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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"> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
<IMG ALIGN=RIGHT hspace=20 vspace=20 SRC="fdt_images/fdt_bedpost.gif" "http://www.w3.org/TR/html4/loose.dtd">
ALT="Bedpost GUI view"> <HTML><HEAD><meta http-equiv="Content-Type"
<p><h3>Bedpost</h3> content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<IMG ALIGN=RIGHT hspace=20 vspace=20 SRC="fdt_images/fdt_bedpost.gif" ALT="Bedpost GUI view">
<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 necessary for running probabilistic tractography. For an overview of files necessary for running probabilistic tractography. For an overview of
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doc/fdt_bedpostx_parallel.html
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg"> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
<p><h3>Parallelising bedpost</h3> Bedpost runs Markov Chain Monte "http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<h3>Parallelising bedpost</h3> Bedpost runs Markov Chain Monte
Carlo sampling to build up distributions on diffusion parameters at Carlo sampling to build up distributions on diffusion parameters at
each voxel. This is a very slow process, so bedpost is very processor each voxel. This is a very slow process, so bedpost is very processor
hungry (a typical bedpost run might take around 20hrs for 60 hungry (a typical bedpost run might take around 20hrs for 60
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<h3>Before running</h3>
<p>Fdt requires 4D analyze data. This will consist of a series of diffusion
weighted volumes and some volumes with no diffusion weighting applied. If you
are running Bedpost or dtifit you
will need text files describing the b-values applied during acquisition of
each volume ('bvals'), and the direction in which diffusion weighting was
applied ('bvecs').
doc/fdt_biggest.html
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
BACKGROUND="fdt_images/fsl-bg.jpg"> "http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<h3>find_the_biggest</h3> <h3>find_the_biggest</h3>
<b>find_the_biggest</b> is a command line utility that performs hard <b>find_the_biggest</b> is a command line utility that performs hard
segmentation of a seed region on the basis of outputs from the <b>Connectivity-based seed classification</b> mode segmentation of a seed region on the basis of outputs from the <b>Connectivity-based seed classification</b> mode
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<hr><font size="1">Copyright &copy 2007, University of Oxford. Written
by <a href="http://www.fmrib.ox.ac.uk/~heidi/index.html" target="_top">H. Johansen-Berg</a>, <a href="http://www.fmrib.ox.ac.uk/~behrens/index.html" target="_top"> T. Behrens</a> and <a href="http://www.fmrib.ox.ac.uk/~saad/index.html" target="_top"> S. Jbabdi</a>.</font>
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg"> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
<IMG ALIGN=MIDDLE hspace=10 vspace=20 SRC="fdt_images/fdt_vectorsx.jpg" "http://www.w3.org/TR/html4/loose.dtd">
ALT="Visualising vector data"> <HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<IMG ALIGN=RIGHT hspace=20 vspace=20 SRC="fdt_images/fslview_dti.gif" ALT="Example GUI view">
<h3>Displaying DWI images in fslview</h3> <h3>Displaying DWI images in fslview</h3>
Outputs of <b>Bedpost</b> or <b>DtiFit</b> can be conveniently displayed in fslview. Outputs of <b>Bedpost</b> or <b>DtiFit</b> can be conveniently displayed in fslview.
If you open an image of diffusion vectors (e.g., dtifit_V1 output of DtiFit or dyads&#60;i&#62; output of Bedpost) then it is If you open an image of diffusion vectors (e.g., dtifit_V1 output of DtiFit or dyads&#60;i&#62; output of Bedpost) then it is
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<h3>Introduction</h3>
<p>For other information on FDT and updated journal references, see the <a href="http://www.fmrib.ox.ac.uk/analysis/research/fdt/">FDT web
page</a>. If you use FDT in your research, please quote the journal
references listed there.
<p>FDT (FMRIB Diffusion Toolbox) is a software tool for analysis of diffusion weighted images. FDT is part
of <a href="http://www.fmrib.ox.ac.uk/fsl">FSL</a> (FMRIB's Software Library)
. FDT has an easy-to-use graphical user interface (GUI) and its component
programmes can also be run from the command line. FDT includes tools for
data preprocessing, local diffusion modelling and tractography. Each stage in FDT is run
separately.
The main FDT programmes are:
<p>eddycorrect - for correction of eddy current distortion
<br>Bedpost - for local modelling of diffusion parameters.
<br>ProbTrack - for tractography and connectivity-based segmentation
<br>DtiFit - for local of fitting of diffusion tensors
<br>The FDT GUI also includes a registration option that registers images using <a
href="http://www.fmrib.ox.ac.uk/fsl/flirt/index.html"> FLIRT </a>
<p>A typical processing pipeline (and approximate time required for each
stage, based on an Intel 2.4GHz processor, and a 60 direction whole brain
dataset of dimensions 256x208x72)
would consist of:
<br>1. Any study or scanner-specific pre-processing (e.g., averaging of multiple
acquisitions, removal of images affected by large artifacts). This would be
done manually by the user.
<br>2. Eddy current correction using FDT (??minutes).
