AUTOGENERATED. DO NOT EDIT.


--affinereg--

Usage: affinereg [-g] [--mask-reference <file>] [--mask-moved <file>] [-T] [-R] [-A] <reference> <moved> <affine> 

Affine registration of reference of <input> and  <moved>.

-g                       use gpu (if available)
--mask-reference file    binary mask for the reference image
--mask-moved file        binary mask for the moved image
-T                       Translation
-R                       Rigid transformation (default)
-A                       All degrees of freedom
-h                       help


--avg--

Usage: avg [-w] bitmask <input> <output> 

Calculates (weighted) average along dimensions specified by bitmask.

-w    weighted average
-h    help


--bench--

Usage: bench [-T] [-S] [-s d] [<output>] 

Performs a series of micro-benchmarks.

-T          varying number of threads
-S          varying problem size
-s flags    select benchmarks
-h          help


--bin--

Usage: bin [-l d] [-o] [-R d] [-C d] [-a d] [-O f:f] [-M] [--zero-fill d:d] [--stream] <label> <src> <dst> 

Binning

-l dim                               Bin according to labels: Specify cluster dimension
-o                                   Reorder according to labels
-R n_resp                            Quadrature Binning: Number of respiratory labels
-C n_card                            Quadrature Binning: Number of cardiac labels
-a window                            Quadrature Binning: Moving average
-O [r:c]deg                          Quadrature Binning: Angle offset for resp and card.
-M                                   Amplitude binning
--zero-fill <dim>:<num_of_frames>    Specify dimension and number of zero filled frames. Zero-filling according to order in label_file.
--stream                             Stream input/output.
-h                                   help


--bitmask--

Usage: bitmask [-b] [dim1 ... dimN ] 

Convert between a bitmask and set of dimensions.

-b    dimensions from bitmask, use with exactly one argument
-h    help


--bloch--

Usage: bloch <signal> <signal> 

simulation tool

-h  help


--cabs--

Usage: cabs <input> <output> 

Absolute value of array (|<input>|).

-h  help


--calc--

Usage: calc [-L...] func <input> <output> 

Perform function evaluation on array.

-L...    Print a list of all supported functions
-h       help


--caldir--

Usage: caldir cal_size <input> <output> 

Estimates coil sensitivities from the k-space center using
a direct method (McKenzie et al.). The size of the fully-sampled
calibration region is automatically determined but limited by
{cal_size} (e.g. in the readout direction).

-h  help


--calmat--

Usage: calmat [-k d:d:d] [-r d:d:d] <kspace> <calibration_matrix> 

Compute calibration matrix.

-k ksize       kernel size
-r cal_size    Limits the size of the calibration region.
-h             help


--carg--

Usage: carg <input> <output> 

Argument (phase angle).

-h  help


--casorati--

Usage: casorati dim1 kern1 ... dimN kernN <input> <output> 

Casorati matrix with kernel (kern1, ..., kernN) along dimensions (dim1, ..., dimN).

-h  help


--cc--

Usage: cc [-p d] [-M] [-r d:d:d] [-A] [-S] [-G] [-E] <kspace> <coeff|proj_kspace> 

Performs coil compression.

-p N    perform compression to N virtual channels
-M      output compression matrix
-r S    size of calibration region
-A      use all data to compute coefficients
-S      type: SVD
-G      type: Geometric
-E      type: ESPIRiT
-h      help


--ccapply--

Usage: ccapply [-p d] [-u] [-t] [-S] [-G] [-E] [-A d] <kspace> <cc_matrix> <proj_kspace> 

Apply coil compression forward/inverse operation.

-p N      perform compression to N virtual channels
-u        apply inverse operation
-t        don't apply FFT in readout
-S        type: SVD
-G        type: Geometric
-E        type: ESPIRiT
-A dim    Perform alignment of coil sensitivities along dimension A
-h        help


--cdf97--

Usage: cdf97 [-i] bitmask <input> <output> 

Perform a wavelet (cdf97) transform.

-i    inverse
-h    help


--circshift--

Usage: circshift dim shift <input> <output> 

Perform circular shift along {dim} by {shift} elements.

-h  help


--coils--

Usage: coils [-k] [-n d] [-b d] [-t <file>] [--H2D8C] [--H3D64C] <sens> 

Compute coil sensitivitity maps in x-space or k-space.

-k          Compute for k-space.
-n d        Select first n coil channels.
-b d        Bitmask for selecting subset of channels.

-t grid     grid on which sensitivity maps should be evaluated.

--H2D8C     2D head coil 8 channels (default).
--H3D64C    3D head coil 64 channels.
-h          help


--compress--

Usage: compress [-d] <input> <mask> <output> 

Compress data using a binary mask (pattern)

-d    decompress data
-h    help


--conj--

Usage: conj <input> <output> 

Compute complex conjugate.

-h  help


--conv--

Usage: conv bitmask <input> <kernel> <output> 

Performs a convolution along selected dimensions.

-h  help


--conway--

Usage: conway [-P] [-n d] <input> <output> 

Conway's game of life.

-P      periodic boundary conditions
-n #    nr. of iterations
-h      help


--copy--

Usage: copy [--stream d] [--delay f] [dim1 pos1 ... dimN posN ] <input> <output> 

Copy an array (to a given position in the output file - which then must exist).

--stream flags    Loop over <flags> while streaming.
--delay f         Wait for f seconds before each copy when streaming.
-h                help


--cpyphs--

Usage: cpyphs <input> <output> 

Copy phase from <input> to <output>.

-h  help


--creal--

Usage: creal <input> <output> 

Real value.

-h  help


--crop--

Usage: crop dimension size <input> <output> 

Extracts a sub-array corresponding to the central part of {size} along {dimension}

-h  help


--cunet--

Usage: cunet [-T,--train-algo ...] [--sigma ...] [-g,--gpu] [-l d] [-r] [-b d] <images> <weights> 

Train a score network with Conditional-UNet architecture.

-T,--train-algo ...    configure general training parameters
--sigma ...            select noise schedule for decreasing coise
-g,--gpu               run on gpu
-l l                   Number of unet levels
-r                     use real-valued network (i.e. with z ~ N(0, I))
-b b                   batch size
-h                     help


--delta--

Usage: delta dims flags size <out> 

Kronecker delta.

-h  help


--denoise--

Usage: denoise [-l ...] [-r f] [-R ...] [-s f] [-i d] [-n] [-N] [-g] [-b d] [-e] [--adaptive-stepsize] [--asl] [--teasl] [--theta f:f] [--tvscales [f:]*f] [--tvscales2 [f:]*f] [--alpha f:f] [--gamma f:f] [-d d] [-u f] [-C d] [-I,--ist] [--fista] [--eulermaruyama] [-m,--admm] [-a,--pridu] [-w f] [-S] [--wavelet <string>] [--fista_pqr f:f:f] [--fista_last] <image> <output> 

Perform image denoising with regularization.


-l 1/-l2                  toggle l1-wavelet or l2 regularization.
-r lambda                regularization parameter
-R <T>:A:B:C             generalized regularization options (-Rh for help)
-s step                  iteration stepsize
-i iter                  max. number of iterations
-n                       disable random wavelet cycle spinning
-N                       do fully overlapping LLR blocks
-g                       use GPU
-b blk                   Lowrank block size
-e                       Scale stepsize based on max. eigenvalue
--adaptive-stepsize      PRIDU adaptive step size
--asl                    ASL reconstruction
--teasl                  Time-encoded ASL reconstruction
--theta theta1:theta2    PWI weight for ASL reconstruction
--tvscales [f:]*f        Scaling of derivatives in TV or TGV regularization
--tvscales2 [f:]*f       Scaling of secondary derivatives in ICTV reconstruction
--alpha alpha1:alpha0    regularization parameter for TGV and ICTGV reconstruction
--gamma gamma1:gamma2    regularization parameter for ICTV and ICTGV reconstruction
-d level                 Debug level
-u rho                   ADMM rho
-C iter                  ADMM max. CG iterations
-I,--ist                 select IST
--fista                  select FISTA
--eulermaruyama          select Euler Maruyama
-m,--admm                select ADMM
-a,--pridu               select Primal Dual
-w f                     inverse scaling of the data
-S                       re-scale the image after denoising
--wavelet name           wavelet type (haar,dau2,cdf44)
--fista_pqr p:q:r        parameters for FISTA acceleration
--fista_last             end iteration with call to data consistency
-h                       help


--ecalib--

Usage: ecalib [-t f] [-c f] [-k d:d:d] [-r d:d:d] [-m d] [-S] [-W] [-I] [-1] [-P] [-N] [-A] [-v f] [-a] [-d d] <kspace> <sensitivities> [<ev-maps>] 

Estimate coil sensitivities using ESPIRiT calibration.
Optionally outputs the eigenvalue maps.

