intensity comparisons
like I proposed.
Thank you.
Marcos
Em Qua, 2013-06-12 às 13:33 -0300, Marcos Martins da Silva escreveu:
> Hi, Eugenio
> Hmmm, I guessed the values I got were different because they
> represented different stages of the processing. And you just confirmed
> that. Thank you. I would like to compare these intensities with the
> intensities I can read in aseg.stats. And I guess the comparison
> would be more interesting if I use the same source.
> Cheers,
> Marcos
> Em Ter, 2013-06-11 às 17:13 -0400, Juan Eugenio Iglesias escreveu:
>
> > Dear Marcos,
> > these are just mean and standard deviations of intensities of the images
> > at different levels of processing. What the best measure is depends on
> > what you want to do with them.
> > Cheers,
> > /Eugenio
> >
> > On Tue, 2013-06-11 at 11:46 -0300, Marcos Martins da Silva wrote:
> > > Hi, Eugenio,
> > > TY again for your help. I did the computing without errors after your
> > > suggestions. I also computed the intensities based on nu.mgz, norm.mgz
> > > and T1.mgz. I would like to know the best measure I should consider,
> > > including because I intend to compare these values with intensity data
> > > from basal ganglia and cortical areas as I can find in aseg.stats. It
> > > follows my results:
> > > norm.mgz based
> > > meannorm= 78.725
> > > variancenorm = 20.258 (stdev = 4.500888801)
> > >
> > > nu.mgz based
> > > meannu = 87.362
> > > variancenu = 26.343 (stdev =5.132543229)
> > >
> > > T1.mgz based
> > > meant1 = 84.023
> > > variancet1 = 23.277 (stdev =4.824624338)
> > >
> > >
> > > It follows the MatLab/Octave code I used
> > > Norm=MRIread('norm_postres.mgz');
> > > datanorm=Norm.vol(:);
> > > Ps=nMRIread('posterior_right_subiculum.mgz');
> > > postsub=double(Ps.vol(:));
> > > meannorm=sum(datanorm.*postsub)/sum(postsub);
> > > variancenorm=sum((datanorm-meannorm).^2.*postsub)/sum(postsub);
> > > Nu=MRIread('nu_postres.mgz');
> > > datanu=Nu.vol(:);
> > > meannu=sum(datanu.*postsub)/sum(postsub);
> > > variancenu=sum((datanu-meannu).^2.*postsub)/sum(postsub);
> > > T1=MRIread('T1_postres.mgz');
> > > datat1=T1.vol(:);
> > > meant1=sum(datat1.*postsub)/sum(postsub);
> > > variancet1=sum((datat1-meant1).^2.*postsub)/sum(postsub);
> > >
> > > The *postres.mgz files are nu, norm and T1 resampled as you suggested.
> > > And nMRIread is the workarounded version of MRIread I said in previous
> > > message.
> > > Cheers,
> > > Marcos
> > > Em Seg, 2013-06-10 às 16:58 -0400, Juan Eugenio Iglesias escreveu:
> > > > Hi Marcos,
> > > > 1. your fix of MRIread.m is great. I forgot of this bug; we should
> > > > totally take care of it.
> > > > 2. a problem is that nu.mgz (or norm.mgz) and the posteriors are in
> > > > difference voxel space. So, you need to resample norm.mgz to the space
> > > > of the subfields. To do so, you can use mri_convert with the option -rl
> > > > ("reslice like"):
> > > > mri_convert norm.mgz norm_resampled.mgz -rl posterior_subiculum.mgz
> > > > 3. Now you can do:
> > > > A=MRIread('norm.mgz');
> > > > data=A.vol(:);
> > > > B=MRIread('posterior_subiculum.mgz');
> > > > post=double(B.vol(:));
> > > > mean=sum(data.*post)/sum(post);
> > > > variance=sum((data-mean).^2.*post)/sum(post);
> > > > Cheers,
> > > > /Eugenio
> > > >
> > > > On Mon, 2013-06-10 at 17:17 -0300, Marcos Martins da Silva wrote:
> > > > > Hi, Eugenio
> > > > > TY for your fast help. I understood you were suggesting to compute
> > > > > that on MatLab and I tried this:
> > > > >
> > > > > NU=MRIread('nu.mgz') ### that runs ok
> > > > > Ps=MRIread('posterior_left_subiculum.mgz') ### it fails with the
> > > > > following message
> > > > > WARNING: error reading MR params
> > > > > Attempted to access mr_parms(1); index out of bounds because
> > > > > numel(mr_parms)=0.