<br>3. Fitting of diffusion tensors on corrected data using dtifit within FDT to check data quality (10 minutes)
<br>4. Fitting of the probabilistic diffusion model on corrected data using Bedpost within FDT
(24 hours, or less if parallelised)
<br>5. Probabilistic tractography run on the outputs of bedpost (endless.. - depends very much on what the
user wishes to do. Generating a connectivity distribution from a single voxel
of interest takes about 10 seconds)
The probabilistic tractography tools within FDT are very flexible and allow
the user to generate connectivity distributions from single or multiple
voxels; to limit these distribution based on anatomical criteria and to
perform segmentation based on the probability of connection to user-defined
target regions.
<p>Before running:
<p>Fdt requires 4D analyze data. This will consist of a series of diffusion
weighted volumes and some volumes with no diffusion weighting applied. You
will need text files describing the b-values applied during acquisition of
each volume ('bvals'), and the direction in which diffusion weighting was
applied ('bvecs')
<p><hr><h3>Eddy Current Correction</h3>
<p><hr><h3>Bedpost</h3>
<p>Bedpost stands for Bayesian Estimation of Diffusion Parameters obtained using sampling
techniques
<p><hr><h3>Registration </h3>
<p>If tractography results are to be stored in any space other than diffusion
space then registration must be run.
<p>Registration within Fdt uses <a href="http://www.fmrib.ox.ac.uk/fsl/flirt">>flirt</a>. Registration can only be
applied after Bedpost has been run. Typically, registration will be run
between three spaces:
<br>Diffusion space (using the nodif_brain.{hdr,img} file stored in the Bedpost directory)
<br>Structural space (using the struct.{hdr,img} file stored in the Bedpost
directory, e.g., the space of a high resolution T1-weighted image of
the same subject)
<br>Standard space (by default, the MNI152 brain stored within the fsldirectory).
<p>Note that struct must have had bet applied. The nodif_brain image should be
the brain extracted version of the nodif image that is automatically stored in
the Bedpost directory. The user will
have to manually apply bet to this image after running bedpost and before
running registration. (it is important that the user check the qualtiy of bet
results on these images and adjust the settings in bet where appropriate)
<p>Transformation matrices, and their inverses, will be dervied from diffusion to structural space and
from structural to standard space. Relevant matrices will be concatenated to
produce transformation matrices between diffusion and standard space. The resulting matrices are stored within
the 'xfms' subdirectory of the bedpost directory and named as follows:
<p>diff2str.mat - from diffusion to structural space
<br>str2diff.mat - from structural to diffusion space
<br>diff2standard.mat - from diffusion to standard space
<br>standard2diff.mat - from standard to diffusion space
<br>str2standard.mat - from structural to standard space
<br>standard2str.mat - from standard to structural space
<p>By default, transformation matrices between diffusion and structural space are
derived using 6 degrees of fredom, the mutual information cost
function and normal search; transformations matrices between structural and standard space
are derived using 12 degrees of freedom, the correlation ratio cost
function and normal search. These parameters may be adjusted if required
using the drop down menus in the reigistration panel.
<p><b>******** PROBTRACK - probabilistic tracking *********</b>
<p>After Bedpost has been applied it is possible to run tractography analyses
using ProbTrack.
Probtrack can be run in multiple different modes. Every mode requires the user to
specify a bedpost directory. For all modes, the bedpost directory must contain the following files:
<br>merged_phsamples.{img,hdr}
<br>merged_thsamples.{img,hdr}
<br>nodif_brain_mask.{img,hdr}
<p>Results from probtract can be stored in any available space -e.g., diffusion
space, structural space or standard space. Note, however, that
tractography itself ALWAYS takes place in diffusion space - it is simply the
results of probtrack that are transformed into the required space before saving. If probtrack results are to be stored in a space other than diffusion space
then the following files must be in the xfms subdirectory of the bedpost directory:
<p>for running analyses in structural space:
<br>struct.{img,hdr}
<br>xfms/str2diff.mat
<br>xfms/diff2str.mat
<p>for running analyses in standard space:
<br>standard.{img,hdr}
<br>xfms/standard2diff.mat
<br>xfms/diff2standard.mat
<p>OPTIONS TAB
<br>For all modes in probtrack, the user is able to change the setting of certain
parameters by clicking the options tab.
<p>Number of samples (default 5000): This determines the number of individual
pathways (or samples) that are drawn through the probability distrubutions on
principle fibre direction (see <a href="appendix"> appendix </a>for more details on the modelling and
tractography methods). By default this is set to 5000 as we are confident
that convergence is reached with this number of particles. However, reducing
this number will speed up processing and can be useful for preliminary or
exploratory analyses.
<p>Curvature Threshold (default 0.2): We limit how sharply pathways can turn
in order to
exclude implausible pathways. This number is the cosine of the minimum allowable
angle between two steps. By default this is set to 0.2 (corresponding to a
minimum angle of approximately 80 degrees). Adjusting this number can enable
pathways with sharper angles to be detected.