-t threshold     This determined the size of the null-space.
-c crop_value    Crop the sensitivities if the eigenvalue is smaller than {crop_value}.
-k ksize         kernel size
-r cal_size      Limits the size of the calibration region.
-m maps          Number of maps to compute.
-S               create maps with smooth transitions (Soft-SENSE).
-W               soft-weighting of the singular vectors.
-I               intensity correction
-1               perform only first part of the calibration
-P               Do not rotate the phase with respect to the first principal component
-N               Use phase normalization
-A               Approximate eigenvalue decomposition using Nyström
-v variance      Variance of noise in data.
-a               Automatically pick thresholds.
-d level         Debug level
-h               help


--ecaltwo--

Usage: ecaltwo [-c f] [-m d] [-S] x y z <input> <sensitivities> [<ev-maps>] 

Second part of ESPIRiT calibration.
Optionally outputs the eigenvalue maps.

-c crop_value    Crop the sensitivities if the eigenvalue is smaller than {crop_value}.
-m maps          Number of maps to compute.
-S               Create maps with smooth transitions (Soft-SENSE).
-h               help


--epg--

Usage: epg [-C] [-M] [-H] [-F] [-S] [-B] [-1 f] [-2 f] [-b f] [-o f] [-r f] [-e f] [-f f] [-s d] [-n d] [-u d] [-v d] <signal intensity> [<configuration states>] [<(rel.) signal derivatives>] [<configuration derivatives>] 

Simulate MR pulse sequence based on Extended Phase Graphs (EPG)

-C        CPMG
-M        fmSSFP
-H        Hyperecho
-F        FLASH
-S        Spinecho
-B        bSSFP
-1 T1     T1 [units of time]
-2 T2     T2 [units of time]
-b B1     relative B1 [unitless]
-o OFF    off-resonance [units of inverse time]
-r TR     repetition time [units of time]
-e TE     echo time [units of time]
-f FA     flip angle [degrees] / refocusing FA for CPMG
-s SP     spoiling (0: ideal, 1: conventional RF, 2: random RF)
-n N      number of pulses
-u U      unknowns as bitmask (0: T1, 1: T2, 2: B1, 3: off-res)
-v V      verbosity level
-h        help


--estdelay--

Usage: estdelay [-R] [-p d] [-n d] [-r f] [-B] <trajectory> <data> [<qf>] 

Estimate gradient delays from radial data.

-R      RING method
-p p    [RING] Padding
-n n    [RING] Number of intersecting spokes
-r r    [RING] Central region size
-B      [RING] Assume B0 eddy currents
-h      help


--estdims--

Usage: estdims <traj> 

Estimate image dimension from non-Cartesian trajectory.
Assume trajectory scaled to -DIM/2 to DIM/2 (ie dk=1/FOV=1)

-h  help


--estmotion--

Usage: estmotion [-g] [-l d] [--optical-flow] [-r f] [--max-flow f] flags <reference> <moved> <motion field> [<inverse motion field>] 

Non-rigid registration with greedy SyN or optical flow algorithm.

-g                use gpu (if available)
-l d              number of levels in Gaussian pyramide
--optical-flow    use optical flow instead of greedy SyN
-r lambda         regularization strength for TV (optical flow)
--max-flow max    constraint on flow magnitude (optical flow)
-h                help


--estscaling--

Usage: estscaling [-i] [-x d:d:d] [-p,--percentile f] <k-space> <scaling> 

Estimate scaling from k-space center.


-i                   invert scaling (to directly multiply k-space data)
-x x:y:z             image dimensions
-p,--percentile p    use p-percentile for scaling
-h                   help


--estshift--

Usage: estshift [-f] flags <arg1> <arg2> [<out>] 

Estimate sub-pixel shift.

-f    use FoV coordinates
-h    help


--estvar--

Usage: estvar [-k d:d:d] [-r d:d:d] <kspace> 

Estimate the noise variance assuming white Gaussian noise.

-k ksize       kernel size
-r cal_size    Limits the size of the calibration region.
-h             help


--extract--

Usage: extract dim1 start1 end1 ... dimN startN endN <input> <output> 

Extracts a sub-array along dims from index start to (not including) end.

-h  help


--fakeksp--

Usage: fakeksp [-r] <image> <kspace> <sens> <output> 

Recreate k-space from image and sensitivities.

-r    replace measured samples with original values
-h    help


--fft--

Usage: fft [-u] [-i] [-n] bitmask <input> <output> 

Performs a fast Fourier transform (FFT) along selected dimensions.

-u    unitary
-i    inverse
-n    un-centered
-h    help


--fftmod--

Usage: fftmod [-i] bitmask <input> <output> 

Apply 1 -1 modulation along dimensions selected by the {bitmask}.

-i    inverse
-h    help


--fftrot--

Usage: fftrot dim1 dim2 theta <input> <output> 

Performs a rotation using Fourier transform (FFT) along selected dimensions.

-h  help


--fftshift--

Usage: fftshift [-b] bitmask <input> <output> 

Apply fftshift along dimensions selected by the {bitmask}.

-b    apply ifftshift
-h    help


--filter--

Usage: filter [-m d] [-l d] [-G] [-a d] [-C d] [-d d] [-b d] [-Z] <input> <output> 

Apply filter.

-m dim    median filter along dimension dim
-l len    length of filter
-G        geometric median
-a dim    Moving average filter along dimension dim
-C dim    Cumulative sum along dimension dim
-d dim    forward difference along dimension dim
-b dim    backward difference along dimension dim
-Z        set first (forward diff) or last (backward) value to zero
-h        help


--flatten--

Usage: flatten <input> <output> 

Flatten array to one dimension.

-h  help


--flip--

Usage: flip bitmask <input> <output> 

Flip (reverse) dimensions specified by the {bitmask}.

-h  help


--fmac--

Usage: fmac [-A] [-C] [-s d] <input1> [<input2>] <output> 

Multiply <input1> and <input2> and accumulate in <output>.
If <input2> is not specified, assume all-ones.

-A      add to existing output (instead of overwriting)
-C      conjugate input2
-s b    squash dimensions selected by bitmask b
-h      help


--fovshift--

Usage: fovshift [-t <file>] [-S <file>] [-s f:f:f] [-p] <input> <output> 

Shifts FOV.

-t file     k-space trajectory
-S file     FOV shift
-s X:Y:Z    FOV shift
-p          interpret FOV shift in units of pixel instead of units of FoV
-h          help


--gmm--

Usage: gmm [--score] [--sample] <weights> <mean values> <variances> [<evaluate at these points>] <output> 

Evaluate Gaussian mixture.

--score     compute score
--sample    create sample
-h          help


--grid--

Usage: grid [-k] [-t <file>] [-D d0:d1:...:dN] [-T d] [--b1 f:f:f] [--b2 f:f:f] [--b3 f:f:f] [--bt f] <grid> 

Compute sampling grid for x-space / k-space (and time).

-k                    Compute k-space grid
-t Trajectory file    Sampling trajectory for k-space

-D d0:d1:...:dN       Size of x-space / k-space
-T T                  Number of time points
--b1 f1:f2:f3         First basis vector
--b2 f1:f2:f3         Second basis vector
--b3 f1:f2:f3         Third basis vector
--bt f1               Time step.
-h                    help


--grog--

Usage: grog [-s,--calib-spokes d] <radial trajectory> <radial data> <gridded trajectory> <gridded data> 

GROG calibration and gridding of radial data.


-s,--calib-spokes num    Number of spokes for GROG calibration
-h                       help


--hist--

Usage: hist [-c] [-s d] bitmask <input> <output> 

Compute histogram from -0.5 to 0.5.

-c         complex
-s size    number of bins
-h         help


--homodyne--

Usage: homodyne [-r f] [-I] [-C] [-P <file>] [-n] dim fraction <input> <output> 

Perform homodyne reconstruction along dimension dim.

-r alpha         Offset of ramp filter, between 0 and 1. alpha=0 is a full ramp, alpha=1 is a horizontal line
-I               Input is in image domain
-C               Clear unacquired portion of kspace
-P phase_ref>    Use <phase_ref> as phase reference
-n               use uncentered ffts
-h               help


--ictv--

Usage: ictv [-i d] [-u f] [--tvscales [f:]*f] [--tvscales2 [f:]*f] [--gamma f:f] lambda flags <input> <output> 

Infimal convolution of total variation along dims specified by flags.

-i i                     max. iterations
-u rho                   rho in ADMM
--tvscales [f:]*f        Scaling of derivatives of the first gradient
--tvscales2 [f:]*f       Scaling of derivatives of the second gradient
--gamma gamma1:gamma2    gamma1 * || grad (x - z) ||_1, gamma2 * || grad z ||_1
-h                       help


--index--

Usage: index [--end] [--log] [--min f] [--max f] dim size <name> 

Create an array counting from 0 to {size-1} in dimensions {dim}.

