FSL

Bedpost

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 appendix.

Bedpost now allows to model crossing fibres within each voxel on the brain. Crucially, Bedpost only models crossing fibres in voxels where the data support crossing fibres. For details on the model used in this case, see Behrens et al, NeuroImage 2007, 34:144-55.

Bedpost takes about 24 hours to run but can easily be parallelised if multiple processors are available.

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 Bedpost.

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: 3D volume with no diffusion weighting
nodif_brain_mask: 3D binary brain mask volume derived from running bet on nodif
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 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
```
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 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.
The format is
```
b_1 b_2 b_3 ... b_n
```
The order of bvals must match the order of data.

Outputs of Bedpost

Bedpost creates a new directory at the same level as the input directory called <indir>.bedpost 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 - 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
dyads - Mean of PDD distribution in vector form. Note that this file can be loaded into fslview for easy viewing of diffusion directions
nodif - Single volume with no diffusion weighting - copied from input directory
nodif_brain - brain extracted version of nodif - copied from input directory
nodif_brain_mask - binary mask created from nodif_brain - copied from input directory

Advanced options

You may change some options before running Bedpost, 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).

Fibres: Number of fibres modelled per voxel.
Weight: Multiplicative factor for the prior on the additional modelled fibres. A smaller factor means more weighting for the additional fibres.
Burnin: Number of iterations before starting the sampling. These might be increased if the data are noisy, and the MCMC needs more iterations to converge.

command line utility

bedpost <indir> [-f <nfibres> -w <weight> -b <niter>]