This code allows inferring multi-channel bias fields from, e.g., uncombined multi-coil MR images.
This code is written in Matlab and depends on the SPM12 software, which should be on your Matlab path.
If you have access to the development version of SPM (e.g., if you work
at the FIL), it is advised to
recompile SPM with OpenMP activated. This can be done by following
these instructions, except that the
option USE_OPENMP=1 must be specified. For example, on linux:
cd /home/login/spm12/src
make distclean
make USE_OPENMP=1 && make install
make external-distclean
make external && make external-install
Assuming that you have a series of uncombined coil images in nifti format, and that you like graphical interfaces, the most simple usage is:
>> multibias;
You will first be asked to select all input files, then to choose an output directory. The algorithm will then run and write the results on disk. There will be as many bias field images as channels (prefixed by 'b') and one mean image (prefixed by 'm').
If more flexibility is required, multibias can be used as a function:
>> [b,m] = multibias(x, opt);
The input x can be either:
- A 5D array: the fourth dimension should correspond to channels (and therefore to bias fields), while the fifth dimension should correspond to different contrasts. Note that in this model, bias fields are shared across contrasts and differ across channels.
- A 2d cell array of filenames. Here the first dimension corresponds to channels and the second to contrasts. Each file should contain a 3D volume. A 5D array is built from this combination of 3D files.
- Empty. In this case, files can be selected through the GUI.
The output are
bis a 4D array of estimated bias field. The fourth dimension
corresponds to channels.mis a 5D array of combined 'mean' images. The fourth dimension corresponds to contrasts, while the fourth dimension is of size 1.- If no output argument is asked (
>> multibias(x, opt);), an output folder is asked for through the GUI and the output bias and mean images are writen on disk.
Several options are available, as fields of the opt structure:
| Name | Range | Default | Description |
|---|---|---|---|
| itermax | int > 0 | 16 | Maximum number of iterations |
| tol | float > 0 | 1E-3 | Gain threshold for early stopping |
| lambda | float > 0 | 1E3 | Regularisation factor per channel |
| sigma | float > 0 | NaN | Noise standard deviation per channel |
| vs | float > 0 | NaN/1 | Voxel size. By default, it is inferred from the input headers. If it cannot be inferred, the default value is 1. |
| verbose | int >= 0 | 1 | Verbosity level: 0=quiet / 1=print / 2=plot / 3=plot more |
| map | bool | false | Memory map input data (slower but saves RAM) |
| threads | int >= 0 'matlab' 'automatic' |
'matlab' | Number of threads used by both Matlab and SPM: 'matlab' = Matlab's current settings 'automatic = Matlab's automatic setting |
| mask | bool | false | Mask of voxels to discard: false = use all all voxels true = estimate and mask out background array = mask out using provided background mask |
| folder | char | '' | Output folder. Do not write output files if empty. |
This algorithm has been described and used in a couple of ISMRM abstracts:
- Estimation of net receive sensitivity - at 3T and 7T - for correction of inter-scan motion artefacts in R1 mapping.
Yaël Balbastre, Nadège Corbin, Martina F. Callaghan
ISMRM 2020 - Modelling RF coil-sensitivity induced artefacts in prospective motion-corrected accelerated 3D-EPI fMRI
Nadine Graedel, Yaël Balbastre, Nadège Corbin, Oliver Josephs, Martina F. Callaghan
ISMRM 2020
This software is released under the GNU General Public License version 3 (GPL v3). As a result, you may copy, distribute and modify the software as long as you track changes/dates in source files. Any modifications to or software including (via compiler) GPL-licensed code must also be made available under the GPL along with build & install instructions.