Spatial normalization issues using 7t MRI Scanner

Hi, I am trying to adopt new 7t machine for fMRI analysis, and having trouble with spatial normalization using fmriprep.


As you can see from attached data, occipital area and cerebellum region are visibly distorted.

This issue does not show when we use 3t anatomical image for analysis, so it seems like a issue might be the 7t anatomical image scans…

How can I fix the problem with the data? Or is there anything I need to check or fix when we scanning the participants to solve this issue? Any answers would be really helpful. Thanks!

Hi @leeeut000, I assume that the image looks reasonable prior to using with fMRIPrep?

How do the other steps perform? Often bad normalization results from a bad brain mask. We may also need to update the registration parameters.

Bringing fMRIPrep up to date to work with 7T images might be a non-trivial effort. Would you be interested in being involved? We’d be happy to have your contributions and help you getting started with development.

We have also had a similar issue with spatial normalization of our 7T data.

Our brain masks look great (see fig 1A).

But the spatial normalization looks very bad, especially in the temporal lobes, for most subjects. See Fig 1B.

We have noticed that the intensity of our anatomical images really drops off in the temporal lobes, exactly where most of the distortions are happening. This is due to field inhomogeneities present at 7T. Is it possible this is related to an intensity normalization step during recon-all?

Thank you,


May be this can help?

or here: HiResRecon - Free Surfer Wiki

For best results I advise to do an inhomogeneity correction by division before processing the data. See T1 weighted Brain Images at 7 Tesla Unbiased for Proton Density, T2* contrast and RF Coil Receive B1 Sensitivity with Simultaneous Vessel Visualization. The “corrected by division” image can be generated in SPM by first to aligning the MP-RAGE and the GE by co-registration. Afterwards use the image calculator to do the division using the formula: (i1./i2 .* (i2>20)) .* 100. The value of 20 depends on the image intensity and 100 is simply a scaling facotr (i1= MP-RAGE, i2= GE)