Two SVCA algorithms were used to automatically extract reference tissue TACs from dynamic PET scans. Predefined kinetic classes were available from a previous study using data from seven healthy controls and seven patients with traumatic brain injury (Boellaard et al, 2008 (link)). Details concerning this procedure and cluster analysis were reported in Turkheimer et al (2007) (link); however, a summary of cluster analysis is as follows.
The original method (SVCA6) uses six kinetic classes normal gray matter, normal white matter, blood, bone, and soft tissue regions, and gray matter with specific binding. The first five classes were defined on a separate set of normal controls while the last one, corresponding to gray matter with high microglia density, was obtained from the brain injury patients.
To extract the reference, the dynamic PET scan is first normalized as described by Turkheimer et al (2007) (link): each voxel value is reduced by the frame average and divided by the standard deviation. Therefore, the normalization is affected by the size of the reconstructed field of view; for this study, both definition of kinetic classes and application cluster analysis were preformed on scans acquired from the same scanner using similar scanning protocol. However, in case of using SVCA4, normalization was done on voxels that correspond with brain tissue only (based on MRI-derived coregistered gray- and white-matter segmentations). Thereby, this method also avoids the effects of differences in field of view between different scanners.
Next, each voxel TAC of this scan is analyzed using the set of predefined kinetic classes to find the scaling coefficient of each kinetic class, so that the total TAC is equal to the sum of these scaled kinetic classes. As the kinetic classes are not orthogonal, a nonnegative least squares algorithm (Turkheimer et al, 2007 (link)) is used for finding the scaling coefficients. Scaling coefficients of each kinetic class are stored in coefficient maps showing their spatial distribution.
Finally, to extract the reference tissue curve, the coefficient map from the (normal) gray-matter kinetic class is used to calculate the weighted average, as follows: where, N is the number of voxels, TACNS(t) the resulting reference tissue TAC, TACiVoxel(t) the TAC from voxel i of the (nonnormalized) dynamic PET scan, and wiGray the gray-matter kinetic class scaling coefficient estimated for voxel i.
The modified supervised cluster analysis method (SVCA4) (Boellaard et al, 2008 (link)) is similar to SVCA6, except that only four kinetic classes are used: gray matter with specific (R)-[11C]PK11195 binding, gray matter without specific binding, white matter, and blood. This modified method uses the mentioned coregistered segmented MRI scans to exclude skull and soft tissue parts from each frame of the PET scan before performing cluster analysis, same as mentioned above but now with only four kinetic classes. Removal of skull and soft tissue was simply done by setting voxel values to zero for nonbrain structures.
The original method (SVCA6) uses six kinetic classes normal gray matter, normal white matter, blood, bone, and soft tissue regions, and gray matter with specific binding. The first five classes were defined on a separate set of normal controls while the last one, corresponding to gray matter with high microglia density, was obtained from the brain injury patients.
To extract the reference, the dynamic PET scan is first normalized as described by Turkheimer et al (2007) (link): each voxel value is reduced by the frame average and divided by the standard deviation. Therefore, the normalization is affected by the size of the reconstructed field of view; for this study, both definition of kinetic classes and application cluster analysis were preformed on scans acquired from the same scanner using similar scanning protocol. However, in case of using SVCA4, normalization was done on voxels that correspond with brain tissue only (based on MRI-derived coregistered gray- and white-matter segmentations). Thereby, this method also avoids the effects of differences in field of view between different scanners.
Next, each voxel TAC of this scan is analyzed using the set of predefined kinetic classes to find the scaling coefficient of each kinetic class, so that the total TAC is equal to the sum of these scaled kinetic classes. As the kinetic classes are not orthogonal, a nonnegative least squares algorithm (Turkheimer et al, 2007 (link)) is used for finding the scaling coefficients. Scaling coefficients of each kinetic class are stored in coefficient maps showing their spatial distribution.
Finally, to extract the reference tissue curve, the coefficient map from the (normal) gray-matter kinetic class is used to calculate the weighted average, as follows: where, N is the number of voxels, TACNS(t) the resulting reference tissue TAC, TACiVoxel(t) the TAC from voxel i of the (nonnormalized) dynamic PET scan, and wiGray the gray-matter kinetic class scaling coefficient estimated for voxel i.
The modified supervised cluster analysis method (SVCA4) (Boellaard et al, 2008 (link)) is similar to SVCA6, except that only four kinetic classes are used: gray matter with specific (R)-[11C]PK11195 binding, gray matter without specific binding, white matter, and blood. This modified method uses the mentioned coregistered segmented MRI scans to exclude skull and soft tissue parts from each frame of the PET scan before performing cluster analysis, same as mentioned above but now with only four kinetic classes. Removal of skull and soft tissue was simply done by setting voxel values to zero for nonbrain structures.
