Would appreciate confirmation that my pipeline is accurate to calculate FA in ROIs

The goal is to calculate diffusivity in specific ROIs.

My steps were to preprocess, normalize, and register the ROI mask to subject native space. I am confident in those commands as the output look good.

To get DTI values I then used :

#whole brain streamlines, probabilistic (includes random variation/noise)
tckgen -act ${dir_out_sub}/anatomical/${subject}_T1_5tt_coreg.mif -backtrack -seed_gmwmi ${dir_out_sub}/anatomical/${subject}_gmwmSeed_coreg.mif -seed_image ${dir_out_sub}/preprocessed/dwi_${subject}_mask.mif -nthreads 8 -minlength 25 -cutoff 0.06 -select 10M ${dir_out_sub}/basis_functions/wmfod_norm_${subject}.mif ${outDir}/${subject}_WholeBrain_ACT_10M.tck -info

echo "Sifting the tracks with tcksift:" #-term_number 10M <not needed?
tcksift -act ${dir_out_sub}/anatomical/${subject}_T1_5tt_coreg.mif  ${outDir}/${subject}_WholeBrain_ACT_10M.tck ${dir_out_sub}/basis_functions/wmfod_norm_${subject}.mif ${outDir}/${subject}_sift_10mio.tck

echo "convert ROI into mif and then mask"
mrconvert ${dir_out_sub}/Warped_Tract/${subject}_LEFT.nii.gz ${dir_out_sub}/Warped_Tract/${subject}_LEFT.mif
mrthreshold ${dir_out_sub}/Warped_Tract/${subject}_LEFT.mif ${dir_out_sub}/Warped_Tract/${subject}_LEFT_mask.mif

echo "tckedit to get streamlines within ROI mask"
tckedit ${outDir}/${subject}_sift_10mio.tck  ${outDir}/${subject}_sift_LEFT_10mio.tck -mask ${dir_out_sub}/Warped_Tract/${subject}_LEFT_mask.mif

echo "calculating mean FA in subject space / ROI"
tcksample -stat_tck mean ${outDir}/${subject}_sift_LEFT_10mio.tck ${dir_out_sub}/tensors/FA_${subject}.mif ${meanDIR}/FA/${subject}_LEFT_meanFA.txt

I then use R to calculate the average of the entire ${subject}_LEFT_meanFA.txt output file.

Is there any issue with my approach? Or suggestions to improve? Thanks in advance.

*also should my tcksift command above produce less than 10million streamlines?