- Use jackknifing to estimate uncertainty of DTI parameters which are
- important for probabilistic tractography on per voxel basis.
- Produces useful input for 3dTrackID, which does both mini- and full
- probabilistic tractography for GM ROIs in networks, part of FATCAT (Taylor & Saad, 2013) in AFNI.
COMMAND: 3dDWUncert -inset FILE -input [base of FA/MD/etc.]
{-grads | -bmatr} FILE -prefix NAME -iters NUMBER
- OUTPUT:
AFNI-format file with 6 subbricks, containing uncertainty information. The bricks are in the following order:
[0] bias of e1 in direction of e2 [1] stdev of e1 in direction of e2 [2] bias of e1 in direction of e3 [3] stdev of e1 in direction of e3 [4] bias of FA [5] stdev of FA
RUNNING, need to provide: -inset FILE :file with b0 and DWI subbricks
(e.g., input to 3dDWtoDTI)
-prefix PREFIX :output file name part. -input INPREF :basename of DTI volumes output by,
e.g., 3dDWItoDT or TORTOISE. Assumes format of name is, e.g.: INPREF_FA+orig.HEAD or INPREF_FA.nii.gz . Files needed with same prefix are: FA, L1, V1, V2, V3 .
- -input_list FILE :an alternative way to specify DTI input files, where
- FILE is a NIML-formatted text file that lists the explicit/specific files for DTI input. This option is used in place of ‘-input INPREF’. See below for a ‘INPUT LIST FILE EXAMPLE’.
-grads FILE :file with 3 columns for x-, y-, and z-comps of DW-gradients (which have unit magnitude). NB: this option also assumes that only 1st DWI subbrick has a b=0 image (i.e., all averaging of multiple b=0 images has been done already); if such is not the case, then you should convert your grads to the bmatrix format and use `-bmatr’. OR
- -bmatr FILE :using this means that file with gradient info
- is in b-matrix format, with 6 columns representing: b_xx 2b_xy 2b_xz b_yy 2b_yz b_zz. NB: here, bvalue per image is the trace of the bmatr, bval = b_xx+b_yy+b_zz, such as 1000 s/mm^2. This option might be used, for example, if multiple b-values were used to measure DWI data; if TORTOISE preprocessing has been employed, then its *.bmtxt file can be used directly.
- -mask MASK :can include a mask within which to calculate uncert.
- Otherwise, data should be masked already.
-iters NUMBER :number of jackknife resample iterations, e.g. 50.
- -calc_thr_FA FF :set a threshold for the minimum FA value above which
- one calculates uncertainty; useful if one doesn’t want to waste time calculating uncertainty in very low-FA voxels that are likely GM/CSF. For example, in adult subjects one might set FF=0.1 or 0.15, depending on SNR and user’s whims (default: FF=-1, i.e., do all).
- -csf_fa NUMBER :number marking FA value of `bad’ voxels, such as
- those with S0 value <=mean(S_i), which breaks DT assumptions due to, e.g., bulk/flow motion. Default value of this matches 3dDWItoDT value of csf_fa=0.012345678.
Consider, for example, if you hadn’t used the ‘-sep_dsets’ option when outputting all the tensor information from 3dDWItoDT. Then one could specify the DTI inputs for this program with a file called, e.g., FILE_DTI_IN.niml.opts (the name must end with ‘.niml.opts’):
- <DTIFILE_opts
dti_V1=”SINGLEDT+orig[9..11]” dti_V2=”SINGLEDT+orig[12..14]” dti_V3=”SINGLEDT+orig[15..17]” dti_FA=”SINGLEDT+orig[18]” dti_L1=”SINGLEDT+orig[6]” />
This represents the minimum set of input files needed when running 3dDWUncert. (Note that MD isn’t needed here.) You can also recycle a NIMLly formatted file from ‘3dTrackID -dti_list’– the extra inputs needed for the latter are a superset of those needed here, and won’t affect anything detrimentally (I hope).
- EXAMPLE:
3dDWUncert -inset TEST_FILES/DTI/fin2_DTI_3mm_1+orig -prefix TEST_FILES/DTI/o.UNCERT -input TEST_FILES/DTI/DT -grads TEST_FILES/Siemens_d30_GRADS.dat -iters 50
If you use this program, please reference the jackknifing algorithm done with nonlinear fitting described in:
Taylor PA, Biswal BB (2011). Geometric analysis of the b-dependent effects of Rician signal noise on diffusion tensor imaging estimates and determining an optimal b value. MRI 29:777–788.
- and the introductory/description paper for the FATCAT toolbox:
- Taylor PA, Saad ZS (2013). FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox. Brain Connectivity 3(5):523-535.