FSL

### DTIFIT

DTIFIT 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 BEDPOSTX not DTIFIT).

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 DTIFIT.

Input: You can specify an input directory containing all the required files with standardized filenames, or alternatively you can specify input files manually by turning on the specify input files manually 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 filename. Required files are:

- Diffusion weighted data (data): A 4D series of data volumes. This will include diffusion-weighted volumes and volume(s) with no diffusion weighting.
- BET binary brain mask (nodif_brain_mask): A single binarised volume in diffusion space containing ones inside the brain and zeroes outside the brain.
- 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 dti
- Gradient directions (bvecs): 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 the input data series.
  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 dtifit 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!
- b values (bvals): An ASCII text file containing a list of b values 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

#### Outputs of dtifit
- <basename>_V1 - 1st eigenvector
- <basename>_V2 - 2nd eigenvector
- <basename>_V3 - 3rd eigenvector
- <basename>_L1 - 1st eigenvalue
- <basename>_L2 - 2nd eigenvalue
- <basename>_L3 - 3rd eigenvalue
- <basename>_MD - mean diffusivity
- <basename>_FA - fractional anisotropy
- <basename>_MO - mode of the anisotropy (oblate ~ -1; isotropic ~ 0; prolate ~ 1)
- <basename>_S0 - raw T2 signal with no diffusion weighting

V1 Lines | V1 RGB | L1 | FA

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command line utility

    dtifit

    Compulsory arguments (You MUST set one or more of):
    	-k,--data	dti data file
    	-o,--out	Output basename
    	-m,--mask	Bet binary mask file
    	-r,--bvecs	b vectors file
    	-b,--bvals	b values file

    Optional arguments (You may optionally specify one or more of):
    	-V,--verbose	switch on diagnostic messages
    	-h,--help	display this message
    	--cni	        Input counfound regressors
    	--sse	        Output sum of squared errors
    	--littlebit	Only process small area of brain
    	--save_tensor	Save the elements of the tensor
    	-z,--zmin	min z
    	-Z,--zmax	max z
    	-y,--ymin	min y
    	-Y,--ymax	max y
    	-x,--xmin	min x
    	-X,--xmax	max x