<p>Verbose: If this option is selected then FDT prints additional logging
information to screen while it is running.
<p>Loopcheck: By default, we terminate pathways that loop back on themselves
-i.e paths that travel to a point where they have already been.
<p>Advanced options:
<br>Clicking the triangle reveals some further options:
<p>Use anisotropy constraints: By default, pathways are traced regardless of
anisotropy values. However, it is possible to constrain tracking according to
aniostropy values. ?? dunno what this does exactly
<p>Use modified Euler streamlining: hmmmm
<p>Maximum number of steps (default 1000): By default, particles are terminated
when they have travelled 1000 steps. Using a step length of 0.5mm this
corresponds to a distance of 50cm. These values can be adjusted if required.
<p>Step length (default 0.5mm): This determines the length of each step. This
setting may be adjusted from default e.g., depending on the voxel size being
used, or if tracking is being performed on different sized brains
(e.g,. infants of non-human animals).
<p><b>PATH DISTRIBUTION ESTIMATION</b>
<br>The first four modes of ProbTrack involve generating connectivity
distributions from user-specified seed voxel(s). The output will be a single
image in the space of the specified seed like <a href="pic"> this </a>. All
brain voxels will have a value (though many of these will be zero)
representing the connectivity value between that voxel and the seed voxel
(i.e., the number of particles that pass through that voxel). Note that when
connectivity distributions are generated from multiple seed voxel within a
region of interest then the time required for the analysis to run will be
approximately the number of seed voxels multiplied by the time taken to
generate a distrubiton from a single voxel.
<p>SETTING UP THE GUI FOR PATH DISTRIBUTION ESTIMATION - SETTINGS COMMON TO ALL
MODES:
<p>Seeds space: specification of seeds is different for each mode - see below.
<p>If seed space is not diffusion, then check the button and use the browse
buttons to locate a reference image (e.g., subject1.bedpost/struct.hdr if seed space is structral
space or subject1.bedpost/standard.hdr if seed space is standard space) and the transformation
matrix from seed space to diffusion space (e.g., subject1.bedpost/xfms/str2diff.mat if seed
space is structural space or subject1.bedpost/xfms/standard2diff.mat if seed space is standard
space).
<p>If an exclusion mask is to be used then check the box and use the browse button
to locate the mask file. This must be a binarised analyze file in seed
space. Pathways will be terminated if they enter the exclusion mask. For
example, an exclusion mask of the midline will terminate pathways that cross
into the other hemisphere. (Note that paths are always terminated when they reach the
brain surface as defined by nodif_brain_mask)
<p>Output:
<br>Use the browse button to specify an output name. This will be a filename or a
directory name depending on the mode.
<p>AVAILABLE MODES AND MODE-SPECIFIC SETTINGS:
<p>SINGLE SEED VOXEL: Generates a connectivity distribution from a single,
user-specified voxel
<br>GUI SETTTINGS
<br>Seeds space:
<br>Enter the x,y,z co-ordinates of a single seed voxel. Use the buttons to the
right to specify whether the co-ordinates are given in voxels or millimetres.
<br>OUTPUT
The output will be a single image in the space of the specified seed. All
brain voxels will have a value (though many of these will be zero)
representing the connectivity value between that voxel and the seed voxel
(i.e., the number of particles that pass through that voxel).
<p>SEED MASK MODE: Generates a connectivity distribution from a user-specified
region of interest.
<br>Settting up the GUI
<br>Seed image: Use the browse button to locate the seed image - this should be a binary mask.
<br>OUTPUT
<br>The output directory will contain:
<br>logfile - a text record of the command that was run
<br>The output file - will be a single image in the space of the specified seed
mask. All
brain voxels will have a value (though many of these may be zero)
representing the number of particles that pass through that voxel from the
seed mask. Connectivity distributions from multiple seed voxels are summed to
produce this output. Therefore the connectivity values will depend on the
number of voxels in the seed mask.
<p>SEED MASK AND TARGET MASK - generates a connectivity distribution from voxels
in the seed mask and retains only those paths that pass through the target
mask.
<br>Setting up the GUI
<br>Seed image and Target image: Use the browse buttons to locate binary masks of
the seed and target. These must be in the same space.
<br>OUTPUT:
<br>The output directory will contain:
<br>logfile - a text record of the command that was run
<br>The output file - will be a single image in the space of the specified seed mask. All
brain voxels will have a value (though many of these may be zero)
representing the number of particles that pass through that voxel from the
seed mask. Connectivity distributions from multiple seed voxels are summed to
produce this output. Therefore the connectivity values will depend on the
number of voxels in the seed mask.
<p>TWO MASKS - SYMMETRIC - generates a connectivity distribution from all
voxels in mask image 1 and retains only those pathways that pass through mask
image 2. Also
generates a connectivity distribution from all voxels in mask image 2 and retains
pathways that pass through mask image 1. Ouput is the sum of these connectivity
distributions.
<br>Setting up the GUI
<br>Mask image 1 and Mask image 2: Use the browse buttons to locate your binary
mask of area one and area two. These must be in the same space.
