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| do not have a tool to do that easily, but you can do it in matlab. |
do not have a tool to do that easily, but you can do it in matlab. For parallel operation, add --threads N where N is the number of CPUs you want to use. |
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mri_gtmpvc --i pet.nii.gz --psf FWHM --seg gtmseg.mgz --reg template.reg.lta --default-seg-merge --auto-mask PSF .01 --o gtmpvc.output |
mri_gtmpvc --i pet.nii.gz --reg template.reg.lta --psf FWHM --seg gtmseg.mgz --default-seg-merge --auto-mask PSF .01 --mgx .01 --o gtmpvc.output |
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| 6mm |
6mm. Set this to 0 to turn off PVC. --seg gtmseg.mgz is the segmentation created with gtmseg --default-seg-merge will merge several segmentations, eg, all the ventricular CSF segments will be merged into one ROI --auto-mask FWHM .01 will create a mask to exclude voxels from the analysis (generally saves time) --mgx .01 Run Muller-Gartner analysis --o gtmpvc.output This is the output folder. |
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There will be many files in the output folder some of which are described here: |
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By default, this will scale by pons. The output will be gtm.stats.dat and gtm.nii.gz. They both basically have the same information. gtm.stats.dat is an easy to read text file. Where each row is an ROI, something like: |
gtm.stats.dat is an easy to read text file where each row is an ROI, something like: |
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| 17 = index for RO | 17 = index for ROI |
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By default, this will scale by the intensity in pons. If you do not want scaling (eg, when doing a dynamic analysis), add --no-rescale |
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is the PVC uptake of ROI relative to Pons. These can easily be concatenated together (mri_concat) and used as input to mri_glmfit for group analysis. |
is the PVC uptake of ROI relative to Pons. For single time point data, this will be totally redundant with gtm.stats.dat. Use of the NIFTI format makes it easy to concatenate (mri_concat) these files together for group analysis (mri_glmfit). |
PETSurfer provides a set of tools within FreeSurfer for Partical Volume Correction (PVC) and Kinetic Modeling. While these are typically used for PET analysis, the tools can be used in any context where PVC is needed. PVC methods include the Symmetric Geometric Transfer Matrix (SGTM), two-compartment model (also known as the Meltzer Method), three-compartment model (also known as the Muller-Gartner (MG) Method), region-based voxel-wise (RBV). SGTM is used for ROI analysis where as the others are used for voxel-wise analysis. All PVC implementations also accounts for the volume fraction effect. The voxel-wise output can then be analyzed on the cortical surface or in the volume. PETSurfer will be released with FreeSurfer version 6.
In all cases, you will need a T1-weighted MRI of your subject of sufficient quality to run in FreeSurfer. FreeSurfer analysis must be done first. After that, follow the steps below.
1. Create a segmentation for the GTM
gtmseg --s subject
where "subject" is the name of the FreeSurfer subject when you ran recon-all. This creates a high-resolution segmentation (gtmseg.mgz) in the FS folder used to run the PVC methods. This should take about an hour or two. gtmseg.mgz will use aseg.mgz for subcortical structures, ?h.aparc.annot for cortical structures, and will estimate some extra-cerebral structures. There are ways to customize this segmentation to use different ROI definitions (eg, aparc.a2009s instead of aparc).
2. Register your PET image with the anatomical:
mri_coreg --s subject --mov template.nii.gz --reg template.reg.lta
where template.nii.gz is the template image for your PET data. If your PET data only has one frame (eg, an SUV image), then that will be your template. If your PET data has multiple frames (ie, dynamic), then you will need to create the template from the dynamic data. This can be done by extracting a single frame (mri_convert pet.nii.gz --frame frameno template.nii.gz) or averaging all the time frames together (eg, mri_concat pet.nii.gz --mean --o template.nii.gz). It might make sense to do a time-weighted average rather than simple average, but we do not have a tool to do that easily, but you can do it in matlab. For parallel operation, add --threads N where N is the number of CPUs you want to use.
If you are not using PVC, you can use the template.reg.lta to sample the PET volume onto the surface using mri_vol2surf, then apply standard surface-based analysis.
3. Apply Partial Volume Correction
mri_gtmpvc --i pet.nii.gz --reg template.reg.lta --psf FWHM --seg gtmseg.mgz --default-seg-merge --auto-mask PSF .01 --mgx .01 --o gtmpvc.output
--psf FWHM is the full-width/half-max of the the point-spread function of the scanner as measured in image space. Eg, an HR+ is typically about 6mm. Set this to 0 to turn off PVC. --seg gtmseg.mgz is the segmentation created with gtmseg --default-seg-merge will merge several segmentations, eg, all the
- ventricular CSF segments will be merged into one ROI
--auto-mask FWHM .01 will create a mask to exclude voxels from the analysis (generally saves time) --mgx .01 Run Muller-Gartner analysis --o gtmpvc.output This is the output folder.
There will be many files in the output folder some of which are described here:
gtm.stats.dat is an easy to read text file where each row is an ROI, something like:
9 17 Left-Hippocampus subcort_gm 473 174.083 1.406 0.1216
9 = ninth row 17 = index for ROI Left-Hippocampus = name of ROI subcort_gm = tissue class 473 = number of PET voxels in the ROI 174 = variance reduction factor for ROI (based on GLM/SGTM) 1.406 = PVC uptake of ROI relative to Pons 0.1216 = resdiual varaince across voxels in the ROI
By default, this will scale by the intensity in pons. If you do not want scaling (eg, when doing a dynamic analysis), add --no-rescale
gtm.nii.gz is a nifti file with each "voxel" being an ROI. The value is the PVC uptake of ROI relative to Pons. For single time point data, this will be totally redundant with gtm.stats.dat. Use of the NIFTI format makes it easy to concatenate (mri_concat) these files together for group analysis (mri_glmfit).