The day-to-day stability of GABA is an important assumption for many research projects, particularly those investigating neurochemical change/difference as a result of intervention or disorder. While reliability studies have typically found baseline MRS measures of GABA to be stable, some work has pointed to systematic changes linked to the menstrual cycle. Consequently, some researchers have since chosen to include only males within their samples, which can be deeply problematic for the generalisability of findings. Previous work exploring GABA change across the menstrual cycle has involved relatively small samples and been restricted to a small number of anatomical regions. Therefore, we are proposing to extend and expand upon this important work by running a large multisite project using a standardized methodological approach. Our proposal is for each site to collect data from 1-3 naturally cycling female participants before pooling the data. We are very keen to hear from research teams interested in getting involved in the project. Please contact – Katherine.dyke@nottingham.ac.uk for details.
Recording Excellent Phantom Spectra on the Philips platform
The purpose of this post is to describe our standard process for scanning phantoms, and describe phantom-specific parameters that we use. Acquiring excellent-quality phantom spectra on clinical scanners is a real challenge, which requires a certain amount of witchcraft.
Excellent phantom spectra have narrow lines (~1-2 Hz), so the individual lines of multiplets are resolved, and flat undisturbed baselines to enable accurate phasing and integration.
The first step of acquiring excellent phantom spectra is preparing an excellent phantom (old instructions for a good GABA phantom).
Relatively high concentration (~10 mM) is a great start, since phantom experiments often involve parameter series of >10 individual experiments.
Sodium azide helps keep the bugs down (and build-up of gunk impacts shimming).
Store your phantom in a dark fridge to minimize breakdown, but also know your metabolites (GABA lasts ‘forever’, GSH less well, I think).
Use a bottle with friendly geometry that doesn’t result in violent field distortions (we used to use old peanut jars or milk cartons, and they were a lottery on this count). The 1 liter Nalgene bottle style 2125 is fine without being perfect. Spherical containers are better and more contentrated.
Avoid air bubbles. It’s better to fill the phantom up (and sacrifice exact concentration) that sacrifice shimmability.
The second step is placement. Try to locate your phantom centrally within the head coil and the main B0 field. A perfect spherical phantom at isocenter in a perfect magnet shouldn’t need shimming, so the closers you can get your ‘cylindrical’ phantom aligned with the central axis of the magnet, the better. If your phantom has an air bubble (most do), it is usually worth tilting the phantom to keep it up one end (ideally in the ‘cap’ region) rather than along the full light of the bottle. Placement judgment and luck in equal measure; sometimes I spend 20 minutes trying to optimize shim and WS on a phantom and just give up. After physically moving it within the bore, I start again.
Even with good shimming, phantoms are less homogeneous than we’d like. Voxel sizes of 2x2x2 cm^3 often yield (noiser) spectra with narrower lines than 3x3x3 cm^3 voxels.
On the Philips system, we use the pb-auto projection-based shimming routine, and it performs well (and quickly) both for phantoms and in vivo. However, you don’t have the option to tweak individual shim currents to optimize the line width manually, so excellent phantom shimming involves an iterative process of slightly moving and/or rotating the voxel, re-running the prep and checking the line width. The reported line width (in the log box) is an imprecise indication, but mostly we rely upon viewing the spectra to check linewidth.
The final piece of the puzzle is water suppression (WS). We tend to use the ‘exc’ option with WS optimization ON for phantom experiments. The automated optimization does a good job of keeping baseline disturbances from residual water signal to a minimum. The quality of WS achievable is dependent on shim quality, and we tend to optimize shim and WS at the same time. Again, sometimes you just can’t get things nice enough, even after moving the voxel around, so you have to reposition the phantom in the scanner. I tend to schedule 30 minutes to get great shim and WS, and then the time to acquire experiments, so 1 hour is never enough to get a full phantom dataset (even when it is technically ‘enough’). Take you time, and get everything just right. This also helps with not mis-entering a parameter series, another common failure mode of phantom experiments!
