Hi – Our imaging center has a 3T Prisma with a 20-channel and 64-channel head coil. With both coils, there’s a gradient in tSNR/SFNR, with maximal tSNR/SFNR near the coils (i.e., the cortical surface). I realize this is the expected behavior of these coils, but I was wondering if anyone had advice for optimizing subcortical signal, especially in the striatum? Unlike the HCP, we would generally be running event-related designs.
We’ve done some initial tests comparing different resolutions (e.g., 2mm vs. 3mm) and different TRs (e.g., 2s vs. 1s). We’ve also done some test with multiband on (sms = 3) or off. These initial tests suggest multiband, faster TRs, and smaller voxels reduce tSNR/SFNR, but I don’t think tSNR/SFNR metrics are really the whole story here, so I’d be happy to look at any additional metrics.
Thanks! This is very helpful to see. There’s still some chatter on the twitter link, so I wanted to return to this post and share what we had at this stage. Here’s a link to our OSF page (in progress): https://osf.io/8enxp/
One thing that I learned while looking into this issue is that it looks like many people recommend using the pre-scan normalization option with this phased-array head coils.
This does seem to do a good job with making the images look better (more homogenous), but we had some masking issues with FMRIPREP with those runs, which can be seen for some of the task-rest data for sub-103 and sub-104. The FMRIPREP folks are using those data to make their approach more robust. (Thanks!)