7.1.67. 3dDWUncert

Link to classic view

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:
    1. 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.
    • ** * ** * ** * ** * ** * ** * ** * ** * ** * ** * ** * ** * ** * ** * **
  • DTI LIST FILE EXAMPLE:

    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.

Table Of Contents

This Page