One final note on chemical shift: your phantom is likely to be at room-temperature and ‘close-to-physiological’ pH. Chemical shifts and coupling constants can depend on temperature (and pH) and for our commonest phantoms the water line tends to come at ~4.8 ppm. For frequency-selective experiments like edited MRS, you need to account for this in setting editing pulse frequencies. This is handled in our patch via the BASING parameter ‘water freq’. The scanner tends to assume the water line comes at 4.68 ppm, so we can infer the correct water shift from a processed spectrum by checking how far off-resonance the metabolite signals appear in teh SpectroView window.
Optimizing Voxel geometry on the Philips platform
One common artifact that we see in MR spectra are unwanted water echoes. The spectrum below shows this behavior around 4 ppm. It manifests as a region of the spectrum with ‘locally increased noise’, which is more accurately considered as a broad signal with a large first-order phase error.
This happens when the sequence accidentally refocuses water signals from outside of voxel OOV (or merely fails to fully suppress them with the gradient pulses), and can seriously interfere with quantification. For a given brain location and acquisition protocol, these artifacts are relatively consistent and so can be ‘optimized away ‘. This process is something we advise collaborators to do every time they set up a protocol for a new brain region.
The key parameters are the voxel orientation (transverse/coronal/sagittal) which changes the ordering of the slice-selective pulses within the PRESS sequence (or rotates the experiment 90 degrees about a cube-face) and the water-fat shifts (L/R etc) which flip the slice-selective gradients one-by-one (reflecting the experiment spatially a plane parallel to a cube-face).
Before you work on a challenging brain region, make sure your sequence works in PCC or some other ‘easy’ region with which you have experience. When you are sure the sequence is set up correctly, then you can move onto this geometric optimization.
Step 1: Run an in vivo scan in a new brain region. Use 320 averages and ~25ml volume so you know what to expect in terms of SNR. If the spectrum looks ‘clean’ with respect to these OOV echoes, then I would not continue with this process. Run a new subject and hopefully that’ll look fine too.
Step 2: If you see OOV echoes, run scans with all three options of the orientation parameter, and select the one that gives the smallest OOV echoes.
Step 3: With that orientation, start working on the water-fate shifts. One quick option is to flip all three and see if it solves your problem. Hopefully you can see the OOV echoes in the Spectroscopy tool on the scanner. For each water-fat axis, select the option that gives the smallest OOV echoes.
This process is not 100% reproducible across subjects, but it is enough to be useful.
One final note: The data above were acquired from this midline ACC voxel. The orange box corresponds to the 3 ppm signal and the white to the chemical-shift-displaced water signal. When setting up a new voxel location, I tend to recommend setting the water-fat shifts to put the white box ‘inside‘ the orange box. By ‘inside’, I mean away from scalp and other challenging areas.. towards the middle of the brain….
Postdoctoral Fellowship Openings
We are looking to recruit two postdoctoral fellows to join our team developing new acquisition and analysis methods for MRS. Any prior experience with MRI or MRS is applicable, we are just looking to recruit someone curious and motivated to join our team. If you might be interested do reach out by email ( raee2 at jhu dot edu), as I am keen to talk to any interested candidates. Whatever your background, exspecially if it differs from ours, we want to hear from you.
We have active projects investigating oxidative stress in autism and are developing the tools for a large multi-site project imaging the neonatal brain. A postdoctoral fellowship in our lab is an excellent way to build up your skills for a successful independent research career. Don’t hesitate to drop us an email.
A reading list on Advanced Editing
In setting up a new project, I recently wrote a reading list for someone coming new to edited MRS from outside the area. I thought it would be useful to post here for future reference and for anyone getting started.
Before you understand Advanced Editing, you need a grasp of edited MRS in general, so I’d suggest starting with a couple of reviews. There is other literature out there (including by other groups), but I reference our papers and the minimal reading.
Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA. Neuroimage. 2014;86:43-52. doi: 10.1016/j.neuroimage.2012.12.004. Epub 2012 Dec 13.
This review focuses on J-difference-edited MRS of GABA, but should be a good starting point for the basic principles.
Edited 1 H magnetic resonance spectroscopy in vivo: Methods and metabolites.
Harris AD, Saleh MG, Edden RA. Magn Reson Med. 2017 Apr;77(4):1377-1389. doi: 10.1002/mrm.26619.
This review broadens out into other metabolites that can be edited and also includes a little more detail about the mechanism of J-difference editing.
