HERMES: Hadamard Encoded Editing

ISMRM 2016 was an exciting one for our group, as student Kimberly Chan presented HERMES: Hadamard-Encoding and Reconstruction of MEGA-Edited Spectroscopy.

HERMES: Coming to a journal near you in the very near future.

HERMES: Coming to a journal near you in the very near future.

In principle, HERMES allows the orthogonal encoding of an arbitrary number of edited metabolites in a single acquisition.  Rather than jettisoning all the 'other' signals as with MEGA editing, HERMES can put them into a second difference spectrum, or a third, or...

 

Tissue-Corrected GABA Values in Gannet

We have recently published a paper proposing a tissue correction that accounts for voxel segmentation, both in terms of water relaxation/visibility, and the different GABA concentration in gray matter and white matter.
This paper can be found here.

The different corrections proposed are now implemented in Gannet, with output names that are not entirely clear... so, here are the equivalencies.

MRS_struct.out.GABAconciu
This is the uncorrected GABA concentration (relative to water).

MRS_struct.out.GABAconciuTissCorr
This is the CSF-corrected GABA concentration (relative to water).  (We should probably change the naming !).  It is MRS_struct.out.GABAconciu divided by (f_GM+f_WM), and is filled by GannetSegment.

MRS_struct.Quantify.QuantTissCorrGABA_iu
This is the GABA concentration (relative to water), corrected for the visibility and relaxation of the water signals in GM/WM/CSF and for the GABA concentration differential between GM and WM (alpha in the paper).  It corresponds to Equation 5 in the paper. It is filled by GannetQuantify.

MRS_struct.Quantify.QuantNormTissCorrGABA_iu
This is the most fully corrected GABA concentration (relative to water), corresponding to Equation 6 in the paper. It is filled by GannetQuantify.  This is what we recommend as the measure to be used.

GABA phantom recipe

I am going to port some of the key posts off the old blog, starting with this...

It seems that there is some value in a standardized 10 mM GABA phantom for cross-platform testing etc... This recipe is also a reasonable place to start if you want a 'will-work-without-much-thought' option.  Everything is available form Sigma Aldrich.

  • Container: 1 liter Nalgene bottle style 2125
  • Buffer: One PBS sachet P5368
  • GABA: 1.03 g (RMM 103.1) A2129 

Make up to 1 liter with deionized water.  pH can be adjusted using NaOH/HCl (but to a first approximation does not need to be).

It might also be worth adding 0.75g of Glycine, which gives a singlet that doesn't overlap with any GABA signals. That would give an internal 10 mM standard that is useful in some situations for referencing. 

Can we make the voxel smaller?

A common question that comes up with new collaborators (or old ones!) is "Can we make the voxel smaller?" or "Can we make the measurement shorter?", or sometimes, for the most ambitious, both.

My usual reply is "Yes, you can. But you should be aware of the consequences".

MRS in general, and edited MRS particularly, works at low levels of signal-to-noise.  MR is an inherently insensitive technique and we are detecting signals from millimolar metabolites within the head using an array of coils outside the head.  

SNR is determined by several factors, most of which are unchangeable: coil size; coil geometry; field strength; etc.  The main variables that are, well, variable are the number of averages (i.e. scan time) and the voxel size.  

SNR is directly proportional to voxel size, so the SNR of a 3x3x3 cm^3 voxel is over three times that of a 2x2x2 cm^3 voxel.  Even reducing voxel size by 10% linearly (which has an almost negligible impact on its apparent size) will reduce SNR by 30%. So my feeling is that you lose SNR far more quickly than you feel like you are gaining resolution.

SNR is also proportional to the square-root of the number of averages. This means that 'buying back' SNR losses from reducing voxel size with increased scan time is very expensive.  To regain that 30% signal, you would have to scan for twice as long... not a good trade.  The flip side of this is that reducing scan time hurts you relatively slowly.  Decreasing the scan time by 20% only impacts SNR by 10%.

The reason that this question often comes up it that people want spatial specificity to be as high as possible, and either want good temporal resolution or rapid scan times.  This is almost universally true, so experiments are already defaulted to the lowest SNR that I feel comfortable with.  For GABA, the 3x3x3 voxel scanned for 10 minutes also coincides with the mean SNR-factor from a literature review of GABA-edited  MRS.

But the quick answer is, "yes, you can reduce voxel size or scan time, but they will impact SNR".  If you want to do both, reducing scan time will hurt you less.  

Gannet, a website

Website screenshot

Website screenshot

It is clearly past the time when Gannet should have a website.

There is also a lot to be said for moving the blog off blogspot, which is blocked in China, so useless to some collaborators.  

So, here we go.