MEGA-PRESS Study Design Help

Study & Experiment Design
2

Hi Zanetta,

A couple thoughts:

  • Anything prefrontal/frontal tends to be more difficult to shim. What is your shim procedure before you go to the interactive shim? Some folks have recommended doing the regular GRE or brain shim three times in a row. If you have access to the CMRR spectro package, consider the FASTMAP/FASTESTMAP modules, which can give good results in tricky regions.
  • Shim is one thing, but another huge problem in your mPFC spectrum is an awful lot of the spurious echoes (high-frequency oscillations) that you see in your mPFC spectrum; they tend to occur a lot in tricky-to-shim regions. One thing you can experiment with to ameliorate that is the order and orientations of the slice-selective gradients (I believe that on the Siemens this is in Routine → Orientation, and I can’t recall the name of the other option, but it allows to try different orders - send a screenshot of the exam card tabs and I can take another look). See this thread and this paper.
  • Your SVS Edit 1 and 2 protocols in the OCC look good to me and have approximately what I consider good SNR (we typically tell people to measure for 10 mins in a 27-ml voxel at TR = 2 sec).
  • I would advise against going lower on TR (introduces T1 weighting to all of your signals), voxel size, or the number of transients. Yes, I know everyone wants to keep their scans as short as possible and measure in the smallest possible volume, but in order to measure GABA+ reliably, you can’t beat the laws of physics/SNR. I’d say your signals look just about acceptable in the KC protocol, but this operates the very lower end of what I usually recommend for SNR (the KC protocol is already at just about 40% of that size and 80% of that measurement duration, so your SNR is probably down to about 30% of what I’d let people start with). My advice is usually that it’s better to sacrifice one region of interest but at least get interpretable data rather than a bunch of noise.

For your Osprey analysis, the three algorithm choices you mention only pertain to the spectral alignment, not the modeling. Based on the initial three KC plots, RobSpecReg appears to be much better in aligning than RestrSpecReg, which gives massive subtraction artefacts in the GABA region.

You will definitely want to include modeling of the co-edited MM resonance at 3 ppm. This is more thoroughly explained in the documentation - we should probably add an example job file for TWIX data as well (you’re the second person this week that built from the example file for un-edited data and missed this crucial part), but you can find a good template in the Philips MEGA example. This will substantially improve the modeling of the 3-ppm signal compared to what you have now.

Specifically, add:

% Add macromolecule and lipid basis functions to the fit?
opts.fit.fitMM              = 1;                % OPTIONS:    - 0 (no)
                                                %             - 1 (yes, default)

% How do you want to model the co-edited macromolecules at 3 ppm for GABA-edited MRS?
opts.fit.coMM3              = '3to2MM';      % OPTIONS:    - {'3to2MM'} (default)
                                                %             - {'3to2MMsoft'}
                                                %             - {'1to1GABA'}
                                                %             - {'1to1GABAsoft'}
                                                %             - {'freeGauss'}
                                                %             - {'fixedGauss'}
                                                %             - {'none'}

opts.fit.FWHMcoMM3          = 14;

You will then need to report the composite of GABA+macromolecules (GABA+, which is provided by OspreyQuantify), because GABA alone cannot be reliably estimated from a MEGA-PRESS experiment.

HTH,
Georg

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