--end        include the endpoint (last value is size)
--log        use logarithmic spacing (but specify min and max)
--min min    minimum value (default 0)
--max max    maximum value (default size)
-h           help


--interpolate--

Usage: interpolate [-x d:d:d] [-A] [-D] [-N] [-C] flags <input> [<coordinates (default) / displacement field / affine transform>] <output> 

Interpolate with coordinates, displacement field, or affine transform.

-x x:y:z    output dimensions for affine interpolation or coordinates
-A          use affine transform for interpolation
-D          use displacement field for interpolation
-N          use nearest neighbour interpolation
-C          use cubic interpolation
-h          help


--invert--

Usage: invert [-r f] <input> <output> 

Invert array (1 / <input>). The output is set to zero in case of divide by zero.

-r reg    regularization
-h        help


--itsense--

Usage: itsense alpha <sensitivities> <kspace> <pattern> <output> 

A simplified implementation of iterative sense reconstruction
with l2-regularization.

-h  help


--join--

Usage: join [-a] [-s] dimension <input>1> ... <input>N> <output> 

Join input files along {dimensions}. All other dimensions must have the same size.
	 Example 1: join 0 slice_001 slice_002 slice_003 full_data
	 Example 2: join 0 `seq -f "slice_%%03g" 0 255` full_data

-a    append - only works for cfl files!
-s    join along streamed dimension!
-h    help


--looklocker--

Usage: looklocker [-t f] [-D f] <input> <output> 

Compute T1 map from M_0, M_ss, and R_1*.

-t threshold    Pixels with M0 values smaller than {threshold} are set to zero.
-D delay        Time between the middle of inversion pulse and the first excitation.
-h              help


--lrmatrix--

Usage: lrmatrix [-d] [-i d] [-m d] [-f d] [-j d] [-k d] [-N] [-s] [-l d] [-o <file>] <input> <output> 

Perform (multi-scale) low rank matrix completion

-d          perform decomposition instead, ie fully sampled
-i iter     maximum iterations.
-m flags    which dimensions are reshaped to matrix columns.
-f flags    which dimensions to perform multi-scale partition.
-j scale    block size scaling from one scale to the next one.
-k size     smallest block size
-N          add noise scale to account for Gaussian noise.
-s          perform low rank + sparse matrix completion.
-l size     perform locally low rank soft thresholding with specified block size.
-o out2     summed over all non-noise scales to create a denoised output.
-h          help


--mandelbrot--

Usage: mandelbrot [-s d] [-n d] [-t f] [-z f] [-r f] [-i f] [-I] <output> 

Compute mandelbrot set.

-s size    image size
-n #       nr. of iterations
-t t       threshold for divergence
-z z       zoom
-r r       offset real
-i i       offset imag
-I         Save iterations
-h         help


--measure--

Usage: measure [--mse] [--mse-mag] [--ssim] [--psnr] <reference> <input> [<output>] 

Measure image quality.

--mse        mse
--mse-mag    mse of rss (over coil dim)
--ssim       ssim of rss (over coil dim) and mean over other dims
--psnr       psnr of rss (over coil dim) and mean over other dims
-h           help


--mip--

Usage: mip [-m] [-a] bitmask <input> <output> 

Maximum (minimum) intensity projection (MIP) along dimensions specified by bitmask.

-m    minimum
-a    do absolute value first
-h    help


--mnist--

Usage: mnist [-a,--apply] [-t,--train] [-g,--gpu] <input> <weights> <ref/output> 

Trains or applies a MNIST network.
This network is to demonstrate how a neural network can be implemented in BART.

-a,--apply    apply nnet
-t,--train    trains network
-g,--gpu      run on gpu
-h            help


--moba--

Usage: moba [-r ...] [-L] [-P] [-T] [-G] [-D] [--bloch] [-m d] [-l d] [-i d] [-R,--reduction f] [-j f] [-u f] [-C d] [-s f] [-B f] [-b f:f] [-d d] [-f f] [-p <file>] [-J] [-M] [-g] [--positive-maps d] [--not-wav-maps d] [--l2-on-parameters d] [--pusteps d] [--ratio f] [--l1val f] [--temporal_damping f] [-I <file>] [-t <file>] [-o f] [--img_dims d:d:d] [-k] [--kfilter-1] [--kfilter-2] [-e f] [--fat_spec_0] [--scale_data f] [--seq ...] [--sim ...] [--other ...] <kspace> <TI/TE> <output> [<sensitivities>] 

Model-based nonlinear inverse reconstruction

-r <T>:A:B:C               generalized regularization options (-rh for help)
-L                         T1 mapping using model-based look-locker
-P                         T1 mapping using reparameterized (M0, R1, alpha) model-based look-locker (TR required!)
-T                         T2 mapping using model-based Fast Spin Echo
-G                         T2* mapping using model-based multiple gradient echo
-D                         Joint T1 and T2* mapping using model-based IR multiple gradient echo
--bloch                    Bloch model-based reconstruction
-m model                   Select the MGRE model from enum { WF = 0, WFR2S, WF2R2S, R2S, PHASEDIFF, ..., WF_fB0, WF_R2S, T1_R2S, W_T1_F_T1_RS2 } [default: WFR2S]
-l 1/-l2                    toggle l1-wavelet or l2 regularization.
-i iter                    Number of Newton steps
-R,--reduction redu        reduction factor
-j minreg                  Minimum regularization parameter
-u rho                     ADMM rho [default: 0.01]
-C iter                    inner iterations
-s step                    step size
-B bound                   lower bound for relaxation
-b SMO:SC                  B0 field: spatial smooth level; scaling [default: 222.; 1.]
-d level                   Debug level
-f FOV                     
-p PSF                     
-J                         Stack frames for joint recon
-M                         Simultaneous Multi-Slice reconstruction
-g                         use gpu
--positive-maps flag       Maps with positivity constraint as FLAG!
--not-wav-maps d           Maps removed from wavelet denoising (counted from back!)
--l2-on-parameters flag    Flag for parameter maps with l2 norm
--pusteps ud               Number of partial update steps for IRGNM
--ratio f:[0;1]            Ratio of partial updates: ratio*<updated-map> + (1-ratio)*<previous-map>
--l1val f                  Regularization scaling of l1 wavelet (default: 1.)
--temporal_damping f       Temporal damping factor.
-I init                    File for initialization
-t traj                    K-space trajectory
-o os                      Oversampling factor for gridding [default: 1.]
--img_dims x:y:z           dimensions
-k                         k-space edge filter for non-Cartesian trajectories
--kfilter-1                k-space edge filter 1
--kfilter-2                k-space edge filter 2
-e kfilter_strength        strength for k-space edge filter [default: 2e-3]
--fat_spec_0               select fat spectrum from ISMRM fat-water tool
--scale_data f             scaling factor for data
--seq ...                  configure sequence parameters
--sim ...                  configure simulation parameters
--other ...                configure other parameters
-h                         help


--mobafit--

Usage: mobafit [-T] [-I] [-L] [-G] [-D] [-S] [-m d] [-a] [-i d] [-M d] [-g] [-B <file>] [--init [f:]*f] [--scale [f:]*f] [--levenberg-marquardt] [--reg-against-init] [--min-flag d] [--max-flag d] [--max-mag-flag d] [--min [f:]*f] [--max [f:]*f] [--b1map <file>] [--b0map <file>] [--seq ...] [--sim ...] [--other ...] [--pool ...] [--cest ...] <enc> <echo/contrast images> [<coefficients>] 

Pixel-wise fitting of physical signal models.

-T                         Multi-Echo Spin Echo: f(M0, R2) = M0 * exp(-t * R2)
-I                         Inversion Recovery: f(M0, R1, c) =  M0 * (1 - exp(-t * R1 + c))
-L                         Inversion Recovery Look-Locker: f(Mss, M0, R1s) = Mss - (Mss + M0) * exp(-t * R1s)
-G                         MGRE
-D                         diffusion
-S                         Simulation based fitting
-m model                   Select the MGRE model from enum { WF = 0, WFR2S, WF2R2S, R2S, PHASEDIFF } [default: WFR2S]
-a                         fit magnitude of signal model to data
-i iter                    Number of IRGNM steps
-M num_lorentzian_pools    Multi-Pool-Lorentzian - Number of pools
-g                         use gpu
-B file                    temporal (or other) basis
--init [f:]*f              Initial values of parameters in model-based reconstruction
--scale [f:]*f             Scaling
--levenberg-marquardt      Use Levenberg-Marquardt instead of Gauss-Newton
--reg-against-init         Use initial guess as regularization target with IRGN
--min-flag flags           Apply minimum constraint on selected maps
--max-flag flags           Apply maximum constraint on selected maps
--max-mag-flag flags       Apply maximum magnitude constraint on selected maps
--min [f:]*f               Min bound (map must be selected with "min-flag")
--max [f:]*f               Max bound (map must be selected with "max-flag" or "max-mag-flag")
--b1map [deg]              Input B1 map as cfl file
--b0map [rad/s]            Input B0 map as cfl file
--seq ...                  configure sequence parameters for simulation based fitting
--sim ...                  configure simulation parameters
--other ...                configure other simulation parameters
--pool ...                 configure pool parameters for BMC simulation
--cest ...                 configure parameters for CEST simulation
-h                         help


--mobasig--

Usage: mobasig [-I] [-L] [-M] [-D] [-T] [-G] [-m d] <parameters/coefficients> <encoding> <signal> 

Forward calculation of physical signal models.

