Tuesday, March 13, 2012
I wrote a post in May last year to highlight the enhanced motion sensitivity of GRAPPA-EPI compared to single-shot EPI for fMRI. Paul Mullins and I had also discussed the use of GRAPPA for resting-state fMRI in the Comments of an earlier post. The literature is still fairly quiet on the adverse effects of GRAPPA for fMRI although, as I noted in the May post, there are one or two reports of reduced fMRI sensitivity when using parallel imaging, some of which might be attributable to motion (whether it was diagnosed as motion or not in the published work).
In the May, 2011 post I explained the two types of motion sensitivity that plague GRAPPA in its usual incarnation for EPI time series acquisitions. The first type - motion contamination of the auto-calibration scans (ACS) - might be mitigated by vigilance and a suitably resilient task script, e.g. one that uses plenty of null events at the start of the acquisition, before the first real stimulus is presented, to give the operator sufficient time to evaluate the images being generated with the current ACS and decide whether or not to stop and start over. This approach is no guarantee that motion won't have contaminated the ACS, but simple tactics like this can help avoid the worst effects of motion during the start of the run.
The second type of motion is that which happens after the ACS and during the (under-sampled) time series itself. This problem is one of mismatch. Displacement of the head from its position during the ACS acquisition can lead to spatial errors in the current image volume. Thus, whilst attaining motion-free ACS might be considered essential for fMRI, maintaining proper matching of the ACS to the under-sampled time series is also important. The bigger the mismatch the more likely there will be a penalty in statistical power for the time series.
In this post I want to tackle the issue of non-head motion in the scanner, and its effects on GRAPPA-EPI images. This investigation was motivated by one of my users who reported seeing occasional "banding" in a study that had used GRAPPA-EPI. The traditional evaluation of head motion suggested that the subjects weren't moving very much, so I started looking into other possible instabilities. I was quite surprised just how sensitive GRAPPA-EPI can be to small perturbations, as you will shortly see.
A quick review of some brain data
Let's begin by looking at one of the problem GRAPPA-EPI data sets from a human subject. The acquisition specifics are as follows: 12-channel head RF coil on a Siemens Trio/TIM scanner, GRAPPA with R = 2, reconstructed matrix = 96x96, FOV = 224x224 mm, slice thickness = 3 mm, 10% gap, interleaved sagittal slices, flip angle = 90 deg, TR/TE=2000/26 ms, echo spacing = 0.8 ms, readout bandwidth = 1408 Hz/pixel.
Here is a cine-loop through the raw data: