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:
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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:
1. the MRI-PET registration is inaccurate
2. the MRI segmentation is inaccurate
3. 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
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