-I          Inversion Recovery: f(M0, R1, c) =  M0 * (1 - exp(-t * R1 + c))
-L          Inversion Recovery Look-Locker (M0', MSS, R1S)
-M          Multi-Pool-Lorentzian model
-D          diffusion
-T          Multi-Echo Spin Echo: f(M0, R2) = M0 * exp(-t * R2)
-G          MGRE
-m model    Select the MGRE model from enum { WF = 0, WFR2S, WF2R2S, R2S, PHASEDIFF } [default: WFR2S]
-h          help


--morphop--

Usage: morphop [-e] [-d] [-o] [-c] [-l] [-B] mask_size <binary input> [<binary output>] 

Perform morphological operators on binary data with odd mask sizes.

-e    EROSION (default)
-d    DILATION
-o    OPENING
-c    CLOSING
-l    LABEL
-B    use BALL structuring element
-h    help


--multicfl--

Usage: multicfl [-s] <cfl>1> ... <cfl>N> 

Combine/Split multiple cfl files to one multi-cfl file.
In normal usage, the last argument is the combined multi-cfl,
with '-s', the first argument is the multi-cfl that is split up

-s    separate
-h    help


--ncalib--

Usage: ncalib [-g] [-t <file>] [-p <file>] [-B <file>] [-r d:d:d] [-i d] [-o] [-N] [-m d] [-x,--dims d:d:d] [--shared-img-dims d] [--shared-col-dims d] [--scale-loop-dims d] [--phase-pole d] <kspace> <sensitivities> [<image (roughly scaled to rss of lowres k-space)>] 

Estimate coil sensitivities using ENLIVE calibration.

-g                         use gpu
-t file                    kspace trajectory
-p file                    kspace pattern
-B file                    subspace basis
-r cal_size                Limits the size of the calibration region.
-i iter                    Number of Newton steps
-o                         return oversampled coils
-N                         Normalize coil sensitivities
-m nmaps                   Number of ENLIVE maps to use in reconstruction
-x,--dims x:y:z            Explicitly specify sens dimensions
--shared-img-dims flags    deselect image dims with flags
--shared-col-dims flags    deselect coil dims with flags
--scale-loop-dims flags    scale parameters as if ncalib was looped over these dims
--phase-pole d             Use phase pole detection after d iterations (0 for every iteration)
-h                         help


--nlinv--

Usage: nlinv [-i d] [--reduction-factor f] [-R ...] [--reg-iter d] [-d d] [-c] [-N] [-m d] [-U] [-f f] [-p <file>] [-t <file>] [-B <file>] [-I <file>] [-g] [-S] [-x,--dims d:d:d] [--sens-dims d:d:d] [--real-time] [--phase-pole d] [--fast] [--nufft-conf ...] <kspace> <output> [<sensitivities>] 

Jointly estimate image and sensitivities with nonlinear
inversion using {iter} iteration steps. Optionally outputs
the sensitivities.

-i iter                 Number of Newton steps
--reduction-factor q    reduction factor
-R <T>:A:B:C            generalized regularization options (-Rh for help)
--reg-iter iter         Number of Newton steps with regularization (-1 means all)
-d level                Debug level
-c                      Real-value constraint
-N                      Do not normalize image with coil sensitivities
-m nmaps                Number of ENLIVE maps to use in reconstruction
-U                      Do not combine ENLIVE maps in output
-f FOV                  restrict FOV
-p file                 pattern / transfer function
-t file                 kspace trajectory
-B file                 temporal (or other) basis
-I file                 File for initialization
-g                      use gpu
-S                      Re-scale image after reconstruction
-x,--dims x:y:z         Explicitly specify image dimensions
--sens-dims x:y:z       Explicitly specify sens dimensions
--real-time             Use real-time (temporal l2) regularization
--phase-pole d          Use phase pole detection after d iterations (0 for every iteration)
--fast                  Use tuned but less generic model
--nufft-conf ...        configure nufft
-h                      help


--nlinvnet--

Usage: nlinvnet [--iter-net d] [--resnet-block ...] [--unet ...] [-i d] [--lambda f] [--lambda-sens f] [-N,--network ...] [--filter <file>] [--conv-time d] [--init-rtnlinv] [-x,--dims d:d:d] [-t,--train] [-a,--apply] [--rss-norm] [--pattern <file>] [--trajectory <file>] [--nufft-conf ...] [-B,--basis <file>] [--scaling f] [--scaling-flags d] [-g,--gpu] [-b,--batch-size d] [-l,--load <file>] [-T,--train-algo ...] [--adam ...] [--train-loss ...] [--mask <file>] [--train-loss-l2-reg f] [--ksp-training] [--ss-ksp-split f] [--ss-ksp-split-shared d] [--ss-ksp-use-reco <file>] [--ss-ksp-leaky f] [--temporal-train-mask d:d] [--average-coils-loss d] [-L d] <kspace> <weights> <output/reference> [<sensitivities>] 

Perform NLINV-Net reconstruction.

--iter-net iter                number of iterations with network
--resnet-block ...             configure residual block
--unet ...                     configure U-Net block
-i iter                        Number of Newton steps
--lambda val                   additional regularization for network part (negative means trainable)
--lambda-sens val              additional regularization for sensitivities (negative means trainable)
-N,--network ...               select neural network
--filter <filter>              filter output of network block
--conv-time w                  convolve along dimension 10 with window size w
--init-rtnlinv                 initialize with rtnlinv recon
-x,--dims x:y:z                image dimensions
-t,--train                     train nlinvnet
-a,--apply                     apply nlinvnet
--rss-norm                     scale output image to rss normalization
--pattern <pattern>            sampling pattern
--trajectory <traj>            trajectory
--nufft-conf ...               configure nufft
-B,--basis <basis>             basis
--scaling val                  scaling of data, negative means normalization to norm=val
--scaling-flags flags          scaling is increased with sqrt(selected dims)
-g,--gpu                       run on gpu
-b,--batch-size d              size of mini batches
-l,--load <weights-init>       load weights for continuing training
-T,--train-algo ...            configure general training parameters
--adam ...                     configure adam optimizer
--train-loss ...               configure the training loss
--mask <mask>                  mask for computation of loss
--train-loss-l2-reg l          add l(||x||^2 + ||Wc||^2) to train loss
--ksp-training                 provide kspace as reference
--ss-ksp-split p               use p% of kspace data as network input
--ss-ksp-split-shared flags    shared dims for mask
--ss-ksp-use-reco file         mask should contain 0 or 1. Entries with 1 are neverused as loss
--ss-ksp-leaky l               also use network input scaled by l as reference
--temporal-train-mask s:e      Only use data in [s, e) as train reference
--average-coils-loss l         Average coils along window of size l for loss computation
-L flags                       loop over dims (apply only)
-h                             help


--nlmeans--

Usage: nlmeans [-p,--patch_length d] [-d,--patch_dist d] [-H f] [-a f] flags <input> <output> 

Non-local means filter

-p,--patch_length int    patch length
-d,--patch_dist int      patch distance
-H h                     NLMeans h
-a a                     NLMeans a (stddev for gaussian euclidean distance)
-h                       help


--nnet--

Usage: nnet [-a,--apply] [-e,--eval] [-t,--train] [-g,--gpu] [-b,--batch-size d] [-l,--load <file>] [-N,--network ...] [-U,--unet-segm ...] [--nnunet-segm ...] [--train-loss ...] [--valid-loss ...] [--valid-data ...] [-T,--train-algo ...] [--adam ...] [--load-memory] [--export-graph <string>] <input> <weights> <ref/output> 

Trains or applies a neural network.