<br>OUTPUT:
<br>The output directory will contain:
<br>logfile - a text record of the command that was run
<br>The output file - will be a single image in the space of the specified masks. All
brain voxels will have a value (though many of these may be zero)
representing the number of particles that pass through that voxel from either
of the
seed masks. Connectivity distributions from multiple seed voxels are summed to
produce this output. Therefore the connectivity values will depend on the
number of voxels in the seed masks.
<p><b>CONNECTIVITY BASED SEED CLASSIFICATION</b>
<br>This mode quantifies connectivity values
between a seed mask and any number of user-specified target masks.
<br>Setting up the GUI
<br>Seed image: use the browse button to locate your binary mask of seed voxels.
<br>Target list: Use the add button to locate each target mask. Targets must be
binary masks in the same space as the seed mask. When all targets are loaded you
can press the save list button to save the list of targets as a text file. If
you already have a text file list of required targets (including their path)
then you can load it with the load list button.
<br>OUTPUT:
<br>The output directory will contatin:
<br>logfile - a text record of the command that was run
<br>A single volume for each target mask, named seeds_to_<target>.(img,hdr) where
<target> is replaced by the file name of the relevant target mask. In these
output images, the value of each voxel within
the seed mask is the number of particles seeded from that voxel reaching the
target mask. The value of all voxels outside the seed mask will be zero.
<p><b>********* DTIFit ***************</b>
<br>FDT can be used to fit a diffusion tensor model at each voxel.
<br>Setting up the GUI
<br>Input: You can specify an input directory containing all the required files
with standardized filenames,
or alternatively you can specific input files manually. 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:
<br>Diffusion weighted data (data.{img,hdr}): A 4-dimensional series of analyze images. This will
include diffusion-weighted volumes and volume(s) with no diffusion weighting.
<br>BET binary brain mask (nodif_brain_mask.{hdr,img}): A single binarised
volume in diffusion space containing ones inside the brain and zeroes outside
the brain.
<br>Output basename: User specifies a basename that will be used to name the
outputs of DTIFit. If the directory input option is used then the basename
will be dtifit (??IS THIS RIGHT)
<br>Gradient directions (bvecs): 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 the input data series.(??in x,y,z, or phase,
read, slice? Do you only include rows for diffusion weighted vols? one entry
per row?)
<br>bvalues (bvals): A text file containing a list of bvalues 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.
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg"> <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"
"http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<h3>DTIFit</h3> <h3>DTIFit</h3>
<b>DTIFit</b> fits a diffusion tensor model at each voxel. You would <b>DTIFit</b> fits a diffusion tensor model at each voxel. You would
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<p><h3>Eddy Current Correction</h3> "http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
<link REL="stylesheet" TYPE="text/css"
href="../fsl.css"><TITLE>FSL</TITLE></HEAD>
<BODY><OBJECT data="fdt_top.html"></OBJECT>
<h3>Eddy Current Correction</h3>
Eddy currents in the gradient coils induce (approximate) stretches and shears Eddy currents in the gradient coils induce (approximate) stretches and shears
in the diffusion weighted images. These distortions are different for in the diffusion weighted images. These distortions are different for
different gradient directions. Eddy Current Correction corrects for these different gradient directions. Eddy Current Correction corrects for these
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<IMG ALIGN=RIGHT hspace=20 vspace=20 SRC="fdt_images/fdt_gui.gif"
ALT="Example GUI view">
<h3>Introduction</h3>
For other information on FDT and updated journal references, see the <a
href="http://www.fmrib.ox.ac.uk/analysis/research/fdt/" target="_top">FDT web
page</a>. If you use FDT in your research, please quote the journal
references listed there.
<p>FDT (FMRIB's Diffusion Toolbox) is a software tool for analysis of diffusion weighted images. FDT is part
of <a href="http://www.fmrib.ox.ac.uk/fsl" target="_top">FSL</a> (FMRIB's Software Library)
. FDT has an easy-to-use graphical user interface (GUI) and its component
programmes can also be run from the command line. FDT includes tools for
data preprocessing, local diffusion modelling and tractography. Each stage in FDT is run
separately.
The main FDT programmes, which are accesible from the GUI are:
<ul><li><a href="fdt_eddy.html">eddycorrect</a> - for correction of eddy current distortion</li>
<li><a href="fdt_bedpost.html">bedpost</a> - for local modelling of diffusion parameters.</li>
<li><a href="fdt_probtrackx.html">probtrack</a> - for tractography and connectivity-based segmentation</li>
<li><a href="fdt_dtifit.html">dtifit</a> - for local fitting of diffusion tensors</li></ul>
<br>The FDT GUI also includes a <a
href="fdt_reg.html">registration</a> option that registers images
using <a href="http://www.fmrib.ox.ac.uk/fsl/flirt/index.html" target="_top">FLIRT</a>.
