Multi-tissue informed log-domain intensity normalisation
mtnormalise [ options ] input output [ input output ... ]
- input output: list of all input and output tissue compartment files (see example usage).
This command takes as input any number of tissue components (e.g. from multi-tissue CSD) and outputs corresponding normalised tissue components corrected for the effects of (residual) intensity inhomogeneities. Intensity normalisation is performed by optimising the voxel-wise sum of all tissue compartments towards a constant value, under constraints of spatial smoothness (polynomial basis of a given order). Different to the Raffelt et al. 2017 abstract, this algorithm performs this task in the log-domain instead, with added gradual outlier rejection, different handling of the balancing factors between tissue compartments and a different iteration structure.
The -mask option is mandatory and is optimally provided with a brain mask (such as the one obtained from dwi2mask earlier in the processing pipeline). Outlier areas with exceptionally low or high combined tissue contributions are accounted for and reoptimised as the intensity inhomogeneity estimation becomes more accurate.
Default usage (for 3-tissue CSD compartments):
$ mtnormalise wmfod.mif wmfod_norm.mif gm.mif gm_norm.mif csf.mif csf_norm.mif -mask mask.mif
Note how for each tissue compartment, the input and output images are provided as a consecutive pair.
- -mask image the mask defines the data used to compute the intensity normalisation. This option is mandatory.
- -order number the maximum order of the polynomial basis used to fit the normalisation field in the log-domain. An order of 0 is equivalent to not allowing spatial variance of the intensity normalisation factor. (default: 3)
- -niter number set the number of iterations. The first (and potentially only) entry applies to the main loop. If supplied as a comma-separated list of integers, the second entry applies to the inner loop to update the balance factors (default: 15,7).
- -reference number specify the (positive) reference value to which the summed tissue compartments will be normalised. (default: 0.282095, SH DC term for unit angular integral)
- -balanced incorporate the per-tissue balancing factors into scaling of the output images (NOTE: use of this option has critical consequences for AFD intensity normalisation; should not be used unless these consequences are fully understood)
- -check_norm image output the final estimated spatially varying intensity level that is used for normalisation.
- -check_mask image output the final mask used to compute the normalisation. This mask excludes regions identified as outliers by the optimisation process.
- -check_factors file output the tissue balance factors computed during normalisation.
- -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.
Raffelt, D.; Dhollander, T.; Tournier, J.-D.; Tabbara, R.; Smith, R. E.; Pierre, E. & Connelly, A. Bias Field Correction and Intensity Normalisation for Quantitative Analysis of Apparent Fibre Density. In Proc. ISMRM, 2017, 26, 3541
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
Author: Thijs Dhollander (email@example.com), Rami Tabbara (firstname.lastname@example.org), David Raffelt (email@example.com), Jonas Rosnarho-Tornstrand (firstname.lastname@example.org) and J-Donald Tournier (email@example.com)
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