-a,--apply                   apply nnet
-e,--eval                    evaluate nnet
-t,--train                   trains network
-g,--gpu                     run on gpu
-b,--batch-size batchsize    size of mini batches
-l,--load <weights-init>     load weights for continuing training
-N,--network ...             select neural network
-U,--unet-segm ...           configure U-Net for segmentation
--nnunet-segm ...            configure nnU-Net for segmentation
--train-loss ...             configure the training loss
--valid-loss ...             configure the validation loss
--valid-data ...             provide validation data
-T,--train-algo ...          configure general training parameters
--adam ...                   configure Adam
--load-memory                load files into memory
--export-graph <file.dot>    export graph for visualization
-h                           help


--noise--

Usage: noise [-s d] [-r] [-n f] [--uniform] <input> <output> 

Add noise with selected variance to input.

-s d           random seed initialization. '0' uses the default seed.
-r             real-valued input
-n variance    DEFAULT: 1.0
--uniform      select uniform noise distribution
-h             help


--normalize--

Usage: normalize [-b] flags <input> <output> 

Normalize along selected dimensions.

-b    l1
-h    help


--nrmse--

Usage: nrmse [-t f] [-s] [-S,--scientific] <reference> <input> 

Output normalized root mean square error (NRMSE),
i.e. norm(input - ref) / norm(ref)

-t eps             compare to eps
-s                 automatic (complex) scaling
-S,--scientific    use scientific notation in output
-h                 help


--nufft--

Usage: nufft [-a] [-i] [-x d:d:d] [-t] [-r] [-c] [-l f] [-m d] [-P] [-s] [-g] [-1] [--lowmem] [--zero-mem] [--no-precomp] [-B <file>] [-p <file>] [-o,--oversampling f] [-w,--width f] [-F <file>] [-T <file>] [--nufft-conf ...] <traj> <input> <output> 

Perform non-uniform Fast Fourier Transform.

-a                     adjoint
-i                     inverse
-x x:y:z               dimensions
-t                     Toeplitz embedding for inverse NUFFT
-r                     turn-off Toeplitz embedding for inverse NUFFT
-c                     preconditioning for inverse NUFFT
-l lambda              l2 regularization
-m iter                max. number of iterations (inverse only)
-P                     periodic k-space
-s                     DFT
-g                     GPU
-1                     use/return oversampled grid
--lowmem               use low-mem mode of the nuFFT
--zero-mem             use zero-overhead mode of the nuFFT
--no-precomp           turn off precomputation
-B file                temporal (or other) basis
-p file                weighting of nufft
-o,--oversampling o    oversample grid by factor (default: o=2; required for Toeplitz)
-w,--width w           width of Kaiser-Bessel window (default: w=6)
-F file                b0 inhomogeneity fieldmap
-T file                timemap for conjugate phase reconstruction
--nufft-conf ...       configure nufft
-h                     help


--nufftbase--

Usage: nufftbase dimensions <trajectory> <output> 

Compute the Fourier transform of a basis function to be used in the nuFFT.

-h  help


--onehotenc--

Usage: onehotenc [-r] [-i d] <input> <output> 

Transforms class labels to one-hot-encoded classes


-r          get class label by maximum entry
-i index    select dimension
-h          help


--ones--

Usage: ones dims dim1 ... dimN <output> 

Create an array filled with ones with {dims} dimensions of size {dim1} to {dimn}.

-h  help


--pattern--

Usage: pattern [-s d] <kspace> <pattern> 

Compute sampling pattern from kspace

-s bitmask    Squash dimensions selected by bitmask
-h            help


--phantom--

Usage: phantom [-k] [-x d] [-s d] [-t <file>] [--coil ...] [-3] [-2] [-b] [-r d] [--rotation-angle f] [--rotation-steps d] [-S d] [-G] [-T] [--NIST] [--SONAR] [--BRAIN] [-N d] [-g d] [--ELLIPSOID] [--ellipsoid_center d:d:d] [--ellipsoid_axes f:f:f] [--FILE <file>] [--stl <file>] <output> 

Image and k-space domain phantoms.

-k                          k-space
-x n                        dimensions in y and z
-s nc                       nc sensitivities
-t file                     trajectory
--coil ...                  configure type of coil
-3                          3D simulation
-2                          enforce 2D simulation
-b                          basis functions for geometry
-r d                        random seed initialization. '0' uses the default seed.
--rotation-angle [deg]      Angle of rotation
--rotation-steps n          Number of rotation steps

-S nc                       Output nc sensitivities
-G                          geometric object phantom
-T                          tubes phantom
--NIST                      NIST phantom (T2 sphere)
--SONAR                     Diagnostic Sonar phantom
--BRAIN                     BRAIN geometry phantom
-N num                      Random tubes phantom with num tubes
-g n=1,2,3                  select geometry for object phantom
--ELLIPSOID                 Ellipsoid.
--ellipsoid_center d:d:d    x,y,z center coordinates of ellipsoid.
--ellipsoid_axes f:f:f      Axes lengths of ellipsoid.
--FILE name                 Arbitrary geometry based on multicfl file.
--stl file                  path to stl file
-h                          help


--phasepole--

Usage: phasepole [-e,--estimate] [-s,--sample] [--espirit] [-x,--dims d:d:d] [-c <file>] [-t f] [-d f] [--center f:f:f] [-n d] [<input (coils/singularity positions)>] <output (sampled phase/singularity positions)> 

Detect and sample phase poles.

-e,--estimate      Estimate phase poles (input is sensitivity maps / output is singularity positions)
-s,--sample        Sample phase poles (input is position / phase)
--espirit          Use ESPIRiT mode (diameter=1, no closing)
-x,--dims x:y:z    Explicitly specify image dimensions
-c <curl>          output curl map
-t thresh          threshold for pole detection
-d diameter        diameter of curve integral to detect poles (in FoV)
--center x:y:z     specify position of phase pole to sample
-n normal          normal direction for 2D pole detection (0,1,2)
-h                 help


--pics--

Usage: pics [-l ...] [-r f] [-R ...] [-c] [-s f] [-i d] [-t <file>] [-n] [-N] [-g] [--gpu-gridding] [-p <file>] [--precond] [-b d] [-e] [--adaptive-stepsize] [--asl] [--teasl] [--theta f:f] [--tvscales [f:]*f] [--tvscales2 [f:]*f] [--alpha f:f] [--gamma f:f] [-W <file>] [-d d] [-u f] [-C d] [-f f] [-I,--ist] [--fista] [--eulermaruyama] [-m,--admm] [-a,--pridu] [-w f] [-S] [--shared-img-dims d] [-K] [-B <file>] [-P f] [-M] [-U,--lowmem] [--psf_export <file>] [--psf_import <file>] [--wavelet <string>] [--fista_pqr f:f:f] [--ist_last] [--motion-field <file>] [--nufft-conf ...] <kspace> <sensitivities> <output> 

Parallel-imaging compressed-sensing reconstruction.


-l 1/-l2                    toggle l1-wavelet or l2 regularization.
-r lambda                  regularization parameter
-R <T>:A:B:C               generalized regularization options (-Rh for help)
-c                         real-value constraint
-s step                    iteration stepsize
-i iter                    max. number of iterations
-t file                    k-space trajectory
-n                         disable random wavelet cycle spinning
-N                         do fully overlapping LLR blocks
-g                         use GPU
--gpu-gridding             use GPU for gridding
-p file                    pattern or weights
--precond                  interpret weights as preconditioner
-b blk                     Lowrank block size
-e                         Scale stepsize based on max. eigenvalue
--adaptive-stepsize        PRIDU adaptive step size
--asl                      ASL reconstruction
--teasl                    Time-encoded ASL reconstruction
--theta theta1:theta2      PWI weight for ASL reconstruction
--tvscales [f:]*f          Scaling of derivatives in TV or TGV regularization
--tvscales2 [f:]*f         Scaling of secondary derivatives in ICTV reconstruction
--alpha alpha1:alpha0      regularization parameter for TGV and ICTGV reconstruction
--gamma gamma1:gamma2      regularization parameter for ICTV and ICTGV reconstruction
-W <img>                   Warm start with <img>
-d level                   Debug level
-u rho                     ADMM rho
-C iter                    ADMM max. CG iterations
-f rfov                    restrict FOV
-I,--ist                   select IST
--fista                    select FISTA
--eulermaruyama            select Euler Maruyama
-m,--admm                  select ADMM
-a,--pridu                 select Primal Dual
-w f                       inverse scaling of the data
-S                         re-scale the image after reconstruction
--shared-img-dims flags    deselect image dims with flags
-K                         randshift for NUFFT
-B file                    temporal (or other) basis
-P eps                     Basis Pursuit formulation, || y- Ax ||_2 <= eps
-M                         Simultaneous Multi-Slice reconstruction
-U,--lowmem                Use low-mem mode of the nuFFT
--psf_export file          Export PSF to file
--psf_import file          Import PSF from file
--wavelet name             wavelet type (haar,dau2,cdf44)
--fista_pqr p:q:r          parameters for FISTA acceleration
--ist_last                 end iteration with call to data consistency
--motion-field file        motion field
--nufft-conf ...           configure nufft
-h                         help


--pocsense--

Usage: pocsense [-i d] [-r f] [-l d] <kspace> <sensitivities> <output> 

Perform POCSENSE reconstruction.

