usage: __main__.py [-h] --in_dwis IN_DWIS [IN_DWIS ...] --in_bvals IN_BVALS
[IN_BVALS ...] --in_bvecs IN_BVECS [IN_BVECS ...]
--in_bdeltas {0,1,-0.5,0.5} [{0,1,-0.5,0.5} ...]
[--mask MASK] [--tolerance tol] [--skip_b0_check]
[--fit_iters FIT_ITERS] [--random_iters RANDOM_ITERS]
[--do_weight_bvals] [--do_weight_pa] [--do_multiple_s0]
[--op OP] [--fa FA] [--processes NBR]
[-v [{DEBUG,INFO,WARNING}]] [-f] [--not_all] [--md file]
[--ufa file] [--mk_i file] [--mk_a file] [--mk_t file]
Script to compute microstructure metrics using the DIVIDE method. In order to
operate, the script needs at leats two different types of b-tensor encodings.
Note that custom encodings are not yet supported, so that only the linear
tensor encoding (LTE, b_delta = 1), the planar tensor encoding
(PTE, b_delta = -0.5), the spherical tensor encoding (STE, b_delta = 0) and
the cigar shape tensor encoding (b_delta = 0.5) are available. Moreover, all
of `--in_dwis`, `--in_bvals`, `--in_bvecs` and `--in_bdeltas` must have the
same number of arguments. Be sure to keep the same order of encodings
throughout all these inputs and to set `--in_bdeltas` accordingly (IMPORTANT).
By default, will output all possible files, using default names. Thus, this
script outputs the results from the DIVIDE fit or direct derivatives:
mean diffusivity (MD), isotropic mean kurtosis (mk_i), anisotropic mean
kurtosis (mk_a), total mean kurtosis (mk_t) and finally micro-FA (uFA).
Specific names can be specified using the
file flags specified in the "File flags" section.
If --not_all is set, only the files specified explicitly by the flags
will be output. The order parameter can also be computed from the uFA and a
precomputed FA, using separate input parameters.
>>> scil_btensor_metrics.py --in_dwis LTE.nii.gz PTE.nii.gz STE.nii.gz
--in_bvals LTE.bval PTE.bval STE.bval --in_bvecs LTE.bvec PTE.bvec STE.bvec
--in_bdeltas 1 -0.5 0 --mask mask.nii.gz
IMPORTANT: If the script does not converge to a solution, it is probably due to
noise outside the brain. Thus, it is strongly recommanded to provide a brain
mask with --mask.
Based on Markus Nilsson, Filip Szczepankiewicz, Björn Lampinen, André Ahlgren,
João P. de Almeida Martins, Samo Lasic, Carl-Fredrik Westin,
and Daniel Topgaard. An open-source framework for analysis of multidimensional
diffusion MRI data implemented in MATLAB.
Proc. Intl. Soc. Mag. Reson. Med. (26), Paris, France, 2018.
Formerly: scil_compute_divide.py
options:
-h, --help show this help message and exit
--in_dwis IN_DWIS [IN_DWIS ...]
Path to the input diffusion volume for each b-tensor encoding type.
--in_bvals IN_BVALS [IN_BVALS ...]
Path to the bval file, in FSL format, for each b-tensor encoding type.
--in_bvecs IN_BVECS [IN_BVECS ...]
Path to the bvec file, in FSL format, for each b-tensor encoding type.
--in_bdeltas {0,1,-0.5,0.5} [{0,1,-0.5,0.5} ...]
Value of b_delta for each b-tensor encoding type, in the same order as dwi, bval and bvec inputs.
--mask MASK Path to a binary mask. Only the data inside the mask will be used for computations and reconstruction.
--tolerance tol The tolerated gap between the b-values to extract and the current b-value.
[Default: 20]
* Note. We would expect to find at least one b-value in the
range [0, tolerance]. To skip this check, use --skip_b0_check.
--skip_b0_check By default, we supervise that at least one b0 exists in your data
(i.e. b-values below the default --tolerance). Use this option to
allow continuing even if the minimum b-value is suspiciously high.
If no b-value is found below the threshold, the script will continue
with the original --tolerance and no b0 volumes.
Use with care, and only if you understand your data.
--fit_iters FIT_ITERS
The number of time the gamma fit will be done [1]
--random_iters RANDOM_ITERS
The number of iterations for the initial parameters search. [50]
--do_weight_bvals If set, does not do a weighting on the bvalues in the gamma fit.
--do_weight_pa If set, does not do a powder averaging weighting in the gamma fit.
--do_multiple_s0 If set, does not take into account multiple baseline signals.
--processes NBR Number of sub-processes to start.
Default: [1]
-v [{DEBUG,INFO,WARNING}]
Produces verbose output depending on the provided level.
Default level is warning, default when using -v is info.
-f Force overwriting of the output files.
--not_all If set, only saves the files specified using the file flags. (Default: False)
Order parameter (OP):
--op OP Output filename for the order parameter. The OP will not be output if this is not given. Computation of the OP also requires a precomputed FA map (given using --fa).
--fa FA Path to a FA map. Needed for calculating the OP.
File flags:
--md file Output filename for the MD.
--ufa file Output filename for the microscopic FA.
--mk_i file Output filename for the isotropic mean kurtosis.
--mk_a file Output filename for the anisotropic mean kurtosis.
--mk_t file Output filename for the total mean kurtosis.
Scilpy version: 2.0.2