Then, moving beyond editing in general, some papers that describe editing more than one metabolite, using HERMES and HERCULES
HERMES: Hadamard encoding and reconstruction of MEGA-edited spectroscopy.
Chan KL, Puts NA, Schär M, Barker PB, Edden RA. Magn Reson Med. 2016 Jul;76(1):11-9. doi: 10.1002/mrm.26233.
This paper described the idea of Hadamard encoded editing and its application to separating signals from NAA and NAAG.
Simultaneous edited MRS of GABA and glutathione.
Saleh MG, Oeltzschner G, Chan KL, Puts NAJ, Mikkelsen M, Schär M, Harris AD, Edden RAE. Neuroimage. 2016 Nov 15;142:576-582. doi: 10.1016/j.neuroimage.2016.07.056.
We then applied the same principle to editing GABA and GSH in a single experiment.
Advanced Hadamard-encoded editing of seven low-concentration brain metabolites: Principles of HERCULES. Oeltzschner G, Saleh MG, Rimbault D, Mikkelsen M, Chan KL, Puts NAJ, Edden RAE. Neuroimage. 2019 Jan 15;185:181-190. doi: 10.1016/j.neuroimage.2018.10.002.
Next, we tried to edit everything in a single experiment HERCULES, which used a more elaborate editing scheme and multi-spectrum modeling.
In addition to editing more than one metabolite, we have also developed ways of editing more than one voxel, using PRIAM.
Parallel reconstruction in accelerated multivoxel MR spectroscopy.
Boer VO, Klomp DW, Laterra J, Barker PB. Magn Reson Med. 2015 Sep;74(3):599-606. doi: 10.1002/mrm.25718.
The original PRIAM concept was developed by Vincent Boer…
Dual-volume excitation and parallel reconstruction for J-difference-edited MR spectroscopy.
Oeltzschner G, Puts NA, Chan KL, Boer VO, Barker PB, Edden RA. Magn Reson Med. 2017 Jan;77(1):16-22. doi: 10.1002/mrm.26536.
…and it was applied to editing here. This paper includes proof-of-principle for the combination of HERMES-PRIAM for multi-voxel, multi-metabolite editing.
OSPREY: An introductory video
Universal HERMES of GABA and glutathione now includes ethanol
There is currently little understanding of the acute effects of ethanol (EtOH) on the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) and the antioxidant glutathione (GSH) levels. Previous MRS studies have demonstrated changes in GABA levels in individuals recovering from alcohol use disorder. Alcohol has also been shown to deplete GSH levels, impeding the elimination of reactive oxygen species.
Sequential MEGA-PRESS measurements of GABA, GSH, and EtOH would necessitate 30-min acquisitions, limiting the number of brain regions investigated within a typical 1-hr MR examination and the time resolution of dynamic studies. Muhammad Saleh and colleagues have extended the universal HERMES editing of GABA and GSH to include orthogonal editing of EtOH without an increase in scan time or substantial loss in spectral quality (see figure below). This new sequence is available collaboratively to the community for application in future studies.
a) The separate Experiments A-D are shown: saturated NAA signal in the ON_GABA spectra; saturated water signal in the ON_GSH spectra; and several signals saturated in the vicinity of 3.67 ppm in the ON_EtOH spectra. The saturation range of the editing pulses is shown on each spectrum as a grayscale. b) The Hadamard combinations yield GABA-edited (A+B–C–D), GSH-edited (A+C–B–D), and EtOH-edited (B+C–A–D) spectra.
Processing phantom data through Gannet
Some Gannet users have asked us whether Gannet can process phantom data. The answer is yes! Gannet can load and fit phantom data for GABA, GSH, Glx, Lac, and EtOH edited data acquired either by MEGA-PRESS or HERMES.
Note that to quantify your measurements you will need to also have an unsuppressed water signal acquisition to use as a concentration reference.
For example, the following steps would allow you to process and quantify example Lac-edited phantom data acquired on a Siemens scanner:
Download the latest version of Gannet.
Open GannetPreInitialise.m and make sure the settings match the screenshot in Figure 1 below, making sure MRS_struct.p.phantom is set to 1.
Save your changes and enter the directory containing your phantom data.