-i iter     max. number of iterations
-r alpha    regularization parameter
-l 1/-l2    toggle l1-wavelet or l2 regularization
-h          help


--poisson--

Usage: poisson [-Y d] [-Z d] [-y f] [-z f] [-C d] [-v] [-e] [-s d] <output> 

Computes Poisson-disc sampling pattern.

-Y size    size dimension 1
-Z size    size dimension 2
-y acc     acceleration dim 1
-z acc     acceleration dim 2
-C size    size of calibration region
-v         variable density
-e         elliptical scanning
-s d       random seed initialization. '0' uses the default seed.
-h         help


--pol2mask--

Usage: pol2mask [-X d] [-Y d] <poly> <output> 

Compute masks from polygons.

-X size    size dimension 0
-Y size    size dimension 1
-h         help


--poly--

Usage: poly L N a_1 ... a_N <output> 

Evaluate polynomial p(x) = a_1 + a_2 x + a_3 x^2 ... a_(N+1) x^N at x = {0, 1, ... , L - 1} where a_i are floats.

-h  help


--psf--

Usage: psf [--oversampled] [--oversampled-decomposed] <trajectory> <psf> 

Calculate point-spread-function (PSF) of given trajectory.


--oversampled               oversampled psf with linphases in dim 15
--oversampled-decomposed    oversampled psf with linphases in dim 15
-h                          help


--pulse--

Usage: pulse [--sinc] [--sms] [--rect] [--hypsec] [--arb] [--gauss] [--dur f] [--fa f] [--bwtp f] [--mb d] [--sms-dist f] [--slice-th f] [--N d] <Signal: Bxy> 

Pulse generation tool

--sinc               sinc
--sms                sms
--rect               rect
--hypsec             hypersecant
--arb                arbitrary (OC CEST saturation pulse)
--gauss              gauss
--dur double         pulse duration [s]
--fa double          flip angle [deg]
--bwtp double        bandwidth time product
--mb long            SMS multi-band factor
--sms-dist long      center-to-center slice distance between SMS partitions [m]
--slice-th double    slice thickness [m]
--N int              number of time-steps (default = 1e6 * dur)
-h                   help


--raga--

Usage: raga [-s,--tiny-angle d] [-r,--raga-inc d] [-e,--echoes d] [-m,--slices d] [-z,--partitions d] [-i,--inversions d] [-c,--shifts d] [--no-double-base] spokes <output> 

Generate file with RAGA indices for given approximated tiny golden ratio angle/raga increment and full frame spokes.

-s,--tiny-angle # Tiny GA    tiny (small) golden ratio angle
-r,--raga-inc d              Increment of RAGA Sampling
-e,--echoes m                Number of (non-aligned) echoes
-m,--slices m                Number of (non-aligned) slices
-z,--partitions m            Number of (non-aligned) partitions
-i,--inversions i            Number of (non-aligned) inversions
-c,--shifts c                Number of (non-aligned) shifts
--no-double-base             Define GA over Pi base instead of 2Pi.
-h                           help


--reconet--

Usage: reconet [-t,--train] [-e,--eval] [-a,--apply] [-g,--gpu] [-l,--load <file>] [-b,--batch-size d] [-I,--iterations d] [-n,--normalize] [-N,--network ...] [--resnet-block ...] [--varnet-block ...] [--tensorflow ...] [--data-consistency ...] [--initial-reco ...] [--shared-weights] [--no-shared-weights] [--shared-lambda] [--no-shared-lambda] [--rss-norm] [--trajectory <file>] [--pattern <file>] [--nufft-conf ...] [--mask <file>] [--valid-data ...] [--train-loss ...] [--valid-loss ...] [-T,--train-algo ...] [--adam ...] [--iPALM ...] [--load-memory] [--lowmem] [--test] [--export-graph <string>] [--ksp-training] [--no-precomp] <kspace> <sensitivities> <weights> <ref/out> 

Trains or applies a neural network for reconstruction.

-t,--train                   train reconet
-e,--eval                    evaluate reconet
-a,--apply                   apply reconet
-g,--gpu                     run on gpu
-l,--load <weights-init>     load weights for continuing training
-b,--batch-size d            size of mini batches
-I,--iterations d            number of unrolled iterations
-n,--normalize               normalize data with maximum magnitude of adjoint reconstruction
-N,--network ...             select neural network
--resnet-block ...           configure residual block
--varnet-block ...           configure variational block
--tensorflow ...             configure tensorflow as network
--data-consistency ...       configure data-consistency method
--initial-reco ...           configure initialization
--shared-weights             share weights across iterations
--no-shared-weights          share weights across iterations
--shared-lambda              share lambda across iterations
--no-shared-lambda           share lambda across iterations
--rss-norm                   scale output image to rss normalization
--trajectory <traj>          trajectory
--pattern <pattern>          sampling pattern / psf in kspace
--nufft-conf ...             configure nufft
--mask <mask>                mask for computation of loss
--valid-data ...             provide validation data
--train-loss ...             configure the training loss
--valid-loss ...             configure the validation loss
-T,--train-algo ...          configure general training parameters
--adam ...                   configure Adam
--iPALM ...                  configure iPALM
--load-memory                copy training data into memory
--lowmem                     reduce memory usage by checkpointing
--test                       very small network for tests
--export-graph <file.dot>    export graph for visualization
--ksp-training               Train network on k-space data
--no-precomp                 Don't precompute adjoint and psf
-h                           help


--repmat--

Usage: repmat dimension repetitions <input> <output> 

Repeat input array multiple times along a certain dimension.

-h  help


--reshape--

Usage: reshape [-s d] flags dim1 ... dimN <input> <output> 

Reshape selected dimensions.

-s flags    stream flagged dims
-h          help


--resize--

Usage: resize [-c] [-f] dim1 size1 ... dimN sizeN <input> <output> 

Resizes an array along dimensions to sizes by truncating or zero-padding. Please see doc/resize.txt for examples.

-c    center
-f    front
-h    help


--rmfreq--

Usage: rmfreq [-N d] [-M <file>] <traj> <k> <k_cor> 

Remove angle-dependent frequency

-N #       Number of harmonics [Default: 5]
-M file    Contrast modulation file
-h         help


--rof--

Usage: rof lambda flags <input> <output> 

Perform total variation denoising along dims <flags>.

-h  help


--roistat--

Usage: roistat [-b] [-C] [-S] [-M] [-D] [-E] [-V] <roi> <input> [<output>] 

Compute ROI statistics.

-b    Bessel's correction, i.e. 1 / (n - 1)
-C    voxel count
-S    sum
-M    mean
-D    standard deviation
-E    energy
-V    variance
-h    help


--rovir--

Usage: rovir <positive signal> <negative negative> <orthogonal transform> 

Compute coil compression matrix using ROVir.

-h  help


--rss--

Usage: rss bitmask <input> <output> 

Calculates root of sum of squares along selected dimensions.

-h  help


--rtnlinv--

Usage: rtnlinv [-i d] [-d d] [-c] [-N] [-m d] [-U] [-f f] [-p <file>] [-t <file>] [-I <file>] [-g] [-S] [-T f] [-x d:d:d] <kspace> <output> [<sensitivities>] 

Jointly estimate a time-series of images and sensitivities with nonlinear
inversion using {iter} iteration steps. Optionally outputs
the sensitivities.

-i iter         Number of Newton steps
-d level        Debug level
-c              Real-value constraint
-N              Do not normalize image with coil sensitivities
-m nmaps        Number of ENLIVE maps to use in reconstruction
-U              Do not combine ENLIVE maps in output
-f FOV          restrict FOV
-p file         pattern / transfer function
-t file         kspace trajectory
-I file         File for initialization
-g              use gpu
-S              Re-scale image after reconstruction
-T temp_damp    temporal damping [default: 0.9]
-x x:y:z        Explicitly specify image dimensions
-h              help


--sake--

Usage: sake [-i d] [-s f] <kspace> <output> 

Use SAKE algorithm to recover a full k-space from undersampled
data using low-rank matrix completion.

-i iter    number of iterations
-s size    rel. size of the signal subspace
-h         help


--sample--

Usage: sample [--dims d0:d1:...:dN] [-g] [-s d] [-r,--real-valued] [-a,--ancestral] [-p,--predictor-corrector] [--sigma ...] [--gmm ...] [--cunet ...] [--external-graph <string>] [--mask <file>] [--gamma f] [-N d] [-K d] [-S d] [--save-mod d] [--posterior ...] [--nufft-conf ...] <samples> [<denoised samples (i.e. mmse estimate)>] 

Prior sampling with given diffusion network (either PyTorch or TensorFlow) which is trained as denoiser (i.e. outputs the expectation) or Gaussian Mixture Model using unadjusted Langevin algorithm.