<p>Additional FDT programmes, that can be run only from the command line, are:
<ul><li><a href="fdt_thresh.html">proj_thresh</a> - for thresholding some outputs
of probtrack</li>
<li><A href="fdt_biggest.html">find_the_biggest</a> - for performing hard
segmentation on the outputs of connectivity-based thresholding in probtrack</li>
<li><A href="fdt_vecreg.html">vecreg</a> - for registering vector data</li></ul>
<p>The probabilistic tractography tools within FDT are very flexible and allow
the user to generate connectivity distributions from single or multiple
voxels; to limit these distributions based on anatomical criteria and to
perform segmentation based on the probability of connection to user-defined
target regions.
<p>To call the FDT GUI, either run <b>Fdt</b> (<b>Fdt_gui</b> on Mac
or Windows), or run <b>fsl</b> and press the <b>FDT</b> button.
<p>For an overview of the local diffusion modelling and tractography used
within FDT see the <a href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/">appendix</a>.
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<HTML><TITLE>FDT - FMRIB's Diffusion Toolbox - User Guide</TITLE><BODY BACKGROUND="fdt_images/fsl-bg.jpg">
<p><h3>An overview of local diffusion modelling carried out in Bedpost</h3>
Its all bollox really - Tim just plucks it out of his arse
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<HTML><HEAD><meta http-equiv="Content-Type"
content="text/html;charset=utf-8">
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<h3>Processing pipeline</h3> <h3>Processing pipeline</h3>
<p>To call the FDT GUI, either run <b>Fdt</b>, or run <b>fsl</b> and press the <b>FDT</b> button. <p>To call the FDT GUI, either run <b>Fdt</b>, or run <b>fsl</b> and press the <b>FDT</b> button.
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<p><h3>PROBTRACK - probabilistic tracking</h3><br>
For details about probabilistic tractography as implemented by FDT,
see <a
href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/">here</a>.
Briefly, FDT repetitively samples from the distributions on
voxel-wise principal diffusion directions, each time comupting a
streamline through these local samples to generate a
<em>probabilistic streamline</em> or a <em>sample</em> from the
distribution on the location of the true streamline. By taking many
such samples FDT is able to build up the posterior distribution on the
streamline location or the <em>connectivity distribution</em>.
<p>After Bedpost has been applied it is possible to run tractography analyses
using ProbTrack.
Probtrack can be run in several different modes:
<ul><li><a href="#path">Path distribution estimation</a></li>
<ul><li><a href="#single">Single seed voxel</a></li>
<li><a href="#seedmask">Seed mask</a></li>
<li><a href="#seedwaypoint">Seed mask and waypoint masks</a></li>
<li><a href="#twomask">Two masks - symmetric </a></li></ul>
<li><a href="#seedclass">Connectivity-based seed classification</a></li>
</ul>
Each mode is explained in detail below.
Every mode requires the user to
specify a bedpost directory. For all modes, the bedpost directory must contain the following 4D images:
<li><b>merged_phsamples</b></li>
<li><b>merged_thsamples</b></li>
<li><b>nodif_brain_mask</b></li>
<p>Results from probtrack can be binned in any available space -e.g.,
diffusion space, structural space or standard space. Note, however,
that tractography itself ALWAYS takes place in diffusion space - it is
simply the <em>results</em> of probtrack that are stored in the
required space. If probtrack results are to be stored in a space
other than diffusion space then you will need transformations from
this space back into the space of thediffusion data. The <a
ref="fdt_registration.html">FDT registration tab</a> creates the
following transformations in the <code>xfms</code> subdirectory of the
bedpost directory.
<p>for running analyses in structural space:
<li><b>xfms/str2diff.mat</b></li>
<li><b>xfms/diff2str.mat</b></li>
<p>for running analyses in standard space:
<li><b>xfms/standard2diff.mat</b></li>
<li><b>xfms/diff2standard.mat</b></li>
<hr>
<a name="path"></a>
<h3>Path Distribution Estimation - basics</h3>
The first four modes of ProbTrack involve generating connectivity
distributions from user-specified seed voxel(s). The output will be a single
image in the space of the specified seed like <a href="fdt_images/fdt_simple_tract3.gif">this</a>. All
brain voxels will have a value (though many of these will be zero)
representing the connectivity value between that voxel and the seed voxel
(i.e., the number of samples that pass through that voxel). Note that when
connectivity distributions are generated from multiple seed voxels within a
region of interest then the time required for the analysis to run will be
approximately the number of seed voxels multiplied by the time taken to
generate a distribution from a single voxel.
<p><h4>Setting up the GUI for Path Distribution Estimation - settings common
to all modes:</h4>
<p><b>Seeds space:</b> specification of seeds is different for each mode - see below.
