Our new preprint is out! We evaluated different modeling strategies for linear combination modeling of GABA-edited MRS at 3T. Parameterizing the co-edited macromolecules at 3-ppm improved overall fitting performance.
Different modeling strategies were tested: combining six approaches to account for co-edited MMs, three modeling ranges, three baseline knot spacings, and using basis sets with and without homocarnosine.
Significantly different GABA+ and GABA estimates were found when a well-parameterized MM basis function at 3 ppm was included. The mean GABA estimates were significantly lower when modeling MM, while CVs remained similar. Sparser knot spacing led to lower variation in the GABA and GABA+ estimates and a narrower modeling range - only including signals of interest - did not substantially improve or degrade modeling performance. Additioanlly, results suggest that LCM can separate GABA and the underlying co-edited MM.
GABA-edited MRS is best quanitfied by LCM with a well-parameterized co-edited MM basis fucntion constraint to the non-overlapped MM0.93 in combination with a sparse spline knot spacing and a mdoeling range between 0.5 and 4 ppm.