--dims d0:d1:...:dN         image dimensions
-g                          use gpu
-s s                        seed
-r,--real-valued            real-valued trained network (i.e. with z ~ CN(0, 2I))
-a,--ancestral              ancestral sampling
-p,--predictor-corrector    predictor-corrector sampling
--sigma ...                 select noise schedule for decreasing noise
--gmm ...                   generate a Gaussian mixture model for sampling
--cunet ...                 sampling with conditional unet
--external-graph weights    .pt or .tf file with weights
--mask file                 FoV mask for output of network
--gamma gamma               scaling of stepsize for Langevin iteration
-N N                        number of noise levels
-K K                        number of Langevin steps per level
-S S                        number of samples drawn
--save-mod S                save samples every S steps
--posterior ...             sample posterior
--nufft-conf ...            configure nufft
-h                          help


--saxpy--

Usage: saxpy scale <input1> <input2> <output> 

Multiply input1 with scale factor and add input2.

-h  help


--scale--

Usage: scale factor <input> <output> 

Scale array by {factor}. The scale factor can be a complex number.

-h  help


--sdot--

Usage: sdot <input1> <input2> 

Compute dot product along selected dimensions.

-h  help


--seq--

Usage: seq [-d f] [-N d] [-s f:f:f] [-S f:f:f] [--dist f] [--no-spoiling] [--spoiled] [--FOV f] [--BR d] [--slice_thickness f] [--FA f] [--TR f] [--TE f] [--TE_delta f] [--BWTP f] [--rf_duration f] [--dwell f] [--os f] [--pe_mode d] [--turn] [--mems] [--raga] [--raga_flags d] [--chrono] [-R <file>] [--tiny d] [-e,--echoes d] [-r,--lines d] [-z,--partitions d] [-t,--measurements d] [-f,--raga_full_frames d] [-m,--slices d] [-i,--inversions d] [--sequential-multislice] [--avg-inner] [--mb_factor d] [--sms_distance f] [--IR_NON] [--TI f] [--init_delay f] [--inv_delay f] [--gradient-normal] [--gradient-whisper] [--CUSTOM_LONG d0:d1:...:dN] [--CUSTOM_DOUBLE [f:]*f] [--LOOP d0:d1:...:dN] [<0th moment (x,y,z) at sample points, sample_points, phase of adc>] [<gradients (x,y,z) per imaging block>] [<0th moment (x,y,z) per imaging block>] [<pulseq file>] 

Computes a GRE sequence.

-d dt                                     time-increment per sample (default: seq->conf->phys.tr / 1000)
-N samples                                Number of samples (default: 1000)
-s RO:PE:SL                               FOV shift
-S RO:PE:SL                               relative FOV shift
--dist dist                               slice distance factor [1 / slice_thickness] (default: 1.)
--no-spoiling                             spoiling off (default: rf random)
--spoiled                                 RF_SPOILED (inc: 50 deg, gradient on) (default: rf random)
--FOV FOV                                 Field Of View
--BR BR                                   Base Resolution
--slice_thickness slice_thickness         Slice thickness
--FA flip angle                           Flip angle [deg]
--TR TR                                   TR
--TE TE                                   TE
--TE_delta TE_delta                       TE_delta
--BWTP BWTP                               Bandwidth Time Product
--rf_duration rf_duration                 RF pulse duration
--dwell dwell                             Dwell time
--os os                                   Oversampling factor
--pe_mode pe_mode                         Phase-encoding mode
--turn                                    turn-based PE (default: RAGA)
--mems                                    multi-echo/multi-spoke PE (default: RAGA)
--raga                                    RAGA PE
--raga_flags raga_aligned_flags           RAGA aligned flags (by bitmask)
--chrono                                  save gradients/moments/sampling in chronological order (RAGA)
-R file                                   raga indices
--tiny tiny                               Tiny golden-ratio index
-e,--echoes echoes                        Number of echoes
-r,--lines lines                          Number of phase encoding lines
-z,--partitions partitions                Number of partitions (3D) or SMS groups (2D)
-t,--measurements measurements            Number of measurements / frames (RAGA: total number of spokes)
-f,--raga_full_frames raga_full_frames    Number of full frames (only RAGA)
-m,--slices slices                        Number of slices of multiband factor (SMS)
-i,--inversions inversions                Number of inversions
--sequential-multislice                   seq_order: sequential multislice (default: avg outer)
--avg-inner                               seq_order: average inner (default: avg outer)
--mb_factor mb_factor                     Multi-band factor
--sms_distance sms_distance               SMS slice distance
--IR_NON                                  Magn. preparation: Nonselective Inversion (default: off)
--TI TI                                   Inversion time
--init_delay init_delay                   Initial delay of measurement
--inv_delay inv_delay_time                Inversion delay time
--gradient-normal                         Gradient normal mode (default: fast)
--gradient-whisper                        Gradient whispher mode (default: fast)
--CUSTOM_LONG d0:d1:...:dN                custom long parameters
--CUSTOM_DOUBLE [f:]*f                    custom double parameters
--LOOP d0:d1:...:dN                       sequence loop dimensions
-h                                        help


--show--

Usage: show [-m] [-d d] [-R] [-N] [-s <string>] [-f <string>] <input> 

Outputs values or meta data.

-m           show meta data
-d dim       show size of dimension
-R           print only real part
-N           print index
-s sep       use <sep> as the separator
-f format    use <format> as the format. Default: "%+.6e%+.6ei"
-h           help


--signal--

Usage: signal [-F] [-B] [-T] [-S] [-M] [-G] [-C] [-A] [--fat] [-I] [-s] [--short-TR-LL-approx] [-0 f:f:f] [-1 f:f:f] [-2 f:f:f] [-3 f:f:f] [-4 f:f:f] [-5 f:f:f] [-6 f:f:f] [-r f] [-e f] [-i f] [-f f] [-d f] [-t f] [-n d] [-b d] [--av-spokes d] [-m d] [--freq-samples d] [--pulsed] [-l f] [-a f] [--acquisition-only] <basis-functions> 

Analytical simulation tool.

-F                      FLASH
-B                      bSSFP
-T                      TSE
-S                      SE
-M                      MOLLI
-G                      MGRE
-C                      IR MGRE
-A                      ASL
--fat                   Simulate additional fat component.
-I                      inversion recovery
-s                      inversion recovery starting from steady state
--short-TR-LL-approx    Short TR approximation for analytical LL model.
-0 min:max:N            range of off-resonance frequency [Hz]
-1 min:max:N            range of T1s [s]
-2 min:max:N            range of T2s [s]
-3 min:max:N            range of Mss
-4 min:max:N            range of T1 values for fat [s]
-5 min:max:N            range of FA values [°]
-6 min:max:N            range of CBF values [ml/100g/min]
-r TR                   repetition time
-e TE                   echo time
-i TI                   inversion time
-f FA                   flip angle
-d FF                   fat fraction
-t T1 relax             T1 relax period (second) for MOLLI
-n n                    number of measurements
-b heart beats          number of heart beats for MOLLI
--av-spokes d           Number of averaged consecutive spokes
-m multi echos          number of multi gradient echos
--freq-samples d        Samples in frequency-/z-domain for FSE model based on generating function formalism
--pulsed                Pulsed Arterial Spin Labeling
-l lambda               Blood-brain partition coefficient
-a Delta t              Arterial transit time (ATT) in Buxton model
--acquisition-only      only return the Buxton model signal during image acquisition
-h                      help


--sim--

Usage: sim [-1,--T1 f:f:f] [-2,--T2 f:f:f] [--BLOCH] [--BMC] [--ROT] [--ODE] [--STM] [--split-dim] [--seq ...] [--other ...] [--pool ...] [--CEST ...] <signal: Mxy> [<Partial derivatives: dR1, dM0, dR2, dB1>] 

simulation tool

-1,--T1 min:max:N    range of T1 values
-2,--T2 min:max:N    range of T2 values
--BLOCH              Bloch Equations (default)
--BMC                Bloch-McConnell Equations
--ROT                discretized simulation based on rotational matrices
--ODE                ordinary differential equation solver (default)
--STM                solver based on state-transition matrices
--split-dim          split magnetization into x, y, and z component
--seq ...            configure sequence parameter
--other ...          configure other parameters
--pool ...           configure parameters for 2nd->5th pool
--CEST ...           configure parameters for CEST
-h                   help


--slice--

Usage: slice dim1 pos1 ... dimN posN <input> <output> 

Extracts a slice from positions along dimensions.

-h  help


--spow--

Usage: spow exponent <input> <output> 

Raise array to the power of {exponent}. The exponent can be a complex number.

