It is a little complicated, but the GTM just sets up a general linear model where the value at a given vertex is model as a weighted sum of regression coefficients (the uptake in an ROI). Once you have set the ROI boundaries, then it tries to fit the data by finding the regression coefficients that minimize the error. If the ROI boundaries are not set properly, then it may have to make one of the coefficients negative if another is too positive.
On 3/21/2023 4:46 AM, Federica Di Antonio wrote:
External Email - Use Caution
Dear experts,
I report an answer you gave about some negative values avatar partial volume correction:
"[...] Usually this only happens in small ROIs and/or low uptake regions. Why might the model not be perfect? It could be a number of things:
- the MRI-PET registration is inaccurate
- the MRI segmentation is inaccurate
- the ideal segmentation is not reflected by the MRI segmentation
(eg, one of the segmentations has a hot spot)"
I wonder if it is possible for you to give me an hint about the reason why an inaccurate registration or segmentation can cause negative uptake values. At first sight it seemed intuitive to me, but on second thought that's not so obvious.
I would be very grateful for any help!
Federica Di Antonio
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer