Dear Nina,
We are doing something similar to what you describe in terms of using Literature and/or Real qMRI water relaxation times. There is a stat.csv file in Osprey, where you could provide some data which is missing from the input files (for example qMRI values for every voxel) and with a small code modification these values would be used. Do you already have a technique to extract the qMRI values from the voxels? We have just released a tool which can do this (if you use Siemens or Philips).
The quantification questions are not related to the “Osprey” or “LCModel” options in the job file at all. These two are just the algorithms which will be used solely for the spectral fitting (i.e. obtaining intensities of the for the metabolites and reference water). You should choose between them depending on how they (and you) manage. I believe that for the spectra measured in the normal-appearing cerebral tissue (even if the participants are patients and not healthy subjects) Osprey could be a better choice, it’s easier. For the difficult cases for example High-Grade tumors LCModel might be a better option (my experience). It has to be checked with your data.
All the quantification is performed later at the Quantify step. It’s hard to speak about the possible bias in your study cohort without having more information. Since everything (CSF-corrected, TissCorrWaterScaled and so on) is quantified in one pipeline, you can get all the concentrations quantified differently, and then after careful thinking address the quantification/statistical approach if you have some suspicions.
Best regards,
Andrei
P.S. Just in case: I’m not an Osprey developer