Diana Georgiana Rotaru^{1} and David John Lythgoe^{1}

Low concentration metabolites with J-coupled peaks are difficult to quantify using MRS, even using MEGA-PRESS or its recently propose HERMES implementation. Using LCModel with simulated basis sets can lead to inaccurate metabolite quantification, since the spatial-dependence of the signal is often not fully accounted for. This can be minimised by increasing the number of spatial positions simulated, at the expense of computation time. We simulated basis sets for MEGA-PRESS and HERMES using the recently introduced approach based on one-dimension projections, as a replacement for the commonly used three-dimensional method, providing more accurate basis sets in shorter computation time.

A HERMES acquisition sequence was
implemented on a 3T MR750 General Electric scanner. LCModel was used to
quantify the edited signal, after pre-processing with a FID-A^{3} based
pipeline. Data from six healthy volunteers (mean(sd) age=34(10) years) were
acquired (TR=2s, TE=80ms, 352 averages) and fitted using basis sets simulated
for 1×1×1 (excitation×refocusing1×refocusing2), 1×17×17 and 201×201×201 spatial
positions. Three steps were applied to create each basis set: a) simulation of
excitation pulse (and gradients) followed by spin evolution and calculation of the
average density matrix, b) simulation of first refocusing and editing pulses
(and gradients), along with spin evolution for each spatial position, followed
by calculation of the average density matrix, c) simulation of second
refocusing and editing pulses (and gradients) and spin evolution for each spatial
position, and the averaged density matrix calculated. For steps b) and c) the
averaged density matrix from previous step was used as starting point for all
positions simulated. A 1×17×17 simulation was also performed using the
conventional 3D approach, for comparison.

The goodness of fit for results from each basis set was estimated as the standard deviation of the LCModel fit residual from 2.12 – 3.86 ppm divided by the height of the NAA peak. These were compared between the 201×201×201 point and single point basis sets using a paired t-test.

Figure 1 shows the results of the 1×17×17 HERMES simulation for GABA for TE = 80 ms, using the conventional approach of simulating each sub-voxel individually and using Zhang’s 1D projection method. The difference between the two simulations is also shown. Figure 2 shows examples of the LCModel fits for GABA using a basis set simulated at the centre of the voxel, compared with using a basis set simulated for 201×201×201 locations, using 1D projections.

For the basis set comparisons, the goodness of fit measures for the 201×201×201 and single point basis sets were (mean(sd)) 0.0131(0.0022) and 0.0164(0.0019) respectively (p=0.00004).

Zhang^{4} previously
implemented a method to speed up calculation of MRS basis sets for PRESS and
TE-averaged PRESS. We successfully implemented their method using FID-A in
Matlab to calculate basis sets for GABA and GSH (not reported here) difference
spectra from HERMES MEGA-PRESS. There was no difference between basis sets
calculated using Zhang’s method and the more traditional approach. The basis
set calculated over 201×201×201 spatial locations improved the accuracy of LCModel fits compared with calculating the basis set from a single position.

Using Zhang’s method allows a massive speed-up in performing MRS simulations. Use of an accessible language like Matlab, allows the method to be implemented without recourse to lower level languages like C++ and the GAMMA library. The only drawback with the method is that information on spatial position is lost, which may be important when studying the effects of chemical shift displacement error.

1. Chan KL, Saleh
MG, Oeltzschner G, Barker PB & Edden RAE, Simultaneous measurement of
Aspartate, NAA, and NAAG using HERMES spectral editing at 3 Tesla, *Neuroimage*, 2017, **155**, 587–593.

2. VeSPA Simulation Library: http://scion.duhs.duke.edu/vespa/

3. Simpson R,
Devenyi GA, Jezzard P, Hennessy TJ & Near J, Advanced Processing and
Simulation of MRS Data Using the FID Appliance (FID-A)—An Open Source,
MATLAB-Based Toolkit, *Magn Reson Med*,
2017, **77**, 23–33.

4. Zhang Y, An L
& Shen J, Full density simulation of spatially localised magnetic resonance
spectroscopy for multi-spin systems, *Proceedings
of the 25th Annual Meeting of the ISMRM*, 2017, 5488.

Comparison between
conventional simulation for GABA (1×17×17) and the simulation using 1D
projections (1+17+17). The difference shows the results are identical for both
simulation methods.

Example LCModel fits for
GABA sub-spectrum from a HERMES data set, using a basis set calculated from a
single point at the centre of the voxel, and a basis set simulated over 201×201×201
sub-voxels, using Zhang’s method. The fit for the GABA peak just up field of
NAA is improved using the 201^{3} basis set.