<p>If <b>seed space is not diffusion</b>, then check this button. If
you are in <b>Single seed voxel</b> mode use the browse buttons to
locate a reference image (e.g., subject1.bedpost/struct.hdr if seed
space is structral space or subject1.bedpost/standard.hdr if seed
space is standard space). Next set the transformation matrix from seed
space to diffusion space (e.g., subject1.bedpost/xfms/str2diff.mat if
seed space is structural space or
subject1.bedpost/xfms/standard2diff.mat if seed space is standard
space). Note that, in all cases, the smaller the voxel size in your
seed space image, the lower will be the resulting connectivity values
to these voxels (This is intuitive - the smaller a voxel is, the less
chance that the true streamline will pass through it!). This
highlights the problem with binning a continuous distribution into
meaningless discrete bins. In order for the probability values to be
truly meaningful, the dicrete bins chosen should be anatomically
meaningful, as is the case in <a href="#seedclass">Connectivity-based
seed classification</a>.
<p>If an <b>exclusion mask</b> is to be used then check the box and
use the browse button to locate the mask file. This must be a
binarised analyze file in seed space. Pathways will be terminated if
they enter the exclusion mask. For example, an exclusion mask of the
midline will terminate pathways that cross into the other
hemisphere. (Note that paths are always terminated when they reach the
brain surface as defined by nodif_brain_mask)
<br>Use the browse button to specify an <b>output</b> name. This will
be a filename or a directory name depending on the mode.
<hr>
<h3>Path Distribution Estimation - available modes and mode-specific settings:</h3>
<ul><li><a href="#single">Single seed voxel</a></li>
<li><a href="#seedmask">Seed mask</a></li>
<li><a href="#seedwaypoint">Seed mask and waypoint masks</a></li>
<li><a href="#twomask">Two masks - symmetric </a></li></ul>
<a name="single"></a>
<h3>Single Seed Voxel:</h3>
<IMG ALIGN=RIGHT height=100 SRC="fdt_images/fdt_simple_tract3.gif"
ALT="simple tract">
Generates a connectivity distribution from a single,
user-specified voxel
<p>Gui Options: <br><b>Seeds space:</b>
Enter the x,y,z co-ordinates of a single seed voxel. Use the buttons
to the right to specify whether the co-ordinates are given in voxels
or millimetres. Note if the "seed space is not diffusion" is set, and
the seed space reference image is the MNI152 average brain, then mm
coordinates will have their origin at the AC.
<p>The output will be a single image in the space of the specified seed. All
brain voxels will have a value (though many of these will be zero)
representing the connectivity value between that voxel and the seed voxel
(i.e., the number of samples that pass through that voxel). The example on
the right shows the connectivity distribution from a single seed in the
internal capsule overlaid on an FA image.
<a name="seedmask"></a>
<h3>Seed Mask Mode:</h3>
Generates a connectivity distribution from a user-specified
region of interest.
<p>Gui Options: <br><b>Seed image:</b>
Use the browse button to locate the seed image - this should be a
binary mask. Probabilistic tractography will be run from every voxel
with a value greater than 0 in this mask.
<p>The output directory will contain: <br>
<b>probtrack.log</b> - a text record of the command that was run.<br>
<b>fdt.log</b> - a log of the setup of the FDT GUI when the analysis was run.
To recover this GUI setup, type <code>Fdt fdt.log</code>
<br>The output file - will be a
single image in the space of the specified seed mask. All brain voxels
will have a value (though many of these may be zero) representing the
number of samples that pass through that voxel from the seed mask.
Connectivity distributions from multiple seed voxels are summed to
produce this output. Therefore the connectivity values will depend on
the number of voxels in the seed mask.
<a name="seedwaypoint"></a> <h3>Seed Mask and Waypoint Masks</h3> <IMG
ALIGN=RIGHT height=200 SRC="fdt_images/fdt_twomasks_tracts.gif"
ALT="constraining tracts"> Generates a connectivity distribution from
voxels in the seed mask and retains only those paths that pass through
all of the waypoint masks. The example on the right shows the outputs
from two different analyses which use the same seed mask (orange) but
different waypoint masks (red).
<p>GUI Options:
<br><b>Seed Image:</b> Use the browse button to locate the binary seed mask.
<br><b>Waypoint Masks:</b> Use the add and remove buttons to make a
list of waypoint masks. These must be in the same space as the seed image.
<BR>
<br>The output directory will contain:<br>
<b>probtrack.log</b> - a text record of the command that was run.<br>
<b>fdt.log</b> - a log of the setup of the FDT GUI when the analysis
was run. To recover this GUI setup, type <code>Fdt fdt.log</code>
<br>The output file - will be a single image in the space of the
specified seed mask. All brain voxels will have a value (though many
of these may be zero) representing the number of samples that pass
through that voxel starting the seed mask and which have also passed through all of
the waypoint masks. Connectivity distributions from multiple seed
voxels are summed to produce this output. Therefore the connectivity
values will depend on the number of voxels in the seed mask.
<a name="twomask"></a>
<h3>Two Masks - symmetric</h3>
Generates a connectivity distribution from all
voxels in <b>mask image 1</b> and retains only those pathways that pass
through <b>mask image 2</b>. Also
generates a connectivity distribution from all voxels in <b>mask image 2</b> and retains
pathways that pass through <b>mask image 1</b>. Ouput is the sum of these connectivity
distributions.
