Archive for the ‘Data Processing’ Category

Recent advances in in-vivo spectroscopy methods and applications at CNI

July 6th, 2023

Recent advances in In-vivo spectroscopy methods at the CNI


A growing number of people at the CNI are interested in measuring metabolic changes in the brain using in-vivo spectroscopy (MRS) techniques.  Often, they combine this information with functional MRI measurements. The special interest spectroscopy group at CNI  develops and supports new MRS data acquisition and data analysis capabilities, following the recommendations of the at-large MRS community. This message describes the initiatives for education, support of experimental design, guided analyses, and interpretation of MRS data.

Recent spectroscopy results were shared in a poster that we presented at the 64th ENC, the premier conference for nuclear magnetic research (April 16 – 20, 2023). Examples of studies at the CNI using in-vivo spectroscopy techniques include characterization of biomarkers following transcranial magnetic stimulation, and metabolite characterization for conditions such as addiction, pain, depression, and various forms of dementia. Published, collaborative studies performed at CNI include

  • method applications (DeSouza DD, Stimpson K, Baltusis L, Sacchet MD, Gu M, Hurd R, Wu H, Yeomans DC, Williams N, Spiegel D. Association between anterior cingulate neurochemical concentration and individual differences in hypnotizability. Cerebral Cortex, Volume 30, Issue 6, June 2020, Pages 3644–3654),
  • new method developments (Gu M, Hurd R, Noeske R, Baltusis L, Hancock R, Sacchet MD, Gotlib IH, Chin FT, Spielman DM (2018) GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence, Magnetic Resonance in Medicine 79:41-47),
  • participation in multi-site spectroscopy studies (Hui et al. Frequency drift in MR spectroscopy at 3T, NeuroImage 241(2021) 118430), and
  • a recent paper regarding voxel placement method development (James H. Bishop, Andrew Geoly, Naushaba Khan, Claudia Tischler, Ruben Krueger, Poorvi Keshava, Heer Amin, Laima Baltusis, Hua Wu, David Spiegel, Nolan Williams, Matthew D. Sacchet Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy, Journal of Neuroscience Methods 392 (2023) 109853.

Technical notes

Here is a description of the specific methods we now support.  If you are interested in learning more about these methods, or using them, please consult with Laima Baltusis.

  1. Experimental spectroscopy data acquisition methods continue to evolve. Currently the semi-LASER sequence is the ISMRM (International Society for Magnetic Resonance in Medicine) consensus method, particularly for multi-site 3T MRS studies. We have, therefore, transitioned studies at CNI to the semi-LASER sequence (a GE WIP (Works-in-Progress)) for both single voxel and to MRSI studies. The ENC poster presents both single voxel and new focal 2D MRSI results using the semi-LASER sequence in challenging and therefore less well studied but important areas of the human brain such as the basal ganglia regions. This region is often studied by investigators trying to understand movement, cognition, and emotion, and the single voxel data in this region has been of lower quality. To improve the data quality, and measure small metabolite differences in subregions of interest, we optimized a focal 2D MRSI data acquisition method (presented in the poster). The improvements for focal 2D MRSI and single-voxel MRS includes B0 shimming, encoding and acceleration methods, spatial selection, water suppression, extracranial lipid suppression, and RF (Radio Frequency) performance.
  2. In addition, current basic data reconstruction and visualization can employ SAGE, a GE proprietary analysis and visualization tool with LCModel used as a separate module for data fitting. The next phase of this particular research project will continue to explore additional optimizations of data collection but will focus more on data processing for optimal quantification of metabolites. Areas of study will include improvements in data analysis using LCModel by (1) mitigation of baseline and macromolecular contributions for data analysis with LCModel and (2) improvement of the accuracy of the LCModel basis set by the addition and validation of experimental lower concentration metabolites (glutathione and ascorbate, as examples) to an otherwise synthetic LCModel basis set. The use of Bayesian analysis will also be explored as a technique to remove the broad baseline and macromolecule content without bias. We anticipate presenting these findings at the 2024 ENC conference.


Update on Physiological Data Association in Flywheel

October 23rd, 2020

Dear CNI Users,

In August we wrote to you regarding a problem we found within the CNI Flywheel instance ( In some cases the physiological data (respiratory and cardiac waveforms) from the scanner were not matched to the correct acquisition in Flywheel, or were not uploaded at all. We have determined that such failures occurred most often when a scan ended prior to the prescribed acquisition duration, The matching logic within the reaper used the duration information.  In some cases the mis-match led the reaper to associate the wrong physiological data with an acquisition; in other cases no match was found and thus no physiological data were uploaded.

