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- #!/bin/tcsh -xef
- # Here, we'll go from the DWI + (TORTOISE-style) B-matrix information
- # to do tensor reconstruction and tracking.
- # For the Sept, 2014 NIH Bootcamp:
- # these commands can be run in the TORTOISE-processed demo data set
- # (AFNI_bootcamp_TORTOISE_tutorial_data.tar.gz) in the following
- # directory--
- # TORTOISE_tutorial_2014/DR-BUDDI_example/final/dti_35vol_AP_scan1_up_bupdown_DMC_L0_SAVE_AFNI/
- # *******************************************************************
- # slightly more svelte brain mask
- 3dAutomask \
- -prefix mask2.nii.gz \
- INPREF_MD.nii \
- -overwrite
- # We could convert the TORTOISE-style B-matrix to either AFNI-style
- # one or to grads (see "1dDW_Grad_o_Mat" help for more about style
- # differences); here, I'll opt for 3 columns of gradient components,
- # and we won't need to keep the single row of zeros at the top (i.e.,
- # don't need to turn on a switch to keep it-- 3dDWItoDT expects b=0
- # data in the first DWI brick, and no row of zeros in the grad file
- # for it).
- #
- # We will also need to check if one of the grads needs to be flipped
- # at all-- only way for sure I know to check this is to process
- # through to whole brain tracking and see that it looks ok (checking
- # through corpus callosum and cingulate bundles seems good procedure).
- # -> after first attempt with no flip, I saw that the CC was pretty
- # empty in the middle, probably falsely so, so I invoked a -flip_z...
- 1dDW_Grad_o_Mat \
- -in_bmatT_cols BMTXT.txt \
- -out_grad_cols GRADS.txt \
- -flip_z
- # tensor reconstruction-- sep_dsets is useful here; nonlinear is
- # default anyways.
- 3dDWItoDT -nonlinear -eigs -sep_dsets \
- -mask mask2.nii.gz \
- -prefix DT \
- GRADS.txt \
- DWI.nii \
- -overwrite
- # simple deterministic, WB tracking; just use 1 seed per vox for speed
- 3dTrackID -mode DET \
- -mask mask2.nii.gz \
- -netrois mask2.nii.gz \
- -logic OR \
- -prefix o.WB \
- -dti_in DT \
- -alg_Nseed_X 1 \
- -alg_Nseed_Y 1 \
- -alg_Nseed_Z 1 \
- -overwrite
- # view it-- looks lovely!
- suma -tract o.WB_000.niml.tract
- # *******************************************************************
- # and now a probabilistic example:
- # First, we need to estimate uncertainty of a few key DT parameters
- # that are used in tracking. Below a *very* small number of
- # iterations is used, just for the sake of brevity.
- 3dDWUncert \
- -grads GRADS.txt \
- -inset DWI.nii \
- -input DT \
- -mask mask2.nii.gz \
- -prefix o.UNC \
- -iters 10 \
- -overwrite
- # a really simple mini-probabilistic procedure.
- 3dTrackID -mode MINIP \
- -mask mask2.nii.gz \
- -netrois mask2.nii.gz \
- -logic OR \
- -prefix o.WB_MP \
- -dti_in DT \
- -uncert o.UNC_UNC+orig \
- -mini_num 5 \
- -alg_Nseed_X 1 \
- -alg_Nseed_Y 1 \
- -alg_Nseed_Z 1 \
- -overwrite
- # View it!
- suma -tract o.WB_000.niml.tract
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