<p>GUI Options:
<br><b>Mask image 1</b> and <b>Mask image 2</b>: Use the browse buttons to locate your binary
mask of area one and area two. These must be in the same space.
<p>The output directory will contain:
<br>
<b>probtrack.log</b> - a text record of the command that was run.<br>
<b>fdt.log</b> - a log of the setup of the FDT GUI when the analysis
was run. To recover this GUI setup, type <code>Fdt fdt.log</code>
<br>The output file - will be a single image in the space of the
specified masks. All brain voxels will have a value (though many of
these may be zero) representing the number of samples that pass
through that voxel from either of the seed masks and which also pass
through the other seedmask. Connectivity distributions from multiple
seed voxels are summed to produce this output. Therefore the
connectivity values will depend on the number of voxels in the seed
masks.
<hr>
<a name="seedclass"></a>
<h3>Connectivity-based seed classification</h3>
<IMG ALIGN=RIGHT height=150 SRC="fdt_images/fdt_seeds2targets_quant_eg.gif"
ALT="connectivity-based classification of thalamus">
This mode quantifies connectivity values
between a seed mask and any number of user-specified target masks. In the
example on the right, seed voxels in the thalamus are classified according to
the probabilty of connection to different cortical target masks.
<p>Setting up the GUI
<br><b>Seed image</b>: use the browse button to locate your binary mask of seed voxels.
<br><b>Target list</b>: Use the add button to locate each target mask. Targets must be
binary masks in the same space as the seed mask. When all targets are loaded you
can press the save list button to save the list of targets as a text file. If
you already have a text file list of required targets (including their path)
then you can load it with the load list button.
<p>
<br>The output directory will contatin:
<IMG ALIGN=RIGHT height=150 SRC="fdt_images/fdt_seeds2targets_thal.gif"
ALT="connectivity-based classification of thalamus">
<br><b>probtrack.log</b> - a text record of the command that was run.<br>
<b>fdt.log</b> - a log of the setup of the FDT GUI when the analysis
was run. To recover this GUI setup, type <code>Fdt fdt.log</code>
<br>A single volume for each target mask, named <b>seeds_to_{target}</b> where
{target} is replaced by the file name of the relevant target mask. In these
output images, the value of each voxel within
the seed mask is the number of samples seeded from that voxel reaching the relevant
target mask. The value of all voxels outside the seed mask will be zero.
<p>
There are command line utilities that can be run on the outputs of
<b>Connectivity-based seed classification</b>:
<ul><li><a href="fdt_thresh.html">proj_thresh</a> - for thresholding some outputs
of ProbTrack</li>
<li><A href="fdt_biggest.html">find_the_bigggest</a> - for performing hard
segmentation on the outputs of connectivity-based thersholding in ProbTrack,
see example on the right</li></ul>
<hr>
<a name="options"></a>
<h3>Options Tab </h3>
For all modes in probtrack, the user is able to change the setting of certain
parameters by clicking the <b>options</b> tab.
<p><b>Number of samples</b> (default 5000): This determines the number of individual
pathways (or samples) that are drawn through the probability distributions on
principle fibre direction (see <a href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/"> appendix </a>for more details on the modelling and
tractography methods). By default this is set to 5000 as we are confident
that convergence is reached with this number of samples. However, reducing
this number will speed up processing and can be useful for preliminary or
exploratory analyses.
<p><b>Curvature Threshold</b> (default 0.2): We limit how sharply pathways can turn
in order to
exclude implausible pathways. This number is the cosine of the minimum allowable
angle between two steps. By default this is set to 0.2 (corresponding to a
minimum angle of approximately &#177;80 degrees). Adjusting this number can enable
pathways with sharper angles to be detected.
<p><b>Verbose:</b> If this option is selected then FDT prints additional logging
information to screen while it is running.
<p><b>Loopcheck:</b> By default, we terminate pathways that loop back on themselves
-i.e paths that travel to a point where they have already been.
<p><h4>Advanced options:</h4>
<p><b>Use modified Euler streamlining:</b> Use modified Euler
integration as opposed to simple Euler for computing probabilistic
streamlines. More accurate but slower.
<p><b>Maximum number of steps</b> (default 2000): By default, samples are terminated
when they have travelled 2000 steps. Using a step length of 0.5mm this
corresponds to a distance of 1m. These values can be adjusted if required.
<p><b>Step length</b> (default 0.5mm): This determines the length of each step. This
setting may be adjusted from default e.g., depending on the voxel size being
used, or if tracking is being performed on different sized brains
(e.g., infants or animals).
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<p><h3>PROBTRACKX - probabilistic tracking</h3><br> <HTML><HEAD><meta http-equiv="Content-Type"
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<h3>PROBTRACKX - probabilistic tracking</h3><br>
For details about probabilistic tractography as implemented by FDT, For details about probabilistic tractography as implemented by FDT,
see <a see <a
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<h3>Registration within FDT</h3> <h3>Registration within FDT</h3>
<p>If tractography results are to be stored in any space other than diffusion <p>If tractography results are to be stored in any space other than diffusion
space then registration must be run. space then registration must be run.