We maintain backups of the physiological data, and this is enabling us to correct the association of the physiological data with its proper acquisitions. The restoration has been ongoing in the background for some time, and we expect to complete the process within the next couple of weeks.

Incorrectly associated physiological data in an acquisition will remain within the acquisition and be renamed using the following convention:


Projects in which we found one or more acquisitions containing incorrect or missing physiological data will have a CSV file attached to the project. The files will be named using the following convention:


Those ‘fix’ files will contain detailed information describing each acquisition that had physiological data associated with it. The contents of the file are described at the end of this message.

Please note that physiological data is only automatically associated for EPI-type sequences. If you store physiological data for other sequence (e.g. T2 spin-echo, T1-weighted fast gradient-echo, etc.) then you will need to manually upload the physiological data to Flywheel if you’d like it kept with your acquisition.  Please contact us directly if you have questions about this process.

We appreciate your patience as we work through this issue. If you have any questions regarding this process, or concerns about the data that have been restored to your sessions, please reach out and let us know either via email or Slack.

Contents of the ‘fix’ file

The ‘fix’ file is a text description (CSV) about the corrected physiological data.  It includes:

  • Session ID – A string representing the Flywheel Session ID
  • Acquisition ID – A string representing the Flywheel Acquisition ID
  • Flywheel Path - A human readable string showing the path to the acquisition in Flywheel
  • FIX Status – A string indicating whether the new data were ‘found’ or ‘replaced’
  • Message – describing the action performed, or an errors during execution.
  • Exam – Exam Number
  • Series – Series Number
  • TR – A number defining the TR of the acquired data in milliseconds
  • NTP – An integer defining the  number of temporal samples in the physiological data file
  • RAW NTP Number of temporal positions (from the DICOM or PFile header),
  • Scan Duration – Calculated scan duration
  • PPG_Samples - the number of Physio Samples
  • PPG_Lines_Expected – the number of lines we expected in the PPG file
  • PPG_Lines – the number in samples found in the PPG file found in Flywheel (if applicable)
  • PPG_Lines_Fix – Number of sample found in the Matched/Fixed data,
  • DELTA_FIX_EX – the difference between the fixed data and what was expected
  • DELTA_FW_EX – the difference between what was in Flywheel and what we expected
  • Date and Time – of the acquisition
  • FIX Time Delta Difference between expected timestamp and FIX file timestamp in Sec
  • FW Time Delta Difference between expected timestamp and FW file timestamp in Sec
  • FW File Base Base file name of data found in Flywheel
  • Regx – The regular expression used to find the matched data

Thanks and stay safe!

The CNI Team

Laima measures GABA

August 31st, 2017

You may have been wondering about Laima’s experimental work over the last few years.  You can now read about it in a paper co-authored by folks who work at the CNI, GE, and Lucas.  Nice collaboration!

GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence
Meng Gu, Ralph Hurd, Ralph Noeske, Laima Baltusis, Roeland Hancock, Matthew D. Sacchet, Ian H. Gotlib, Frederick T. Chin, Daniel M. Spielman (

The CNI Team

Upgrade Plan for DV26

August 28th, 2017

Executive summary

We will be upgrading the GE software and computational infrastructure in late September (from DV25 to DV26). The reasons and implications for your projects are explained below in detail.

To migrate the CNI sequences to this new environment, the CNI development team needs time on the scanner (we estimate about 12 hours).  As most of you know, the schedule is very full. Thus, starting September 1st and continuing for a few weeks, the CNI will have priority for any released time and all protocol development time.

If you have already booked protocol development time, we may contact you to negotiate an alternative slot. We will return to a more open policy after the transition is completed. Thank you for your cooperation.


Like many GE sites, we are now planning for a significant upgrade.  This upgrade, DV26, includes new computational hardware and software (but no new MR gear, such as coils or gradients).  This upgrade includes features that will eventually be valuable for many of you.

