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doc/fdt_bedpostx.html
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<p><h3>Bedpost</h3>
<p>Bedpost stands for Bayesian Estimation of Diffusion Parameters obtained using sampling
techniques. Bedpost runs Markov Chain Monte Carlo sampling to build up distributions on diffusion parameters at each voxel. It creates all the
files neccessary for running probabilistic tractography. For an overview of
<p>Bedpost stands for Bayesian Estimation of Diffusion Parameters Obtained using Sampling
Techniques. Bedpost runs Markov Chain Monte Carlo sampling to build up distributions on diffusion parameters at each voxel. It creates all the
files necessary for running probabilistic tractography. For an overview of
the modelling carried out within Bedpost see the <a
href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/">appendix.</a>
<br>Bedpost takes about 24 hours to run but can easily be <a href="fdt_bedpost_parallel.html">parallelised</a> if multiple
<p>Bedpost takes about 24 hours to run but can easily be <a href="fdt_bedpost_parallel.html">parallelised</a> if multiple
processors are available.
<p>To call the FDT GUI, either run <b>Fdt</b>, or run <b>fsl</b> and press the
<b>FDT</b> button. Use the top left drop down menu to select <b>Bedpost</b>.
<p>
<b>Input directory:</b> Use the browse button to select and input directory.
<b>Input directory:</b> Use the browse button to select an input directory.
That directory must contain the following files:
<ul>
<li><b>data</b>: A 4-dimensional series of data volumes. This will
<li><b>data</b>: A 4D series of data volumes. This will
include diffusion-weighted volumes and volume(s) with no diffusion weighting.</li>
<li><b>nodif</b>: 3D volume with no diffusion weighting</li>
<li><b>nodif_brain_mask</b>: 3D binary brain mask volume derived
from running <b>bet</b> on nodif</li>
<li><b>bvecs</b> A text file containing a list
from running <a href="../bet/index.html" target="_top">bet</a> on nodif</li>
<li><b>bvecs</b>: A text file containing a list
of gradient directions applied during diffusion weighted volumes. The
order of entries in this file must match the order of volumes in <b>data</b>.
<br>
The format is <br>
The format is<pre>
x_1 x_2 x_3 ... x_n<br>
y_1 y_2 y_3 ... y_n<br>
z_1 z_2 z_3 ... z_n<br>
<br>
z_1 z_2 z_3 ... z_n<br></pre>
Vectors should be normalised. For volumes in which there was no
diffusion weighting, the entry should still be present, although the
direction of the vector does not matter! </li>
......@@ -35,9 +36,8 @@ each volume acquisition. The order of entries in this file must match the
order of volumes in the input data and entries in the gradient directions text
file.
<br>
The format is <br>
b_1 b_2 b_3 ... b_n<br>
<br>
The format is <br><pre>
b_1 b_2 b_3 ... b_n<br></pre>
The order of <b>bvals</b> must match the order of <b>data.</b></li>
</ul>
<p><h4>Outputs of Bedpost</h4>
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doc/fdt_dtifit.html
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<h3>DTIFit</h3>
<b>DTIFit</b> fits a diffusion tensor model at each voxel. You would typically run
<b>DTIFit</b> on data that has been pre-processed and eddy current corrected.
<b>DTIFit</b> fits a diffusion tensor model at each voxel. You would
typically run DTIFit on data that has been pre-processed and
eddy current corrected. Note that DTIFit is not necessary in order to
use the probabilistic tractography (which depends on the output of
BEDPOST not DTIFit).
<p>To call the FDT GUI, either run <b>Fdt</b>, or run <b>fsl</b> and press the
<b>FDT</b> button. Use the top left drop down menu to select <b>DTIFit</b>.
<p><b>Input:</b> You can specify an input directory containing all the required files
with standardized filenames,
or alternatively you can specific input files manually by turning on the
<b>specify input files manually</b> tab. If an input directory is specified then all files must be named as shown in
or alternatively you can specify input files manually by turning on the
<b>specify input files manually</b> switch. If an input directory is specified then all files must be named as shown in
parentheses below. If input files are specified manually they can have any
filename. Required files are:
<ul>
<li><b>Diffusion weighted data</b> (data): A 4-dimensional series of data volumes. This will
<li><b>Diffusion weighted data</b> (data): A 4D series of data volumes. This will
include diffusion-weighted volumes and volume(s) with no diffusion weighting.</li>
<li><b>BET binary brain mask</b> (nodif_brain_mask): A single binarised
......@@ -28,37 +33,36 @@ of gradient directions applied during diffusion weighted volumes. The
order of entries in this file must match the order of volumes in the
input data series.
<br>
The format is <br>
The format is <br><pre>
x_1 x_2 x_3 ... x_n<br>
y_1 y_2 y_3 ... y_n<br>
z_1 z_2 z_3 ... z_n<br>
<br>
Vectors should be normalised. For volumes in which there was no
z_1 z_2 z_3 ... z_n<br></pre>
Vectors should be normalised to unit length. For volumes in which there was no
diffusion weighting, the entry should still be present, although the
direction of the vector does not matter! </li>
<li><b>bvalues</b> (bvals): A text file containing a list of bvalues applied during
<li><b>b values</b> (bvals): A text file containing a list of b values applied during
each volume acquisition. The order of entries in this file must match the
order of volumes in the input data and entries in the gradient directions text
file.
<br>
The format is <br>
b_1 b_2 b_3 ... b_n<br>
The format is <br> <pre>
b_1 b_2 b_3 ... b_n<br></pre>
<br>
</li>
</ul>
<h4>Outputs of DTIFit</h3.
<ul>
<li><b>basename_V1</b> - 1st eigenvector</li>
<li><b>basename_V2</b> - 2nd eigenvector</li>
<li><b>basename_V3</b> - 3rd eigenvector</li>
<li><b>basename_L1</b> - 1st eigenvalue</li>
<li><b>basename_L2</b> - 2nd eigenvalue</li>
<li><b>basename_L3</b> - 3rd eigenvalue</li>
<li><b>basename_MD</b> - Mean Diffusivity</li>
<li><b>basename_FA</b> - Fractional anisotropy</li>
<li><b>basename_S0</b> - raw T2 signal with no diffusion weighting</li>
<li><b>&lt;basename&gt;_V1</b> - 1st eigenvector</li>
<li><b>&lt;basename&gt;_V2</b> - 2nd eigenvector</li>
<li><b>&lt;basename&gt;_V3</b> - 3rd eigenvector</li>
<li><b>&lt;basename&gt;_L1</b> - 1st eigenvalue</li>
<li><b>&lt;basename&gt;_L2</b> - 2nd eigenvalue</li>
<li><b>&lt;basename&gt;_L3</b> - 3rd eigenvalue</li>
<li><b>&lt;basename&gt;_MD</b> - mean diffusivity</li>
<li><b>&lt;basename&gt;_FA</b> - fractional anisotropy</li>
<li><b>&lt;basename&gt;_S0</b> - raw T2 signal with no diffusion weighting</li>
</ul>
<TABLE WIDTH=100% BORDER=0 CELLPADDING=0 CELLSPACING=0>
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