AFNI program: 3dDTtoNoisyDWI
Output of -help
Take an AFNI-style DT file as input, such as might be output by 3dDWItoDT
(which means that the DT elements are ordered: Dxx,Dxy,Dyy,Dxz,Dyz,Dzz),
as well as a set of gradients, and then generate a synthetic set of DWI
measures with a given SNR. Might be useful for simulations/testing.
Part of FATCAT (Taylor & Saad, 2013) in AFNI.
It is similar in premise to 3dDTtoDWI, however this allows for the modeled
inclusion of Rician noise (such as appears in MRI magnitude images).
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+ COMMAND: 3dDTtoNoisyDWI -dt_in DTFILE -grads GRADFILE -noise_frac0 FF \
{-bval BB} {-S0 SS} {-mask MASK } -prefix PREFIX
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+ OUTPUT:
1) If N gradients are input, then the output is a file with N+1 bricks
that mimics a set of B0+DWI data (0th brick is the B0 reference).
+ RUNNING:
-dt_in DTFILE :diffusion tensor file, which should have six bricks
of DT components ordered in the AFNI (i.e., 3dDWItoDT)
manner:
Dxx,Dxy,Dyy,Dxz,Dyz,Dzz.
-grads GRADFILE :text file of gradients arranged in three columns.
It is assumed that there is no row of all zeros in the
GRADFILE (i.e., representing the b=0 line).
If there are N rows in GRADFILE, then the output DWI
file will have N+1 bricks (0th will be the b=0
reference set of noise S0 measures).
-noise_DWI FF :fractional value of noise in DWIs. The magnitude will
be set by the b=0 reference signal, S0. Rician noise
is used, which is characterized by a standard
deviation, sigma, so that FF = sigma/S0 = 1/SNR0.
For example, FF=0.05 roughly corresponds to an
SNR0=20 'measurement'.
-noise_B0 FF2 :optional switch to use a different fraction of Rician
noise in the b=0 reference image; one might consider
it realistic to have a much lower level of noise in
the reference signal, S0, mirroring the fact that
generally multiple averages of b=0 acquisitions are
averaged together. If no fraction is entered here,
then the simulation will run with FF2=FF.
-prefix PREFIX :output file name prefix. Will have N+1 bricks when
GRADFILE has N rows of gradients.
-mask MASK :can include a mask within which to calculate uncert.
Otherwise, data should be masked already.
-bval BB :optional DW factor to use if one has DT values scaled
to something physical (NB: AFNI 3dDWItoDT works in a
world of b=1, so the default setting here is BB=1; one
probably doesn't need to change this if using DTs made
by 3dDWItoDT).
-S0 SS :optional reference b=0 signal strength. Default value
SS=1000. This just sets scale of output.
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+ EXAMPLE:
3dDTtoNoisyDWI \
-dt_in DTI/DT_DT+orig \
-grads GRADS.dat \
-noise_DWI 0.1 \
-noise_B0 0 \
-prefix NEW_DWIs_SNR10
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If you use this program, please reference 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.
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