The correction of metabolite is performed by scaling imput function
by a parent function.
This parent function is choosen from aviable ones in pet.json
configuration file, in section file, together with it's parameters.
For mean over 4 participants the best fit produce following parameters:
A0: 1 (fixed)
e: 76.0390292 +/- 25.2213235 (33.17%) (init = 0)
a: 0.92514893 +/- 0.02308516 (2.50%) (init = 1)
b: 230.814420 +/- 38.6266001 (16.73%) (init = 683.7752)
To insert into pet.json
:
"metabolite":{
"method": "Sigmoidal",
"parameters": {
"A0": 1,
"e": 76,
"a": 0.925,
"b": 231
}
}
General model Exp2:
fitresult(x) = a*exp(b*x) + c*exp(d*x)
Coefficients (with 95% confidence bounds):
a = 0.9538 (0.3682, 1.539)
b = -0.001434 (-0.00326, 0.0003923)
c = 0.1069 (-0.4965, 0.7104)
d = 3.562e-05 (-0.001228, 0.0013)
To insert into pet.json
:
"metabolite":{
"method": "DoubleExp",
"parameters": {
"a": 0.9538,
"b": -0.001434,
"c": 0.1069,
"d": 3.562e-05
}
}
x = [0, 180, 300, 900, 2100, 3600, 5400];
y = [1, 0.92, 0.76, 0.29, 0.19, 0.14, 0.12];
For PVC you will need an extrnal tool petpvc installed and added to path.
The FWMH was estimated by gaussian fit of point source image
using tool pet_fwhm.
Results where averaged between centrally placed and z = 10
cm:
"FWHM": [6.48, 6.58, 4.67]
For the Logan plot, you will need a magia toolbox