Run GannetLoad using the following command (see the output in Figure 2):
GannetLoad({'meas_MID00380_FID11555_UNIV_Lac_TE140_22ms_1024_1khz.dat'},{'meas_MID00382_FID11557_UNIV_Lac_TE140_H20.dat'});Then run GannetFitPhantom to fit and quantify the edited Lac signal (see the output in Figure 3):
MRS = GannetFitPhantom(MRS);
Figure 1
Figure 2
Figure 3
Finally! It's here - Universal Edited MRS
MEGA-PRESS, a spectral editing method, has gained popularity in the MRS community thanks to its ability to edit low-concentration metabolites with relative ease of implementation, allowing direct and unambiguous measurements of the inhibitory neurotransmitter GABA, the antioxidant glutathione (GSH), and the anaerobic product lactate (Lac). However, current implementations of MEGA-PRESS are diverse across vendors, differing in terms of RF pulse shapes and pulse sequence timings. Recent multi-site data revealed that ~30% of the variance in the GABA+ data is attributed to the site- and vendor-level differences in the implementation of MEGA-PRESS.
Recently, Muhammad Saleh and colleagues developed a new universal MEGA-PRESS sequence for the major MR vendors (Philips, Siemens, GE, and Canon) with common RF pulse shapes and timings, and has functionality for HERMES editing of GABA and GSH (Figure 1). Upon comparing with the existing vendor-native sequences, the universal sequence yielded edited spectra with strong correlations and low variance among vendors at both short and long TEs phantom and in vivo experiments (Figures 2 and 3). The universal sequence includes simultaneous editing of GABA and GSH with HERMES, allowing excellent separation of the edited signals in half the scan time compared with the sequentially acquired conventional MEGA editing. The universal sequence is available collaboratively to the community for application in future studies.
Exciting times ahead: Building on the universal sequence, Georg Oeltzschner and colleagues developed HERCULES sequence, capable of editing seven coupled metabolites that would usually require a single 11-min editing experiment each. This sequence is also available collaboratively to the community for application in future studies.
For more updates, stay tuned for any new developments from Edden's lab. Universal edited MRS sequences will be presented at the 27th Annual Meeting of the ISMRM, Montreal, Canada, 2019. Looking forward to meeting you all.
Figure 1: Pulse sequence diagrams indicating RF pulse shapes and timings for the vendor-native Philips, Siemens, GE and Canon sequences, and the universal sequence at TE = 68 ms.
Figure 2: a) MEGA-PRESS experiment using the GABA phantom (TE = 68 ms) and Lac phantom (TE = 140 ms) from vendor-native sequences (left) and the universal sequence (right). b) Edited spectra from HERMES experiments acquired using the universal sequence, performed in a GABA phantom (left) and a GSH phantom (right).
Figure 4: In vivo experiments using the universal sequence. Spectra acquired on Philips and Siemens scanners are overlaid for each subject: a) MEGA-PRESS GABA (TE = 68 ms) and Lac (TE = 140 ms) spectra; b) GABA- and GSH-edited HERMES spectra. In vivo experiments using the universal sequence for GE (green) and Canon (red) scanners: c) MEGA-PRESS GABA-edited (TE = 68 ms) and Lac-edited (TE = 140 ms) spectra; d) GABA- and GSH-edited HERMES spectra. Spectra acquired are overlaid on the ± 1SD range (in gray) of the amalgamated Philips and Siemens data (6 subjects).
HERMES hits the road
Exciting times! Postdoctoral fellow Muhammad Saleh is doing his first site visit, setting up HERMES with one of our Siemens collaborator sites. This sequence, available via C2P agreement, performs MEGA-PRESS (at variable TEs with correct editing pulse spacing), as well as HERMES for GABA/GSH. We are developing sequences with the same RF timings on the other vendor platforms as part of our 'universal editing' project.
Gannet supports Siemens Hermes data
Gannet now provides support for Hermes data acquired using Siemens 3T scanner. The acceptable format is .dat. Please note that it is beneficial to acquire metabolite data and water data using the same sequence for better coil combination. Below is the GannetLoad window displaying Siemens HERMES data before and after post-processing steps in Gannet.
