I tried to analyze my MEGA-PRESS GABA data (3T Siemens Prisma) in Osprey. Few important parameters of the scan data are - 64 averages, TE = 68ms, and TR = 2000 ms (TWIX data format). Osprey ran smoothly from loading to fitting the data. Though, the fit for the ‘off’ data looks good (the residual looks flat) but the fit for the ‘difference’ data doesn’t look good visually. The residual shows a big peak at 3 ppm/GABA position. I tried all the spectral registration methods to improve the fitting, but the fitting remains all most same in all spectral registration methods. What can I do to improve it? It will be really helpful if I can fit the GABA peak well.
I checked the same data in GANNET, the fitting is not bad. I attached screenshots of the fitting in the GANNET and Osprey LC model(RobustSpec Reg)OFF1_GABAGlx_vox1_fit.pdf (323.1 KB)
Also, another naive doubt, why does the GABA peak fit in the Gannet a unimodal lorenztian function, but, in the Osprey LC model or in the LC model, it looks like a bimodal shape function? Though they are at 3ppm, the same GABA proton.
Swagatameas_MID00026_FID22782_eja_svs_mpress_Gaba_1_Voxel_1_OspreyFit_diff1_diff1.pdf (393.7 KB)
meas_MID00026_FID22782_eja_svs_mpress_Gaba_1_Voxel_1_OspreyFit_off_off.pdf (422.9 KB)