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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"> <HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<p><h3>Bedpost</h3> <p><h3>Bedpost</h3>
<p>Bedpost stands for Bayesian Estimation of Diffusion Parameters obtained using sampling <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 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 files necessary for running probabilistic tractography. For an overview of
the modelling carried out within Bedpost see the <a the modelling carried out within Bedpost see the <a
href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/">appendix.</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. processors are available.
<p>To call the FDT GUI, either run <b>Fdt</b>, or run <b>fsl</b> and press the <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>. <b>FDT</b> button. Use the top left drop down menu to select <b>Bedpost</b>.
<p> <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: That directory must contain the following files:
<ul> <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> 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</b>: 3D volume with no diffusion weighting</li>
<li><b>nodif_brain_mask</b>: 3D binary brain mask volume derived <li><b>nodif_brain_mask</b>: 3D binary brain mask volume derived
from running <b>bet</b> on nodif</li> from running <a href="../bet/index.html" target="_top">bet</a> on nodif</li>
<li><b>bvecs</b> A text file containing a list <li><b>bvecs</b>: A text file containing a list
of gradient directions applied during diffusion weighted volumes. The 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>. order of entries in this file must match the order of volumes in <b>data</b>.
<br> <br>
The format is <br> The format is<pre>
x_1 x_2 x_3 ... x_n<br> x_1 x_2 x_3 ... x_n<br>
y_1 y_2 y_3 ... y_n<br> y_1 y_2 y_3 ... y_n<br>
z_1 z_2 z_3 ... z_n<br> z_1 z_2 z_3 ... z_n<br></pre>
<br>
Vectors should be normalised. For volumes in which there was no Vectors should be normalised. For volumes in which there was no
diffusion weighting, the entry should still be present, although the diffusion weighting, the entry should still be present, although the
direction of the vector does not matter! </li> 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 ...@@ -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 order of volumes in the input data and entries in the gradient directions text
file. file.
<br> <br>
The format is <br> The format is <br><pre>
b_1 b_2 b_3 ... b_n<br> b_1 b_2 b_3 ... b_n<br></pre>
<br>
The order of <b>bvals</b> must match the order of <b>data.</b></li> The order of <b>bvals</b> must match the order of <b>data.</b></li>
</ul> </ul>
<p><h4>Outputs of Bedpost</h4> <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"> <HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<h3>DTIFit</h3> <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 <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>. <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 <p><b>Input:</b> You can specify an input directory containing all the required files
with standardized filenames, with standardized filenames,
or alternatively you can specific input files manually by turning on the or alternatively you can specify 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 <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 parentheses below. If input files are specified manually they can have any
filename. Required files are: filename. Required files are:
<ul> <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> 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 <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 ...@@ -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 order of entries in this file must match the order of volumes in the
input data series. input data series.
<br> <br>
The format is <br> The format is <br><pre>
x_1 x_2 x_3 ... x_n<br> x_1 x_2 x_3 ... x_n<br>
y_1 y_2 y_3 ... y_n<br> y_1 y_2 y_3 ... y_n<br>
z_1 z_2 z_3 ... z_n<br> z_1 z_2 z_3 ... z_n<br></pre>
<br> Vectors should be normalised to unit length. For volumes in which there was no
Vectors should be normalised. For volumes in which there was no
diffusion weighting, the entry should still be present, although the diffusion weighting, the entry should still be present, although the
direction of the vector does not matter! </li> 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 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 order of volumes in the input data and entries in the gradient directions text
file. file.
<br> <br>
The format is <br> The format is <br> <pre>
b_1 b_2 b_3 ... b_n<br> b_1 b_2 b_3 ... b_n<br></pre>
<br> <br>
</li> </li>
</ul> </ul>
<h4>Outputs of DTIFit</h3. <h4>Outputs of DTIFit</h3.
<ul> <ul>
<li><b>basename_V1</b> - 1st eigenvector</li> <li><b>&lt;basename&gt;_V1</b> - 1st eigenvector</li>
<li><b>basename_V2</b> - 2nd eigenvector</li> <li><b>&lt;basename&gt;_V2</b> - 2nd eigenvector</li>
<li><b>basename_V3</b> - 3rd eigenvector</li> <li><b>&lt;basename&gt;_V3</b> - 3rd eigenvector</li>
<li><b>basename_L1</b> - 1st eigenvalue</li> <li><b>&lt;basename&gt;_L1</b> - 1st eigenvalue</li>
<li><b>basename_L2</b> - 2nd eigenvalue</li> <li><b>&lt;basename&gt;_L2</b> - 2nd eigenvalue</li>
<li><b>basename_L3</b> - 3rd eigenvalue</li> <li><b>&lt;basename&gt;_L3</b> - 3rd eigenvalue</li>
<li><b>basename_MD</b> - Mean Diffusivity</li> <li><b>&lt;basename&gt;_MD</b> - mean diffusivity</li>
<li><b>basename_FA</b> - Fractional anisotropy</li> <li><b>&lt;basename&gt;_FA</b> - fractional anisotropy</li>
<li><b>basename_S0</b> - raw T2 signal with no diffusion weighting</li> <li><b>&lt;basename&gt;_S0</b> - raw T2 signal with no diffusion weighting</li>
</ul> </ul>
<TABLE WIDTH=100% BORDER=0 CELLPADDING=0 CELLSPACING=0> <TABLE WIDTH=100% BORDER=0 CELLPADDING=0 CELLSPACING=0>
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