Assuming that you also have the CBF observations for creating the model accurately, the problem essentially boils down to fitting a linear regression model with PET as the input and output as the CBF, the constant factor would be the intercept of the fitted model and the dependent factor would be the parameter of the fitted model.
You can use the fitlm() method (https://www.mathworks.com/help/stats/fitlm.html) for doing the fitting. I'm not sure how the regions factor into this but you can specify that as a Categorical variable.
Something similar to this,
mdl = fitlm(InputData, CBF, 'CategoricalVar',2,'Exclude', [1 4])
- InputData = Table that you have shared above
- CBF = CBF Observations for each row
- 'CategoricalVar', 2 = Treat the 2nd column, i.e. region as a Categorical variable
- 'Exclude', [1 4] = Exclude the 1st and 4th columns, i.e. subject and ASLs from the fitting process.
Use this page to understand which properties can be accessed from the fitted model object, https://www.mathworks.com/help/stats/linearmodel.html
You can find the independent and dependent factors from the property, mdl.Coefficients where mdl is the fitted model object.
