I have been tinkering with main.nf, so far successfully, to add steps such as Gibbs correction to the pipeline. Now I am trying to implement RESTORE robust tensor estimation. On paper, this seems to be an easy task, as the tool used to estimate the tensor (scil_compute_dti_metrics.py) has an option to run RESTORE built into it.
I tried to implement this by adding --method restore\ to the DTI_Metrics process, but I am thrown an error. Tracing back to the log file for the command that invokes scil_compute_dti_metrics, I see the following output message:
Please let me know if there is a way to address this error. Also, should I be concerned about the b-vector normalization warning towards the beginning?
I am running Tractoflow 2.1.1 with the appropriate 2.1.1 singularity container on HPC.
Edit: The data have up to 30 gradients at b=1000 (depending on how much is thrown out during QC) and 4 b0 scans. The data have been denoised, de-Gibbed, and eddy/motion corrected. I have also tagged DIPY now, since SCILPY calls the RESTORE tensor fitting from DIPY.