Would it be possible to use fmriprep fieldmapless-estimation (https://fmriprep.readthedocs.io/en/stable/api/index.html#sdc-fieldmapless) to correct for b0 inhomogeneities in DTI data as done in Wang, 2017?
Would I just add the b0 volume as the epi image? Then could I apply the warp output and to the diffusion-weighted images?
I am trying to employ the technique suggested in Wang, 2017 for my data because it doesn’t have reverse-phase encoding or a field map.
Thank you for any help you can provide!
Yes, although we are working on making it work in isolation (without fMRIPrep). This work is happening on this repo: https://github.com/poldracklab/sdcflows
Hi Osteban, thank you for the help! I looked in sdcflows and it seems like syn.py might be the code to use. I am a bit confused on how implementation might work. I assume I would first download the repository and then make a call to init_syn_sdc_wf?
If I have a b0 volume from my DTI data and a skull stripped and biased corrected T1w image I wanted to use for this correction how would I apply them to the syn.py code you have created?
Sorry if this is a sill question. I don’t have tons of experience with nipype. Thank you for any guidance you can provide.