Estimate fibre orientation distributions from diffusion data using spherical deconvolution
dwi2fod [ options ] algorithm dwi response odf [ response odf ... ]
- algorithm: the algorithm to use for FOD estimation. (options are: csd,msmt_csd)
- dwi: the input diffusion-weighted image
- response odf: pairs of input tissue response and output ODF images
The spherical harmonic coefficients are stored according the conventions described the main documentation, which can be found at the following link:
Perform single-shell single-tissue CSD:
$ dwi2fod csd dwi.mif response_wm.txt wmfod.mif
This algorithm is designed for single-shell data and only uses a single b-value. The response function text file provided should only contain a a single row, corresponding to the b-value used for CSD.
Perform multi-shell multi-tissue CSD:
$ dwi2fod msmt_csd dwi.mif response_wm.txt wmfod.mif response_gm.txt gm.mif response_csf.txt csf.mif
This example is the most common use case of multi-tissue CSD, estimating a white matter FOD, and grey matter and CSF compartments. This algorithm requires at least three unique b-values to estimate three tissue compartments. Each response function text file should have a number of rows equal to the number of b-values used. If only two unique b-values are available, it’s also possible to estimate only two tissue compartments, e.g., white matter and CSF.
DW gradient table import options¶
- -grad file Provide the diffusion-weighted gradient scheme used in the acquisition in a text file. This should be supplied as a 4xN text file with each line is in the format [ X Y Z b ], where [ X Y Z ] describe the direction of the applied gradient, and b gives the b-value in units of s/mm^2. If a diffusion gradient scheme is present in the input image header, the data provided with this option will be instead used.
- -fslgrad bvecs bvals Provide the diffusion-weighted gradient scheme used in the acquisition in FSL bvecs/bvals format files. If a diffusion gradient scheme is present in the input image header, the data provided with this option will be instead used.
DW shell selection options¶
- -shells b-values specify one or more b-values to use during processing, as a comma-separated list of the desired approximate b-values (b-values are clustered to allow for small deviations). Note that some commands are incompatible with multiple b-values, and will report an error if more than one b-value is provided.
WARNING: note that, even though the b=0 volumes are never referred to as shells in the literature, they still have to be explicitly included in the list of b-values as provided to the -shell option! Several algorithms which include the b=0 volumes in their computations may otherwise return an undesired result.
Options common to more than one algorithm¶
- -directions file specify the directions over which to apply the non-negativity constraint (by default, the built-in 300 direction set is used). These should be supplied as a text file containing [ az el ] pairs for the directions.
- -lmax order the maximum spherical harmonic order for the output FOD(s).For algorithms with multiple outputs, this should be provided as a comma-separated list of integers, one for each output image; for single-output algorithms, only a single integer should be provided. If omitted, the command will use the lmax of the corresponding response function (i.e based on its number of coefficients), up to a maximum of 8.
- -mask image only perform computation within the specified binary brain mask image.
Options for the Constrained Spherical Deconvolution algorithm¶
- -filter spec the linear frequency filtering parameters used for the initial linear spherical deconvolution step (default = [ 1 1 1 0 0 ]). These should be supplied as a text file containing the filtering coefficients for each even harmonic order.
- -neg_lambda value the regularisation parameter lambda that controls the strength of the non-negativity constraint (default = 1).
- -norm_lambda value the regularisation parameter lambda that controls the strength of the constraint on the norm of the solution (default = 1).
- -threshold value the threshold below which the amplitude of the FOD is assumed to be zero, expressed as an absolute amplitude (default = 0).
- -niter number the maximum number of iterations to perform for each voxel (default = 50). Use ‘-niter 0’ for a linear unconstrained spherical deconvolution.
Options for the Multi-Shell, Multi-Tissue Constrained Spherical Deconvolution algorithm¶
- -norm_lambda value the regularisation parameter lambda that controls the strength of the constraint on the norm of the solution (default = 1e-10).
- -neg_lambda value the regularisation parameter lambda that controls the strength of the non-negativity constraint (default = 1e-10).
- -predicted_signal image output the predicted dwi image.
- -strides spec specify the strides of the output data in memory; either as a comma-separated list of (signed) integers, or as a template image from which the strides shall be extracted and used. The actual strides produced will depend on whether the output image format can support it.
- -info display information messages.
- -quiet do not display information messages or progress status; alternatively, this can be achieved by setting the MRTRIX_QUIET environment variable to a non-empty string.
- -debug display debugging messages.
- -force force overwrite of output files (caution: using the same file as input and output might cause unexpected behaviour).
- -nthreads number use this number of threads in multi-threaded applications (set to 0 to disable multi-threading).
- -config key value (multiple uses permitted) temporarily set the value of an MRtrix config file entry.
- -help display this information page and exit.
- -version display version information and exit.
- If using csd algorithm:
Tournier, J.-D.; Calamante, F. & Connelly, A. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 2007, 35, 1459-1472
- If using msmt_csd algorithm:
Jeurissen, B; Tournier, J-D; Dhollander, T; Connelly, A & Sijbers, J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. NeuroImage, 2014, 103, 411-426
Tournier, J.-D.; Calamante, F., Gadian, D.G. & Connelly, A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004, 23, 1176-1185
Tournier, J.-D.; Smith, R. E.; Raffelt, D.; Tabbara, R.; Dhollander, T.; Pietsch, M.; Christiaens, D.; Jeurissen, B.; Yeh, C.-H. & Connelly, A. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 2019, 202, 116137
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