At minimum before we make the upgrade, we will be making sure the existing CNI-modified sequences (cni_epi, cni_epi2, muxarcepi, muxarcepi2, sprt, cni_3dgrass, cni_efgre3d,  cni_ir_epi and Probe-MEGA) will all be working at DV26 as they do at DV25, together with offline reconstruction for the mux and spiral sequences using NIMS.  Other existing product sequences are not noticeably changed in this upgrade. As a result, the transition for users should in most cases be seamless.

A beneficial feature of the new system is that GE has incorporated the SMS methods that were implemented by Adam, Kangrong, Bob, Hua  and Matt Middione (GE) at the CNI.  GE refers to their implementation as Hyperband (love the marketing folks; multiband was not enough).  The DV26 product includes only a Hyperband DTI sequence, but GE has agreed to enable us to use a beta version of the Hyperband fMRI sequence.

The user-interface for Hyperband will operate as any normal sequence. Simply prescribe the whole volume you wish to acquire and the online reconstruction will perform the slice and inplane acceleration unaliasing so that undistorted images appear in the mini-viewer and scanner image database. The new computational hardware from GE will perform these reconstructions, eventually reducing the burden on our aging CNI computers.

However, there are some limitations of the new product Hyperband sequences.  GE has not yet implemented our preferred reconstruction algorithm for Hyperband acquisitions (split-slice GRAPPA). Also, there are some support features in the CNI versions of these sequences (e.g. triggering selection) that are not in the product Hyperband sequences.  The CNI team will make modifications to these Hyperband sequences to support the specific CNI features as well as augment the product reconstruction.  Some of these updates, in particular supporting online split-slice-GRAPPA reconstruction, will not be completed until after our migration to DV26.

As a result of these limitations to the Hyperband sequences, and until we have the opportunity to confirm these sequences have the same performance of our existing SMS sequences, we advise users to continue to use the CNI SMS sequences (muxarcepi, muxarcepi2). However we expect that we will be able to recommend users migrate to these Hyperband sequences sometime later this year.  We will keep you posted on our progress.

The CNI Team

Optimizing image quality: Fieldmaps and shimming

April 9th, 2017

Acquiring a high quality fieldmap of the mean magnetic field is crucial to obtaining a good measurement.  Making sure you set the parameters so that this field map is obtained as part of your protocol is something we help you with.  There are cases, including new sequences, where this can require some extra attention.

The problem: The CNI team recently helped diagnose a confusing fieldmap result.  The user was acquiring a fieldmap using a spiral acquisition, but without first performing a high-order shim (HOS) acquisition or setting Autoshim (linear-order shim).  They subsequently acquired EPI data with Autoshim.  This resulted in acceptable imaging quality but also created some problems. The disadvantage was that high-order shim corrections were not used, and the spiral fieldmap data did not incorporate the linear-order shim corrections that were imposed by the Autoshim.  As a result, the data quality were not optimized.

The solution: We recommend that a HOS be acquired as part of your protocol, especially when EPI sequences are used for subsequent data acquisition.  We describe these procedures on our Wiki entries, as well as some troubleshooting steps in a blog entry:

If this overhead is too time-consuming, then it is important to ensure an Autoshim is acquired, which performs a linear-order shim correction. Please note: when Autoshim acquisition is set to “Auto”, and an HOS is acquired, the system should use the HOS acquisition rather than acquire a new linear correction.

Some details: A variation in the B0 main field from the desired B0 leads to an image shift in the phase encode direction of EPI acquired images.  When the B0 field is spatially varying, this leads to varying image shifts which can result in signal pileup, voids, and image warping. There are two methods described in our Wiki for correcting these distortions.  First, you can repeat your EPI data acquisition twice, with a reversal of the phase-encoding direction between data sets as we describe in a Wiki entry: CNI Data Processing

Secondly, you can acquire an explicit fieldmap using the spiral fieldmap sequence and then process the subsequently acquired EPI data as described in another Wiki entry: Fieldmaps

Please note that if the explicit fieldmap correction approach is adopted, it is important to ensure that the subsequent EPI acquisition does not modify the shim corrections.  In this case setting Autoshim to “Off” in your protocol would be advisable.

Adam and Hua

SMS, EPI and tSNR (long)

March 15th, 2016

Bob, Hua and the CNI team are working with the community to improve our methods.  The text below - excerpted from a recent exchange with the Wagner lab – illustrates the tools we use to exchange data and computational methods.  This software infrastructure is an important part of the CNI research and development mission.

