This function calls fslmaths's help, as
fsltan is a wrapper for fslmaths
fsltan.help(...)passed to fslmaths.help
Prints help output and returns output as character vector
if (have.fsl()){
fsltan.help()
}
#> Usage: fslmaths [-dt <datatype>] <first_input> [operations and inputs] <output> [-odt <datatype>]Datatype information: -dt sets the datatype used internally for calculations (default float for all except double images) -odt sets the output datatype ( default is float ) Possible datatypes are: char short int float double input "input" will set the datatype to that of the original imageBinary operations: (some inputs can be either an image or a number) -add : add following input to current image -sub : subtract following input from current image -mul : multiply current image by following input -div : divide current image by following input -rem : modulus remainder - divide current image by following input and take remainder -mas : use (following image>0) to mask current image -thr : use following number to threshold current image (zero anything below the number) -thrp : use following percentage (0-100) of ROBUST RANGE to threshold current image (zero anything below the number) -thrP : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold below -uthr : use following number to upper-threshold current image (zero anything above the number) -uthrp : use following percentage (0-100) of ROBUST RANGE to upper-threshold current image (zero anything above the number) -uthrP : use following percentage (0-100) of ROBUST RANGE of non-zero voxels and threshold above -max : take maximum of following input and current image -min : take minimum of following input and current image -seed : seed random number generator with following number -restart : replace the current image with input for future processing operations -save : save the current working image to the input filenameBasic unary operations: -exp : exponential -log : natural logarithm -sin : sine function -cos : cosine function -tan : tangent function -asin : arc sine function -acos : arc cosine function -atan : arc tangent function -sqr : square -sqrt : square root -recip : reciprocal (1/current image) -abs : absolute value -bin : use (current image>0) to binarise -binv : binarise and invert (binarisation and logical inversion) -fillh : fill holes in a binary mask (holes are internal - i.e. do not touch the edge of the FOV) -fillh26 : fill holes using 26 connectivity -index : replace each nonzero voxel with a unique (subject to wrapping) index number -grid <value> <spacing> : add a 3D grid of intensity <value> with grid spacing <spacing> -edge : edge strength -tfce <H> <E> <connectivity>: enhance with TFCE, e.g. -tfce 2 0.5 6 (maybe change 6 to 26 for skeletons) -tfceS <H> <E> <connectivity> <X> <Y> <Z> <tfce_thresh>: show support area for voxel (X,Y,Z) -nan : replace NaNs (improper numbers) with 0 -nanm : make NaN (improper number) mask with 1 for NaN voxels, 0 otherwise -rand : add uniform noise (range 0:1) -randn : add Gaussian noise (mean=0 sigma=1) -inm <mean> : (-i i ip.c) intensity normalisation (per 3D volume mean) -ing <mean> : (-I i ip.c) intensity normalisation, global 4D mean) -range : set the output calmin/max to full data rangeMatrix operations: -tensor_decomp : convert a 4D (6-timepoint )tensor image into L1,2,3,FA,MD,MO,V1,2,3 (remaining image in pipeline is FA)Kernel operations (set BEFORE filtering operation if desired): -kernel 3D : 3x3x3 box centered on target voxel (set as default kernel) -kernel 2D : 3x3x1 box centered on target voxel -kernel box <size> : all voxels in a cube of width <size> mm centered on target voxel -kernel boxv <size> : all voxels in a cube of width <size> voxels centered on target voxel, CAUTION: size should be an odd number -kernel boxv3 <X> <Y> <Z>: all voxels in a cuboid of dimensions X x Y x Z centered on target voxel, CAUTION: size should be an odd number -kernel gauss <sigma> : gaussian kernel (sigma in mm, not voxels) -kernel sphere <size> : all voxels in a sphere of radius <size> mm centered on target voxel -kernel file <filename> : use external file as kernelSpatial Filtering operations: N.B. all options apart from -s use the default kernel or that _previously_ specified by -kernel -dilM : Mean Dilation of non-zero voxels -dilD : Modal Dilation of non-zero voxels -dilF : Maximum filtering of all voxels -dilall : Apply -dilM repeatedly until the entire FOV is covered -ero : Erode by zeroing non-zero voxels when zero voxels found in kernel -eroF : Minimum filtering of all voxels -fmedian : Median Filtering -fmean : Mean filtering, kernel weighted (conventionally used with gauss kernel) -fmeanu : Mean filtering, kernel weighted, un-normalised (gives edge effects) -s <sigma> : create a gauss kernel of sigma mm and perform mean filtering -subsamp2 : downsamples image by a factor of 2 (keeping new voxels centred on old) -subsamp2offc : downsamples image by a factor of 2 (non-centred)Dimensionality reduction operations: (the "T" can be replaced by X, Y or Z to collapse across a different dimension) -Tmean : mean across time -Tstd : standard deviation across time -Tmax : max across time -Tmaxn : time index of max across time -Tmin : min across time -Tmedian : median across time -Tperc <percentage> : nth percentile (0-100) of FULL RANGE across time -Tar1 : temporal AR(1) coefficient (use -odt float and probably demean first)Basic statistical operations: -pval : Nonparametric uncorrected P-value, assuming timepoints are the permutations; first timepoint is actual (unpermuted) stats image -pval0 : Same as -pval, but treat zeros as missing data -cpval : Same as -pval, but gives FWE corrected P-values -ztop : Convert Z-stat to (uncorrected) P -ptoz : Convert (uncorrected) P to Z -rank : Convert data to ranks (over T dim) -ranknorm: Transform to Normal dist via ranksMulti-argument operations: -roi <xmin> <xsize> <ymin> <ysize> <zmin> <zsize> <tmin> <tsize> : zero outside roi (using voxel coordinates). Inputting -1 for a size will set it to the full image extent for that dimension. -bptf <hp_sigma> <lp_sigma> : (-t in ip.c) Bandpass temporal filtering; nonlinear highpass and Gaussian linear lowpass (with sigmas in volumes, not seconds); set either sigma<0 to skip that filter -roc <AROC-thresh> <outfile> [4Dnoiseonly] <truth> : take (normally binary) truth and test current image in ROC analysis against truth. <AROC-thresh> is usually 0.05 and is limit of Area-under-ROC measure FP axis. <outfile> is a text file of the ROC curve (triplets of values: FP TP threshold). If the truth image contains negative voxels these get excluded from all calculations. If <AROC-thresh> is positive then the [4Dnoiseonly] option needs to be set, and the FP rate is determined from this noise-only data, and is set to be the fraction of timepoints where any FP (anywhere) is seen, as found in the noise-only 4d-dataset. This is then controlling the FWE rate. If <AROC-thresh> is negative the FP rate is calculated from the zero-value parts of the <truth> image, this time averaging voxelwise FP rate over all timepoints. In both cases the TP rate is the average fraction of truth=positive voxels correctly found.Combining 4D and 3D images: If you apply a Binary operation (one that takes the current image and a new image together), when one is 3D and the other is 4D, the 3D image is cloned temporally to match the temporal dimensions of the 4D image.e.g. fslmaths inputVolume -add inputVolume2 output_volume fslmaths inputVolume -add 2.5 output_volume fslmaths inputVolume -add 2.5 -mul inputVolume2 output_volume fslmaths 4D_inputVolume -Tmean -mul -1 -add 4D_inputVolume demeaned_4D_inputVolume