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analysis. All PVC implementations also accounts for the volume
fraction effect. The voxel-wise output can then be analyzed on the
analysis. All PVC implementations also account for the volume
fraction effect (VFE). The voxel-wise output can then be analyzed on the

PETSurfer provides a set of tools within FreeSurfer for Partical Volume Correction (PVC) and Kinetic Modeling (MRTM1, MRTM2). 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 account for the volume fraction effect (VFE). 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 (saves time). Output volumes will be reduced to a bounding box around this mask (saves space) --mgx .01 Run Muller-Gartner analysis. 01 is the GM threshold. Only necessary if you want to do a voxel-wise 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).

nopvc.nii.gz - same interpretation as gtm.nii.gz except that the values have not been PVCed in any way.

mgx.{ctxgm,subctxgm,gm} - these are the voxel-wise values corrected using the "extended" Muller-Gartner method. ctxgm is cortical GM, subctxgm is the subcortical GM, and gm is all GM. These volumes will be of the same resolution as the input PET but the field of view will be reduced to that of a bounding box around the mask.

aux - this subfolder has auxiliary data that are often not of immediate use to the user. The exception is bbpet2anat.lta. This is a registration file that can be used to map the output PET volume (in the mask bounding box) to the anatomical space. This file should be used when mapping the volume to the surface, etc.

If you are doing dynamic (kinetic) analysis, thenadd the following arguments when running mri_gtmpvc:

--km-ref 8 47 --km-hb 11 12 13 50 51 52 --no-rescale

--km-ref 8 47 specifies the ROIs to use as the reference region for the MRTM models. 8 and 47 are the cerebellar hemisphers as found in $SUBJECTS_DIR/subject/mri/gtmseg.ctab (see also $FREESURFER_HOME/FreeSurferColorLUT.txt). This creates the file km.ref.tac.dat with the reference value for each time point.

--km-hb 11 12 13 50 51 52 specifies the ROIs to use as the high-binding region if using MRTM2. This creates km.hb.tac.nii.gz with the value for the high-binding region for each time point.

4. Kinetic Modeling (KM). KM is done with either MRTM1 or MRTM2. To run MRTM1:

mri_glmfit --y km.hb.tac.nii.gz --mrtm1 km.ref.tac.dat time.dat --o mrtm1 --no-est-fwhm --nii.gz

where time.dat is a text file withe acquisition time of each time point in the time activity curve (TAC). This will create a folder called mrtm1 in which will be a file called k2prime.dat. The value of k2prime will be used in the MRTM2 analysis below

For the MRTM2 analysis

set k2p = `cat mrtm1/k2prime.dat`
mri_glmfit --y gtm.nii.gz --mrtm2 km.ref.tac.dat time.dat $k2p --o mrtm2 --no-est-fwhm --nii.gz

This will create a folder called mrtm2 in which the bp.nii.gz will be the non-displaceable binding potential (BPND) for each ROI.

PetSurfer (last edited 2020-07-27 10:45:02 by DougGreve)