The story below illustrates how one group uncovered a problem and effectively communicate it to the CNI team.  They collaborated to develop a solution that improves on previous methods, and this will benefit the entire community.

In a subsequent post, we will describe our plan of action when we discover that a method can be improved. Such advances happen (routinely we hope!).  Our general plan is to share advances by taking advantage of the database (NIMS) so that we can identify data that could be improved and alert you to apply the improvements.

Bob, Brian and Michael


An SMS Story

Bob, Kangrong, Matt, Hua, and Adam have been developing simultaneous multislice methods (SMS) for use at the CNI. Variants of this sequence for rapid, whole-brain acquisitions have been developed and distributed on Siemens, but as far as we know, the CNI implementation is the first fully working system on the GE platform.  The SMS technology allows investigators to collect whole brain fMRI measurements with a repetition time of substantially less than 1 sec even with a resolution as low as 2mm isotropic. For diffusion imaging, SMS allows the scan time to be substantially reduced, allowing more diffusion directions and/or b-value shells to be measured in a reasonable scan time.

Our user community has been very actively involved in testing the CNI implementation.  As with the development of any new technology, feedback from the field is very important.  Since the initial roll out of the method, Karen LaRocque and the team in the Wagner lab have provided particularly useful feedback.

In February, Karen sent a message to Bob noting that

I’m analyzing some multiband (3 band) data and am noticing that the variance in the timeseries seems to vary systematically between the odd-even slices. It’s more apparent for some runs than for others, but is present in multiple runs / subjects.

Karen and her colleagues also shared links to data sets and Jupyter notebooks showing her calculations.

Bob responded with some ideas (the CNI team often uses “mux” as a synonym for SMS)

Hi Karen,

One hypothesis that I have is that this is related to the calibration scan. If the subject moves (even a little along z) during the calibration scan, it can create some inhomogeneity in the signal intensity in the calibration images and that will carry over into the mux scans.

Things to check: 

1. look at the quality of the calibration scan (should be the second volume in the nifti) and see if it correlates with the appearance of the artifact

2. is the tSNR variance caused by changes in the signal or the noise? Or both?

If it is a calibration scan issue, then we can try reconstructing some of of the bad scans using calibration data from a different scan. I will also talk to the MR physics group to see if there are any ideas for a fix in the reconstruction (e.g., smoothing the calibration images). Longer-term, we do hope to develop better calibration scan methods. But that will be some time away.

Also, can you tell me how serious you think this artifact is? Are you thinking that it makes data unusable in some cases? 



Hua pitched in, working with Bob to understand the problem.  Karen pointed Hua to their most problematic data set, which could be referenced by a link to the NIMS data base

From: Karen Fossum LaRocque <>
Sent: Wednesday, February 17, 2016 2:10 PM
To: Hua Wu
Cc: Anthony David Wagner; Michael Lawrence Waskom; Valerie Ann Carr; Ian Connors Ballard; Bob Dougherty
Subject: Re: multiband data: odd-even slice variance differences

Hi Hua,

This is my most problematic scan:,exp=95441,sess=96169,epoch=96210


After some analyses and experiments, the CNI team found an improved method.

From: Bob Dougherty <>

Sent: Monday, February 22, 2016 5:30 PM
To: Karen Fossum LaRocque
Cc: Anthony David Wagner; Michael Lawrence Waskom; Valerie Ann Carr; Ian Connors Ballard; Brian A Wandell; Hua Wu
Subject: Re: multiband data: odd-even slice variance differences

Hi Karen,

We’re trying out an alternative recon algorithm (split-slice GRAPPA) and some z-axis smoothing on the calibration images. The ssGRAPPA seems to make the tSNR more uniform across slices, and the smoothing may increase tSNR a bit as well. Here’s the comparison for subject you pointed us to (exam 7840, series 26):


I’ve put both files up on your cni lx-container (cnic9) in /data/7840_26_1_weightedavg.nii.gz and /data/7840_26_1_spslgrappa.nii.gz. You should be able to scp the files with ‘scp klarocqu@cnic9:/data/7840_26_1_* .’ (for more info on the cni containers, see Note that the image orientation is messed up in these niftis because we’re using our bleeding-edge recon code, which isn’t fully debugged yet. But we hope to get that fixed this week. After more testing, we could be ready to start redoing recons in NIMS sometime next week.