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 the bedpostx now allows to model crossing fibres within each voxel on 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, 34:144-55.
bedpostx takes about 24 hours to run but will automatically batch if run on an SGE-capable system..
To call the FDT GUI, either run Fdt, or run fsl and press the FDT button. Use the top left drop down menu to select BEDPOSTX.
Input directory: Use the browse button to select an input directory. That directory must contain the following files:
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!
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 <indir>.bedpostX which contains all the files you need for probabilistic
tractography. Highlights are (<i> indicates the i-th fibre. It ranges from 1 to the maximum number of fibres set in the advanced options.):
Advanced Options
You may change some options before running bedpostx, depending on the questions you want to ask or the quality of your diffusion data. The default values of these parameters are the ones used in the corresponding paper (Behrens et al, NeuroImage 2007, 34:144-55).
Command line utility