Archive for the ‘SMS’ Category

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

Updates for multiband reconstruction

March 15th, 2017

The CNI has recently introduced a new option for reconstructing SMS (aka, multiband or mux) scans. The default reconstruction method in the SMS reconstruction pipeline is currently 1D-GRAPPA (Blaimer M. et al. MRM 2013). Based on recent research and testing, we believe that the split-slice-GRAPPA (Cauley SF, et al. MRM 2014) reconstruction algorithm does a better job at unaliasing the simultaneously acquired slices, especially in cases where the calibration data are corrupted by subject motion. This more robust unaliasing will help reduce the chance of false correlations in fMRI scans by reducing signal leakage across aliased slices.

Another advantage of the split-slice GRAPPA method is that it is less dependent on the image contrast being consistent between the calibration data and the SMS data, therefore it allows more flexibility in choosing the best calibration scans. So, we have also introduced a new SMS calibration scan option – using a separate single-band scan as an external calibration for the target SMS scan. Most of you doing SMS are using internal calibration, i.e. the first few volumes integrated in the SMS scans are used as the calibration data. And a few of you are doing an external calibration that has the same SMS (mux) factor as the target scan. Compared to these calibration methods, the single-band external calibration has higher SNR in the calibration data and is less sensitive to subject motion during the calibration. Therefore we believe it is a more robust calibration method, and in combination with the split-slice-GRAPPA reconstruction method, is likely to produce better image quality for the SMS scans, especially when you have wiggly subjects.

Here is a compelling example of how the single-band calibration scan can reduce a particularly insidious artifact due to excessive eye motion during the calibration scans (this subject was instructed to intentionally move their eyes during the calibration scans). In these standard deviation maps of the BOLD timeseries, the aliased eye artifact is clearly visible in the 1D GRAPPA reconstruction (top). The split-slice GRAPPA reconstruction (middle) shows a reduced eye artifact in the white matter, but there is still significant aliasing of the eyes. This aliasing is substantially reduced or eliminated in the same data reconstructed using a single-band calibration scan with split-slice GRAPPA (bottom), even though that also had subject eye movement.


However, in cases where there is no excessive motion during the calibration, all the methods are quite comparable:


The CNI SMS data processing pipeline will by default keep using the original 1D-GRAPPA reconstruction method for continuity of ongoing studies. And if you have compliant subjects who remain still, ideally with eyes-closed during calibration, this method should be fine. However, if you think your subjects may move during calibration, then we recommend switching to split-slice GRAPPA for image reconstruction. And you may also consider adding a single-band calibration scan to your protocol. Any SMS scan that doesn’t have a separate calibration scan setup in the same scan session will also be reconstructed using the internal calibrations. In order to use the new methods, you need to do the following in your protocol:

To use the split-slice-GRAPPA reconstruction method, include the keyword “_ssg” at the end of each series description that you want to be reconstructed with split-slice-GRAPPA. Note that this also enables SENSE1 coil combination (Sotiropoulos et. al., MRM 2014).

To use the single-band calibration, you need to set up a separate scan with SMS (mux) factor = 1 (CV 22). Include the keyword “_sbref” in the series description. This single-band scan needs to have the same coverage as the multiband scan, i.e. the same FOV and matrix size but X times the prescribed number of slices, X being the SMS (mux) factor. You will need to increase TR (by a few seconds) in order to accommodate all the slices in one TR. You only need 4 or 5 phases for this scan. Because the TR will be longer, you can set the flip angle to 90 to optimize SNR.

For scans using the single-band calibration, the calibration volume is not included in the reconstructed images, i.e. the NIFTI files will only contain the SMS volumes. This information is also shown in the JSON file that now accompanies all SMS NIFTI files in NIMS. The JSON file contains a list of important parameters related to the scan and the reconstruction.

Please contact Hua or Bob if you have any questions and/or want help setting up these new SMS features in your protocol. Also, we owe thanks to the Wagner and Poldrack labs for help in testing these new methods.