Module Detail Information

Name:Bedpostx
Type: Module
Short URL:
http://bit.ly/1B7Ekkd
Description:BEDPOSTX stands for Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques. The X stands for modelling Crossing Fibres. bedpostx 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 bedpostx see this technical report. bedpostx allows to model crossing fibres within each voxel of the brain. Crucially, bedpostx allows to automatically determine the number of crossing fibres per voxel. For details on the model used in this case, see Behrens et al, NeuroImage 2007. bedpostx takes about 15 hours to run but will automatically batch if run on an SGE-capable system. Note that bedpostx is a wrapper script for a command-line tool called xfibres. Input directory: Use the browse button to select an input directory. That directory must contain the following files: data: A 4D series of data volumes. This will include diffusion-weighted volumes and volume(s) with no diffusion weighting. nodif_brain_mask: 3D binary brain mask volume derived from running bet on nodif (i.e. on a volume with no diffusion weighting). bvecs (with no file extension): An ASCII 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 data. The format is x_1 x_2 x_3 ... x_n y_1 y_2 y_3 ... y_n z_1 z_2 z_3 ... z_n Vectors are normalised to unit length within the bedpostx code. For volumes in which there was no diffusion weighting, the entry should still be present, although the direction of the vector does not matter! bvals (with no file extension): An ASCII 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. The format is b_1 b_2 b_3 ... b_n The order of bvals must match the order of data. Tip: Run bedpostx_datacheck in command line to check if your input directory contains the correct files required for bedpostx. Outputs of BEDPOSTX bedpostx creates a new directory at the same level as the input directory called .bedpostX which contains all the files you need for probabilistic tractography. Highlights are ( indicates the i-th fibre. It ranges from 1 to the maximum number of fibres set in the advanced options.): merged_thsamples - 4D volume - Samples from the distribution on theta merged_phsamples - 4D volume - Samples from the distribution on phi theta and phi together represent the principal diffusion direction in spherical polar co-ordinates merged_fsamples - 4D volume - Samples from the distribution on anisotropic volume fraction (see technical report). mean_thsamples - 3D Volume - Mean of distribution on theta mean_phsamples - 3D Volume - Mean of distribution on phi mean_fsamples - 3D Volume - Mean of distribution on f anisotropy Note that in each voxel, fibres are ordered according to a decreasing mean f-value mean_dsamples - 3D Volume - Mean of distribution on diffusivity d mean_S0samples - 3D Volume - Mean of distribution on T2w baseline signal intensity S0 dyads - Mean of PDD distribution in vector form. Note that this file can be loaded into fslview for easy viewing of diffusion directions dyads_dispersion - 3D Volume - Uncertainty on the estimated fibre orientation. Characterizes how wide the orientation distribution is around the respective PDD.(how is this calculated?) nodif_brain_mask - binary mask created from nodif_brain - copied from input directory
Executable:
/bedpostx
Input Parameters:
 - subject directory
 - Fibres
 - ARD weight
 - burn in period
 - jumps
 - sample
 - model
Output Parameters:
File size:20.87 KB
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