HERMES of GABA and GSH at 7T
Extending the multi-metabolite editing technique at high field scanner, we have developed HERMES of GABA and GSH at ultra-high field scanner. For this, M.G. Saleh and colleagues implemented semi-LASER (sLASER) voxel localizer and Hadamard encoding scheme (Figure a) at 7T to simultaneously edit GABA (at 3 ppm) and GSH (at 2.95 ppm) in half the scan time MEGA-sLASER takes to edit these metabolites sequentially (Figure b). HERMES of GABA and GSH will be presented at 27th Joint Annual Meeting of the ESMRMB-ISMRM, Paris, France, 2018.
Gannet now listed on NITRC!
Gannet was recently added to the software catalogue of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).
Please follow this link to visit the page.
We will be adding to this page in the coming weeks. Stay tuned for further exciting developments....
GABA and GSH in 5 minutes?
Soooo.... this was obviously a terrible idea. And we were only testing that the sequence working, but.....
This a believable GSH spectrum, acquired in 5 minutes (3x3x3 in PCC). And we got GABA too....
Running a GSH-edited MEGA-PRESS dataset through Gannet 3.0
We have improved the data processing and quantification routines for GSH-edited MEGA-PRESS data in Gannet 3.0. Here is a quick "getting started" tutorial on running a GSH-edited MEGA-PRESS dataset:
Download Gannet 3.0, placing the master folder at the top of your MATLAB's search path.
Open GannetPreInitialise.m.
Set MRS_struct.p.target to 'GSH' (MRS_struct.p.target2 can be ignored).
Set MRS_struct.p.ONOFForder to 'offfirst' or 'onfirst' depending on the order of the ON/OFF editing pulses in your MEGA-PRESS acquisition (typically this should be 'offfirst' for Philips data and 'onfirst' for GE/Siemens data).
Set MRS_struct.p.water_removal to 1 (this is the default) to remove residual water in the difference spectrum.
Set MRS_struct.p.AlignTo to 'SpecRegHERMES'. (We recently published a novel frequency-and-phase correction algorithm for multiplexed edited MRS data [1]; this method also works well with GSH-edited data and is recommended.)
Set MRS_struct.p.GSH_model to 'SixGauss' (recommended for long-TE MEGA-PRESS data).
Save your changes.
Change your working directory to where your GSH dataset is saved.
To run GannetLoad on:
GE data, run: MRS = GannetLoad({'GSH_MEGA_1.7'});
Philips SDAT data (no water reference), run: MRS = GannetLoad({'GSH_MEGA_1_act.sdat'});
Philips SDAT data (with water reference), run: MRS = GannetLoad({'GSH_MEGA_1_act.sdat'}, {'GSH_MEGA_1_ref.sdat'});
Philips .data files (no water reference), run: MRS = GannetLoad({'GSH_MEGA_1.data'});
Philips .data files (with water reference), run: MRS = GannetLoad({'GSH_MEGA_1.data'}, {'GSH_MEGA_1_water.data'});
Siemens .rda data (no water reference), run: MRS = GannetLoad({'GSH_MEGA_1_ON.rda', 'GSH_MEGA_1_OFF.rda'});
Siemens .rda data (with water reference), run: MRS = GannetLoad({'GSH_MEGA_1_ON.rda', 'GSH_MEGA_1_OFF.rda'},{'GSH_MEGA_1_water.rda'});
Siemens TWIX data (no water reference), run: MRS = GannetLoad({'GSH_MEGA_1_metab.dat'});
Siemens TWIX data (with water reference), run: MRS = GannetLoad({'GSH_MEGA_1_metab.dat'}, {'GSH_MEGA_1_water.dat'});
Then run GannetFit: MRS = GannetFit(MRS);
1. Mikkelsen M, Saleh MG, Near J, et al. Frequency and phase correction for multiplexed edited MRS of GABA and glutathione. Magn. Reson. Med. 2017;0:1–8. doi: 10.1002/mrm.27027.
Gannet 3.0 released
Introducing Gannet 3.0! This new version includes a number of new data processing features (incl. handling of HERMES data), a revamped output MATLAB structure, slight improvements in speed and efficiency and many other changes and bug fixes.
