Geometric distortion correction using T2* in fMRIPrep (single-echo)

Hello,

I am considering preprocessing a dataset using fMRIPrep, and I would like to leverage both T2 and T2* images to improve geometrical distortion correction of the BOLD images, following the approach described in 10.1111/j.1552-6569.2010.00470.x, as no fieldmap is available for this dataset.

Specifically, my idea is to register the EPI to the T2*, then the T2* to the T2, and finally the T2 to the T1w. As described in the paper, this would allow for an approximate distortion correction by warping the mean EPI image onto the non-EPI T2* image, with the resulting transformation then applied to all BOLD volumes.

From what I understand, this type of custom distortion correction step is not directly supported in fMRIPrep with single-echo data. Do you have any recommendations for how such a procedure could be integrated into or used in combination with fMRIPrep? Would another pipeline such as C-PAC be better suited for implementing this kind of strategy?

Thank you very much for your time and any guidance!

Hi @lelew, and welome to neurostars!

Not a direct answer to your question but SynBOLD-DisCo (GitHub - MASILab/SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans) or OMNI_synth (omni_synthpreproc — omni documentation) which you can use to create a synthetic fieldmap, might be better suited for your purposes.

Best,
Steven

Thank you, Steven! I’ll look into this. Do you happen to have any resources comparing both methods, or at least comparing more common distortion correction techniques and/or their influence on the true signal? I apologize for asking, but I’m struggling to see the advantage of using synthetic fieldmaps when I have access to real data that could potentially help. However, I’m still relatively new to these preprocessing steps, so any additional insights would be greatly appreciated.

Hi @lelew

They have not been compared against each other.

You can read the papers (https://www.sciencedirect.com/science/article/pii/S0730725X23001121 and https://www.sciencedirect.com/science/article/pii/S1878929323000397)

The real data you have are used to inform the predicted distortion map. I think the papers will be able to explain better.

Best,
Steven

Thank you very much for the resources and your help with this!

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Oh, thank you for the updated documentation as well!