> > > > >
> > > > > Error in MRIread (line 100)
> > > > > tr = mr_parms(1);
> > > > >
> > > > > I solved creating a little customized nMRIread.m with the following
> > > > > changes:
> > > > >
> > > > > if numel(mr_parms) > 0
> > > > > tr = mr_parms(1);
> > > > > flip_angle = mr_parms(2);
> > > > > te = mr_parms(3);
> > > > > ti = mr_parms(4);
> > > > > else
> > > > > mr_parms(1) = 0;
> > > > > mr_parms(2) = 0;
> > > > > mr_parms(3) = 0;
> > > > > mr_parms(4) = 0;
> > > > > tr = mr_parms(1);
> > > > > flip_angle = mr_parms(2);
> > > > > te = mr_parms(3);
> > > > > ti = mr_parms(4);
> > > > > end
> > > > >
> > > > > With those changes I assigned 0 to mr-parms elements so it runs
> > > > > without errors
> > > > > But whe I tried the following line I copy and pasted from your
> > > > > message:
> > > > > mean=sum(Ps.*NU)/sum(Ps); ### I got the following error
> > > > > Undefined function 'times' for input arguments of type 'struct'.
> > > > >
> > > > > Any help?
> > > > > Cheers, Marcos
> > > > >
> > > > > PS: I promised I will post the final solution to list. But I guess it
> > > > > is more didactic if these little problems are solved before.
> > > > > TY, again.
> > > > > Em Seg, 2013-06-10 às 14:19 -0400, Juan Eugenio Iglesias escreveu:
> > > > > > Hi Marcos,
> > > > > > the right way of doing this is using the soft posteriors to compute
> > > > > > the
> > > > > > mean and variance, rather than thresholding at p=0.5. For instance,
> > > > > > if
> > > > > > you wanted to compute the mean and variance of the intensitites of
> > > > > > the
> > > > > > subiculum, you would do something like this:
> > > > > > mean=sum(Ps.*NU)/sum(Ps);
> > > > > > variance=sum(Ps.*(NU-mean).^2)/sum(Ps);
> > > > > > (where Ps is the posterior of the subiculum)
> > > > > > Cheers,
> > > > > > /Eugenio
> > > > > >
> > > > > >
> > > > > >
> > > > > > On Mon, 2013-06-10 at 15:12 -0300, Marcos Martins da Silva wrote:
> > > > > > > Hi, Freesurfer Experts
> > > > > > >
> > > > > > > After usual processing with recon-all -all we get the aseg.stats
> > > > > > > file
> > > > > > > with several data including intensity values like:
> > > > > > > normMean
> > > > > > > normStdDev
> > > > > > > normMin normMax normRange
> > > > > > > Left-Hippocampus 77.8939 7.5748 46.0000
> > > > > > > 105.0000 59.0000
> > > > > > >
> > > > > > > How could I get similar results for each hippocampal subfield,
> > > > > > > assuming I also generated all posterior*.mgz files corresponding
> > > > > > > to
> > > > > > > each subfield?
> > > > > > > I guess I should first binarize each of the subfields file with a
> > > > > > > threshold=127 to map all the pertinent voxels, and then use these
> > > > > > > files as masks over nu.mgz and calculate the intensities values.
> > > > > > > But I
> > > > > > > do not know the best way to accomplish this after the binarize
> > > > > > > step.
> > > > > > >
> > > > > > > Thank you in advance for any help.
> > > > > > >
> > > > > > > Marcos
> > > > > > > _______________________________________________
> > > > > > > Freesurfer mailing list
> > > > > > > Freesurfer@nmr.mgh.harvard.edu
> > > > > > > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
> > > > > >
> > > > >
> > > >
> > >
> >
>
>
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