Gannet 3.0 is available for immediate download at the following link: https://github.com/richardedden/Gannet3.0/archive/master.zip
Release notes:
MAJOR CHANGES
+ Added new puffin logo
+ Changes to output structure to accommodate multi-metabolite and multi-voxel datasets
+ Removed unused/redundant lines of code throughout Gannet
+ Removed historical/unused .m files in Gannet folder
+ Optimized code where possible, e.g.:
- Removing loops and vectorizing instead
- Simplifying code, e.g., using one-line logical indexing for frequency ranges
- Pre-allocating memory by setting up variables with zeros
- Removed ‘convergence’ loop in metabolite signal fitting (should be enough to first run lsqcurvefit followed by nlinfit with high enough max iterations/low enough tolerances)
+ A number of updates to ‘PhilipsRead.m’, ‘GERead.m’ and ‘SiemensTwixRead.m’, e.g.:
- Almost all acquisition parameters now parsed from the header
- For P-files and TWIX data, coil combination/pre-phasing is performed by signal-weighting method using unsuppressed water data (when available)
- Complete revamping of ’SiemensTwixRead.m’; automatic determination of MEGA-PRESS sequence type and scanner software version; removed this parameter from ‘GannetPreInitialise.m’
- For Philips HERMES data, only the averages where editing pulses do not affect residual water are used for pre-phasing (currently geared towards GABA-/GSH-edited data)
+ Added support for DICOM MRS data files
+ Added functions to de-identify GE P-files, Philips SDAT/SPAR files and Siemens TWIX files
+ SPM segmentation now called by either ‘CallSPM8segmentation.m’ or ‘CallSPM12segmentation.m’ depending on which version of SPM is installed
GannetLoad:
+ Data are now zero-filled to obtain a nominal spectral resolution of 0.061 Hz/point by default (to handle acquisitions with number of data points/spectral width other than 2048/2000 Hz); removed this parameter from ‘GannetPreInitialise.m’
+ HSVD water filter is applied to both GABA-/GSH-edited HERMES data, and to both pre- and post-aligned DIFF spectra
+ For filtered HERMES data, baseline correction performed by demeaning (real) signal between 9 and 10 ppm
Spectral_Registration:
+ Now only first n points of FIDs used for registration, where n is the last point where SNR > 3
+ By default, median across data points of all transients is used as the reference ‘FID’
+ Frequency/phase offset estimates now saved for each processed dataset
+ Transients are now rejected if their respective standardized mean square error (from the nonlinear regression routine) exceeds +/-3 stds.
+ Added ‘Spectral_Registration_HERMES.m’ for frequency/phase correction of HERMES data
GannetFit:
+ GABA+Glx model fitting now performed by weighted nonlinear regression, where observation weights are supplied in nlinfit to down-weight any Cho subtraction artifact and (for HERMES data only) co-edited(?) signals downfield of 3.7 ppm Glx signal (thanks to Alex Craven of University of Bergen for this idea)
+ Added fitting of residual water in MEGA-PRESS data to calculate water suppression factor
+ Added fitting of NAA signal in OFF spectrum
MINOR CHANGES
+ Lots of minor bug fixes
+ Renamed module output folders to ‘Gannet*_output’ (e.g., ‘GannetLoad_output’)
+ Renamed module output PDFs to ‘<filename>_*.pdf’ (e.g., ‘S01_GABA_load.pdf*)
+ Text in output figures aligned more cleanly
+ Addressed MATLAB warnings
+ Made code easier to read/understand by indenting lines and adding a header comment here and there at beginning of processing steps
+ Optimization options for lsqcurvefit and nlinfit now set consistently throughout Gannet
+ Renamed some variables for the sake of consistency
+ Output figures now open in center of screen regardless of screen resolution (figure pixel dimensions are fixed, however)
+ When batch-processing, each output figure is cleared rather than closed all together
+ Some useful acquisition parameters (e.g., edit pulse frequencies) are parsed from the data file headers (and saved in vendor-specific fields) where available
GannetLoad:
+ Fixed ON/OFF indices for GABA-/GSH-edited HERMES data (for ‘offfirst’ only); assumes ‘C B A D’ as per NeuroImage paper
+ Color of pre-/post-alignment spectra now fixed to red/blue regardless of user’s MATLAB version
+ Post-alignment spectra always plotted second so that they are displayed in the foreground (this is helpful for HERMES data)
+ Renamed ‘SiemensRead_RE.m’ to ‘SiemensRead.m’
+ Renamed ‘Gannetplotprepostalign.m’ to ‘GannetPlotPrePostAlign.m’
GannetFit:
+ Water-scaled ON and OFF spectra now saved along with water-scaled DIFF spectrum
+ Models always plotted second so that they are displayed in the foreground
+ Renamed ‘GaussModel_area’, ‘GABAGlxModel_area’, etc. to ‘GaussModel’, GABAGlxModel’, etc.
