Archive for the ‘NIMS’ Category

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.

s1_grappa
s1_ssg
s1_sbref

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

s2_grappa
s2_ssg
s2_sbref

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.

Data management and storage at the CNI

May 31st, 2016

In planning the CNI environment, we made a risky decision: We committed to providing the community with data management services. Many of you know that most MRI centers do no more than hand the user a DVD at the end of the session, and wish them well. Or perhaps they allow the user to copy the data from the center to their lab over the Internet.  CNI users are supported much more extensively.

Data acquired on our scanner are immediately transferred from the GE system to the Neurobiological Image Management System (NIMS), a database. As they are placed in the database, the MRI data are converted into the formats (such as NIfTI) that most of our community uses.  These are the data that you typically download from a browser.

The full set of MRI data are kept online, backed-up, and available forever. The data are stored in an organized format that your collaborators can appreciate and understand. You can search through the entire database to discover what is there. You can perform simple visualizations and check image quality of your own data. The NIMS software you are using was designed and implemented by Bob Dougherty, Gunnar Schaefer and Reno Bowen.

In its 5th year of operation, the CNI has accumulated a great deal of data.  We are storing the work of about 700 people (trained by Laima). The data comprise more than 11,000 sessions and more than 6,000 subjects. There are more than 45,000 fMRI scans, nearly 10,000 anatomical scans, 5000 diffusion scans and 500 spectroscopy scans. You can search through the system and request access to scans carried out by other labs. Nobody is forced to share their data; but if you would like to share with another scientist, and your IRB permits it, then you can do so with a few clicks.

Over the years there have been a series of updates to the system to accommodate the growing data set.  There have been several hardware upgrades over the last few years, including both increased storage and increased computational power. The CNI data management system now includes a 200 Terabyte main file server with 200TB of off-site backup storage, three compute servers with a total of 80 cores, 1.9 TB of RAM, and 14 TB of fast SSD scratch storage, and a powerful web server, all interconnected by a 10 Gigabit network. The NIMS hardware and software are being maintained by Michael Perry and Bob.

Over the last three years a number of us (Gunnar Schaefer, Michael Perry, Bob Dougherty, Renzo Frigato) have been supported by the Simons Foundation to design the next generation of this software.  This work has also been supported by and integrated with the work being done by Russ Poldrack and Chris Filo, supported by the Arnold Foundation.  The next generation of software has many new features, and we will start to tell the story of the next generation of data management software during the coming months.