This tool uses the simple
linear tensor approach to estimate the diffusion tensor. The methods available
to estimate the tensor are: ordinary least squares, weighted least squares, and
robust tensor fitting (that basically used the method of Zwiers (2010) with a
couple of modifications). The robust tensor fitting approach down-weights outliers in the diffusion signal and thus is recommended if
your data suffers from outliers, e.g. due to physiological noise or subject
motion. It works well if your outliers are sparsely distributed along different
diffusion directions (Zwiers (2010)) and you acquired enough data (N>30, where
“N” is the number of images in the DTI sequence, Chang et al. (2005)). Note that for data with low SNR (SNR » 4) the Gaussian noise distribution might be violated and the methods in
this toolbox might not be valid anymore.
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Use EC and Motion Correction to
correct for EC and motion artefacts. The EC and motion corrected images will
have a prefix “r”.
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Define two variables in matlab, which cover the diffusion directions (3xN
matrix) and b-values (1xN vector), before running the Fit Diffusion tensor
toolbox. The “i-th” column (component)
must correspond to the vector of the diffusion gradient (and the b-value) of
the “i-th” image in the DTI dataset. If the b-value for the low-b-value images
is unknown, type b=1, and if its diffusion gradient direction is unknown, type
a random direction, which is normalised to 1.
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1.
Load pre-processed DTI images.
2.
Load the diffusion directions (3xN vector).
3.
Load the b-values (1xN vector).
4.
Methods to estimate the diffusion tensor:
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ordinary least squares,
-
weighted least squares,
-
robust fitting (Note that this methods assumes that you acquired 2D EPI
data with: z = slice encoding).
Settings:
5.
Choose whether you want to write out the estimated DTI data (i.e.
low-b-value and high-b-value images).
6.
“The confidence interval” changes
the threshold above which outlier will be down-weighted. By increase C you
became more sensitive to outliers but also increase the noise level in your
tensor estimates. The used value for C has been recommended by previous studies
(see e.g. Zwiers (2010) or Meer et al. (1991)). Don’t change this value, if you
don’t know what you are doing.
7.
“Smoothing the residuals”: This measure determines the width of a
two-dimensional in-plane smoothing kernel which is applied on your
residual map (i.e. root-mean-square of
your tensor-fit error).
8.
Option to write all eigenvectors (by default the eigenvector with
greatest eigenvalue as well as all eigenvalues are also written as an output).
9.
Option to write the diffusion tensor and the diffusion-weighted in a
format that can be read by the Freiburg DTI&Fiber tools (Freiburg DTI
tools).
10. Standard deviation of
logarithm of the signal outside the brain. This measure is used if the noise
cannot be estimated from outside the brain (see brain mask option).
11. Option: write brain mask.
Note this brain mask is not optimised to cover the brain shape but to separate
brain-tissue from voxels outside the brain. This option is only there to check
your settings. To construct a useful brain mask see “Pre-processing/Make brain
mask”.
Please cite the
following paper when using this toolbox:
Mohammadi S, Hutton C, Nagy Z, Josephs O, Weiskopf N
(2012), Retrospective correction of physiological noise in DTI using an
extended tensor model and peripheral measurements. Magnetic Resonance in
Medicine, (in press); doi:
10.1002/mrm.24467.
Other literature:
Chang LC,
Jones DK and Pierpaoli C (2005), RESTORE: robust estimation of tensors by
outlier rejection. Magnetic Resonance in Medicine 53: 1088-1095.
Meer P, Mintz D, Rosenfeld A, Kim DY (1991), Robust
regression methods for computer vision: a review. Int J Comput Vis 6: 59–70.
Zwiers M (2010), Patching
cardiac and head motion artefacts in diffusion-weighted images. NeuroImage 53: 565-575.