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<h3>proj_thresh</h3> <h3>proj_thresh</h3>
<b>proj_thresh</b> is a command line utility that provides an alternative way of <b>proj_thresh</b> is a command line utility that provides an alternative way of
expressing connection probability in connectivity-based segmentation. It is expressing connection probability in connectivity-based segmentation. It is
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User Guide - FDT version 2.0 <TD ALIGN=CENTER WIDTH="100%">
<TABLE BORDER=0>
<tr><td align=center><font size=+3><b>FMRIB's Diffusion Toolbox - FDT v2.0</b></font></tr>
<tr valign=bottom><td align=center>
<a href="index.html" target="_top">intro</a> &nbsp;-&nbsp;
<a href="fdt_pipeline.html" target="_top">processing pipeline</a> &nbsp;-&nbsp;
<a href="fdt_eddy.html" target="_top">eddy current correction</a> &nbsp;-&nbsp;
<a href="fdt_dtifit.html" target="_top">dtifit</a> <br>
<a href="fdt_bedpost.html" target="_top">bedpost</a> &nbsp;-&nbsp;
<a href="fdt_bedpost_parallel.html" target="_top">parallelising bedpost</a> &nbsp;-&nbsp;
<a href="fdt_reg.html" target="_top">registration</a> &nbsp;-&nbsp;
<a href="fdt_probtrackx.html" target="_top">probtrackx</a> <br>
<a href="fdt_thresh.html" target="_top">proj_thresh</a> &nbsp;-&nbsp;
<a href="fdt_biggest.html" target="_top">find_the_biggest</a> &nbsp;-&nbsp;
<a href="fdt_display.html" target="_top">using fslview</a> &nbsp;-&nbsp;
<a href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/" target="_top">FDT theory</a>
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<a href="http://www.fmrib.ox.ac.uk/fsl" target="_top">
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<option>Registration</option>
<option>Probtrackx</option>
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<h3>Registration of vector images using vecreg</h3> <h3>Registration of vector images using vecreg</h3>
<p> <p>
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<frameset rows="200,*,60" frameborder="no" framespacing="0" border="0"> <IMG ALIGN=RIGHT hspace=20 vspace=20 SRC="fdt_images/fdt_gui.gif" ALT="Example GUI view">
<frame src="fdt_top.html" marginwidth="10" marginheight="10" frameborder="no"> <h3>Introduction</h3>
<frame name="dynamic" src="fdt_intro.html" marginwidth="10" marginheight="10" frameborder="no"> For other information on FDT and updated journal references, see the <a
<frame src="fdt_bottom.html" marginwidth="10" marginheight="10" frameborder="no"> href="http://www.fmrib.ox.ac.uk/analysis/research/fdt/" target="_top">FDT web
</frameset> page</a>. If you use FDT in your research, please quote the journal
references listed there.
<p>FDT (FMRIB's Diffusion Toolbox) is a software tool for analysis of diffusion weighted images. FDT is part
of <a href="http://www.fmrib.ox.ac.uk/fsl" target="_top">FSL</a> (FMRIB's Software Library)
. FDT has an easy-to-use graphical user interface (GUI) and its component
programmes can also be run from the command line. FDT includes tools for
data preprocessing, local diffusion modelling and tractography. Each stage in FDT is run
separately.
The main FDT programmes, which are accesible from the GUI are:
<ul><li><a href="fdt_eddy.html">eddycorrect</a> - for correction of eddy current distortion</li>
<li><a href="fdt_bedpost.html">bedpost</a> - for local modelling of diffusion parameters.</li>
<li><a href="fdt_probtrackx.html">probtrack</a> - for tractography and connectivity-based segmentation</li>
<li><a href="fdt_dtifit.html">dtifit</a> - for local fitting of diffusion tensors</li></ul>
<br>The FDT GUI also includes a <a
href="fdt_reg.html">registration</a> option that registers images
using <a href="http://www.fmrib.ox.ac.uk/fsl/flirt/index.html" target="_top">FLIRT</a>.
<p>Additional FDT programmes, that can be run only from the command line, are:
<ul><li><a href="fdt_thresh.html">proj_thresh</a> - for thresholding some outputs
of probtrack</li>
<li><A href="fdt_biggest.html">find_the_biggest</a> - for performing hard
segmentation on the outputs of connectivity-based thresholding in probtrack</li>
<li><A href="fdt_vecreg.html">vecreg</a> - for registering vector data</li></ul>
<p>The probabilistic tractography tools within FDT are very flexible and allow
the user to generate connectivity distributions from single or multiple
voxels; to limit these distributions based on anatomical criteria and to
perform segmentation based on the probability of connection to user-defined
target regions.
<p>To call the FDT GUI, either run <b>Fdt</b> (<b>Fdt_gui</b> on Mac
or Windows), or run <b>fsl</b> and press the <b>FDT</b> button.
<p>For an overview of the local diffusion modelling and tractography used
within FDT see the <a href="http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/">appendix</a>.
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