Hyperband Transition

September 14th, 2020
September 14th, 2020

Executive Summary

The GE product Hyperband BOLD and diffusion sequences are a result of close collaboration between GE scientists and engineers with the CNI staff.  The CNI SMS implementation, which we called the “MUX” research sequence, served as a resource and a benchmark for the GE Hyperband product.  Over the last several months Adam and Hua worked with our partners at GE to resolve a limitation of the initial Hyperband implementation. We are glad to report that the two sequences now have the same SNR (signal-to-noise ratio).  We describe the testing that convinced us and the benefits below.  With this improvement, we plan to move CNI users to the Hyperband sequence, which will offer many benefits. Specifically, we ask all users of the UHP system to migrate any protocols using the research MUX sequence to the product Hyperband sequence.  We do not plan to support the research MUX sequence in future releases of the GE platform.  Instead, we will coordinate with GE on improvements to their Hyperband product.


The main benefit for researchers moving to the Hyperband product sequences is that GE’s product releases are fully integrated with other system improvements. The CNI cannot achieve the same level of integration when we port the MUX research sequences.  For example, GE’s product Hyperband sequences are fully integrated with the reconstruction engine.  For this reason unaliased images show up in the mini-viewer and are stored in the image database which makes it possible for operators to immediately detect image quality issues.   Second, CNI’s resources for testing and migrating research sequences each GE system update is limited.  GE’s product sequences go through rigorous in-house acceptance testing with each new release.  Third, by moving to the product users will also benefit from all other system developments for the baseline EPI product (higher-order eddy current correction, bug fixes, etc.).  Fourth, the product sequence includes seamless integration with the User Interface so users simply prescribe whole-volume slices as normally.  Fifth, the product delivers near real-time reconstruction, rather than the slower reconstruction used by the research MUX sequence.

Image quality

We did not recommend using Hyperband until now because testing showed that the GE product SNR was 30% lower than the MUX performance.  The reasons for this SNR drop have been determined by CNI staff, and these corrections are now incorporated into GE’s future product releases.  We also incorporated these fixes into the CNI versions of the base sequences on our system, namely cni_epi and cni_epi2 which correspond to the Hyperband BOLD and diffusion sequences.  These improvements are why we now recommend shifting to the Hyperband sequence.  The shift will enable users to take advantage of integration with the other features of the GE platform.

Our assessment is based on several sets of SMS BOLD and diffusion data across multiple GE systems, including phantom and human subject data.  We acquired on the previous CNI MR750 scanner and the new CNI UHP scanner, as well as on the product sequences compared to our research sequences.  These data are all available on the CNI Flywheel site in the scanner_comparison project which is accessible by all CNI flywheel users.  A brief summary is provided here.

The figure below shows the detrended SNR determined across 50 timepoints for a region at the center of a phantom image acquired on our UHP scanner using the Nova 32-channel coil with both research and product SMS sequences.   The Hyperband sequence shows slightly improved for all non-unity SMS acceleration factors except for the SMS factor 8.  We hypothesize that these variations may be due to the different approach used between CNI and GE SMS sequences in acquiring calibration data to determine how to unalias the data.  CNI sequences generally acquire calibration data at the beginning of the BOLD acquisitions while GE uses a fast separate calibration sequence to acquire this data.  We will continue to investigate the source of this SNR drop at SMS 8, but given the performance at the normal acceleration factors that are used we are not concerned.  The SNR is also still quite high from an absolute perspective, and it is likely physiological noise would be of more concern in an in vivo experiment.


The following figure shows results acquired using the MUX research sequence across multiple platforms at Stanford.  The data were acquired using different Nova 32 channel coils that were available at each site.  As a result only data acquired at CNI used the same receive array, and this shows a substantial increase in detrended SNR when moving from the CNI MR750 system to the CNI UHP.  The Lucas 3T Premier system has higher SNR, and we hypothesize this is due to the receive array.  We will be following up with a future experiment to using the CNI coil on the Lucas Premier system.These SNR values are all quite high however, and likely data will be dominated by physiological noise for in vivo data.  Even so, we are considering recalibrating or purchasing a new coil.


We also evaluated the Hyperband diffusion sequence in terms of baseline SNR and artifact.  The following figure shows compares the research and product SMS sequences when using a whole-volume acquisition of an agar phantom with an SMS factor of 3 and no inplane acceleration.  A nominal b-value of 2800 was prescribed but the diffusion tensor file hand-edited to acquire 50 b0 images that were used to calculate the SNR.  We measured using a peak gradient amplitude of 50mT/m for the diffusion encoding lobes and for the product Hyperband sequence we also measured with a peak gradient amplitude limit that takes advantage of the UHP capabilities, at 100 mT/m.  In this last instance the echo time (TE) for the acquisition is significantly reduced due to the shorter diffusion encoding lobe durations that are required. As shown below, the Axial, Sagittal and Coronal reformats of the data all show good unaliasing and no noticeable banding artifacts.  The SNR for instances when the gradient amplitudes are limited to 50 mT/m are comparable across the MR750 and UHP regardless of whether our research or product SMS sequence is used.   The last measurement with the peak gradient at the UHP limit of 100 mT/m shows an appreciable SNR gain due to the shorter TE.

CNI staff are engaged with GE scientists to investigate different reconstruction approaches that will improve the product Hyperband sequence and reconstruction.  The current product uses a reconstruction method analogous to 1D-GRAPPA;  we are investigating a method analogous to split-slice-GRAPPA.  This latter reconstruction method shows no benefits as of yet, and requires considerably more computation and is not available for real-time reconstruction on the scanner.  Given the performance of the existing product reconstruction method we are confident in recommending its adoption.  At the same time, we will continue to work with GE to investigate possible improvements.

The scanner_comparison project on Flywheel has more data analysis than we’ve reproduced here, and raw data is also available should users be interested. While there is undoubtedly more comparisons that could be performed, there is evidence that the performance matches our research sequence performance, and there are other benefits offered to researchers by moving to the product Hyperband sequences which are significant.  These data and the general considerations are why we now recommend researchers switch to Hyperband.

Transition Assistance

CNI staff will be available to help with transitioning protocols to using the Hyperband product. Given the full integration of these sequences with the user interface and reconstruction we’re sure that users will find it a very easy transition to make.  If warranted we’ll add a channel to our Slack workspace for Hyperband to help with common questions (and yes, if you’re not on the CNI Slack workspace, please sign up now — see https://cni.stanford.edu/slack-and-volunteers for details).

Posted in: Data Acquisition, SMS


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