+ Renamed ‘MRSGABAinstunits’ to ‘CalcInstUnits’
+ Improved quantification of lactate
ISMRM Study Group Prizes
Congratulations to Kim Chan and Nick Puts for winning ISMRM Study Group prizes for their respective abstracts on HERMES and Tourette Syndrome.
3X acceleration with HERMES
Excited that our paper on 3X HERMES acceleration was accepted at NeuroImage. Kim will be presenting it Thursday afternoon at ISMRM. See you there!
The paper is now online (pre-proof).
Editing Glutathione
Over the last year, we have been expanding our horizons to editing things that aren't GABA. Probably the next most popular editing target is glutathione (GSH), so I thought I'd post some thoughts about how to take a GABA scan and modify it to edit GSH.
Editing Frequencies
To address the most obvious change first, the editing-on frequency changes from 1.9 ppm for GABA to 4.56 ppm for GSH (based on shifts from Govindaraju et al.). The editing-off frequency is less critical, but putting it at 8 ppm seems safe.
Echo time
The least obvious issue to resolve is echo time, which we recently wrote a paper about (Chan et al. 2017, the subject of a recent MRM piece). To cut a long story short, it doesn't make a lot of difference whether you stick with medium-TE (~70 ms) or go longer (~120 ms), although the longer TE is slightly better (and certainly expected to be better). Longer TE also has secondary advantages, like accommodating longer more selective editing pulses or longer, higher-bandwidth refocusing pulses. But beware, some editing implementations e.g. the Siemens WIP, don't move the editing pulses when you increase TE, so it is impossible to edit well at longer TEs.
HERMES
Another important consideration, if you are planning a GSH editing study, is that it is now possible to edit GABA and GSH in the same scan without substantially impacting SNR (Saleh et al. 2016). So if you're planning to edit GSH, why not edit GABA too?
Alignment
Gannet performs post-processing frequency-and-phase correction of data to minimize subtraction artefacts due to motion and scanner drift. For simple GSH editing, the 'NAA' alignment works pretty well. ('SpecReg' fails due to editing pulses impacting water). For HERMES, we have a new Gannet version in progress.
Hackathon Live Update Stream
Non-Live webcam feed!
455PM: A quick summary: We haven't come up with a method that makes things better. BUT, we have learned several important things:
1. NAA-based correction can do a lot to clean up the GSH-diff spectrum.
2. Metrics matter. By taken a mean 'quality score', we shot ourselves in the foot. We will reassess all data based on median, and maybe normalized by the group average SD subtraction artifacts, not within-subject...
255PM: Bumped out of the large conference room.
1239PM: In terms of actual progress. We have tested 6 methods. None of them do very well. Onwards and upwards!
1200PM: Pizza arrives as ably planned by Mark (picking up from the organizational travesty that was Nick's lunch arrangement).
933AM: Donutgate: the number of declared donuts doesn't match the number of eats donuts.
930AM: Donut scores: Georg 2 Nick 2 Richard 2 Ashley 1 Muhammad 0 Tao 1 Kim 1 Mark 1
922AM: Nick just left the room. Minus 70 Edden points.
920: Pretend to set up a live webcam feed.
905AM: Preliminary chat over. Repositories forked and coding has begun.
840AM: Skype to Jamie Near in Canada.
755AM: Room setup. Do we have enough extension leads?
730AM: Dunkin' Donuts stop for fuel.
559AM: I remembered to grab my charged-overnight laptop on the way out of the house. A good start.