-h  help


--sqpics--

Usage: sqpics [-l ...] [-r f] [-R ...] [-s f] [-i d] [-t <file>] [-n] [-g] [-p <file>] [-b d] [-e] [-W <file>] [-d d] [-u f] [-C d] [-f f] [-m] [-w f] [-S] <kspace> <sensitivities> <output> 

Parallel-imaging compressed-sensing reconstruction.

-l 1/-l2         toggle l1-wavelet or l2 regularization.
-r lambda       regularization parameter
-R <T>:A:B:C    generalized regularization options (-Rh for help)
-s step         iteration stepsize
-i iter         max. number of iterations
-t file         k-space trajectory
-n              disable random wavelet cycle spinning
-g              use GPU
-p file         pattern or weights
-b blk          Lowrank block size
-e              Scale stepsize based on max. eigenvalue
-W <img>        Warm start with <img>
-d level        Debug level
-u rho          ADMM rho
-C iter         ADMM max. CG iterations
-f rfov         restrict FOV
-m              Select ADMM
-w val          scaling
-S              Re-scale the image after reconstruction
-h              help


--squeeze--

Usage: squeeze <input> <output> 

Remove singleton dimensions of array.

-h  help


--ssa--

Usage: ssa [-w d] [-z] [-m d] [-n d] [-r d] [-g d] <src> <EOF> [<S>] [<backprojection>] 

Perform SSA-FARY or Singular Spectrum Analysis. <src>: [samples, coordinates]

-w window     Window length
-z            Zeropadding [Default: True]
-m 0/1        Remove mean [Default: True]
-n 0/1        Normalize [Default: False]
-r rank       Rank for backprojection. r < 0: Throw away first r components. r > 0: Use only first r components.
-g bitmask    Bitmask for Grouping (long value!)
-h            help


--std--

Usage: std bitmask <input> <output> 

Compute standard deviation along selected dimensions specified by the {bitmask}

-h  help


--stl--

Usage: stl [--input <file>] [--model ...] [-s f] [-m f:f:f] [--stat] [--binary] <output> 

Read and write stl files with '.stl' or cfl fileformat.

--input <file>    Path to input file (.stl or cfl file format).
--model <tag>     Internal stl model (help: bart stl --model h).

-s scale          Multiplicate all coordinates of model with a scale factor.
-m move           Move model by vector.

--stat            Show statistics of model.
--binary          Output STL files in binary format.
-h                help


--svd--

Usage: svd [-e] <input> <U> <S> <VH> 

Compute singular-value-decomposition (SVD).

-e    econ
-h    help


--tee--

Usage: tee [-i,--in <string>] [--out0 <file>] [-t] [-n] [-a] [-b] [write to files (and stdout)1 ... write to files (and stdout)N ] 

Copy stdin to stdout + given output files.

-i,--in data    Input File (instead of stdin)
--out0 meta     Output file which receives only metadata
-t              print time between inputs
-n              No stdout
-a              Abort program on disappearing inputs/outputs.
-b              Create binary output stream
-h              help


--tensorflow--

Usage: tensorflow [-b d] [-n] [-g] TensorFlow Graph [<Arguments>1> ... <Arguments>N> ] 

Load Tensorflow Graph

-b b    Fill placeholder in dims with b
-n      Print all nodes in graph
-g      Use gpu
-h      help


--tgv--

Usage: tgv [--tvscales [f:]*f] [--alpha f:f] lambda flags <input> <output> 

Perform total generalized variation denoising along dims specified by flags.

--tvscales [f:]*f        Scaling of derivatives
--alpha alpha1:alpha0    alpha1 * || grad x - z ||_1, alpha0 * || Eps z ||_1
-h                       help


--threshold--

Usage: threshold [-H] [-N] [-W] [-L] [-D] [-B] [-M] [-E] [-j d] [-b d] lambda <input> <output> 

Perform (soft) thresholding with parameter lambda.

-H              hard thresholding
-N              hard thresholding (val<lambda)
-W              daubechies wavelet soft-thresholding
-L              locally low rank soft-thresholding
-D              divergence-free wavelet soft-thresholding
-B              thresholding with binary output where (val>lambda)
-M              thresholding with binary output where (val<lambda)
-E              thresholding with binary output where (val=lambda)
-j bitmask      joint soft-thresholding
-b blocksize    locally low rank block size
-h              help


--toimg--

Usage: toimg [-g f] [-c f] [-w f] [-d] [-m] [-W] [-D] <input> <output prefix> 

Create magnitude images as png or proto-dicom.
The first two non-singleton dimensions will
be used for the image, and the other dimensions
will be looped over.

-g gamma       gamma level
-c contrast    contrast level
-w window      window level
-d             write to dicom format (deprecated, use extension .dcm)
-m             re-scale each image
-W             use dynamic windowing
-D             Include dimensions in output filenames
-h             help


--toraw--

Usage: toraw [-b,--bitwidth d] [-n,--normalize] [-c,--complex] <input> 

Output BART file as raw samples.

-b,--bitwidth bitwidth    Number of bits (default: 8)
-n,--normalize            Normalize input
-c,--complex              Complex output
-h                        help


--traj--

Usage: traj [-x d] [-y d] [-z d] [-d d] [-e d] [-a d] [-t d] [-m d] [-l] [-r] [-G] [-H] [-s d] [-A] [-D] [--double-base] [-o f] [-R f] [-q f:f:f] [-O] [-3] [-c] [-E] [--mems-legacy] [-C <file>] [--raga-inc d] <output> 

Computes k-space trajectories.

-x x             readout samples
-y y             phase encoding lines
-z z             second phase encoding lines
-d d             full readout samples
-e e             number of echoes
-a a             acceleration
-t t             conf.turns
-m mb            SMS multiband factor
-l               aligned partition angle
-r               radial
-G               golden-ratio sampling
-H               half-circle golden-ratio sampling
-s # tiny GA     tiny golden angle
-A               rational approximation of golden angles
-D               projection angle in [0,360°), else in [0,180°)
--double-base    define GA over 2Pi base instead of default Pi.
-o o             oversampling factor
-R phi           rotate [°]
-q delays        gradient delays: y, x, yx
-O               correct transverse gradient error for radial tajectories
-3               3D
-c               asymmetric trajectory [DC sampled]
-E               multi-echo multi-spoke trajectory
--mems-legacy    OLD multi-echo multi-spoke trajectory
-C file          custom_angle file [phi + i * psi]
--raga-inc d     increment of RAGA Sampling
-h               help


--trajcor--

Usage: trajcor [-q f:f:f] [-V <file>] [-O] <traj> <output traj> 

Correct delays for a given trajectory.


-q delays    gradient delays: y, x, xy
-V file      custom_gdelays
-O           correct transverse gradient error for radial trajectories
-h           help


--transpose--

Usage: transpose dim1 dim2 <input> <output> 

Transpose dimensions {dim1} and {dim2}.

-h  help


--twixread--

Usage: twixread [-x d] [-r d] [-y d] [-z d] [-s d] [-v d] [-c d] [-n d] [-p d] [-f d] [-i d] [-e d] [-a d] [-A] [-L] [-P] [-N] [-R] [-S] [-I d] [-C] [--rational] [-M] [--bin d] [-X] [-d d] <dat file> <output> [<pmu>] 

Read data from Siemens twix (.dat) files.

-x X          number of samples (read-out)
-r R          radial lines
-y Y          phase encoding steps
-z Z          partition encoding steps
-s S          number of slices
-v V          number of averages
-c C          number of channels
-n N          number of repetitions
-p P          number of cardiac phases
-f F          number of flow encodings
-i I          number inversion experiments
-e E          number of echoes
-a A          total number of ADCs
-A            automatic [guess dimensions]
-L            use linectr offset
-P            use partctr offset
-N            only get noise
-R            get data of reference scan
-S            don't include reference lines
-I flags      ignore (squash) selected dimensions (defaults to LEVEL_FLAG)
-C            read data chronologically and ignore adc postitions
--rational    Rational Approximation Sampling
-M            MPI mode
--bin d       Binning of spokes for RAGA sampled data
-X            no consistency check for number of read acquisitions
-d level      Debug level
-h            help


--unwrap--

Usage: unwrap [-b f] dim <input> <output> 

Unwrap along selected dimensions.

-b bounds    bounds (default: PI)
-h           help


--upat--

Usage: upat [-Y d] [-Z d] [-y d] [-z d] [-c d] <output> 

Create a sampling pattern.

-Y Y      size Y
-Z Z      size Z
-y uy     undersampling y
-z uz     undersampling z
-c cen    size of k-space center
-h        help


--var--

Usage: var bitmask <input> <output> 

Compute variance along selected dimensions specified by the {bitmask}

-h  help


--vec--

Usage: vec val1 ... valN <output> 

Create a vector of values.

-h  help


--version--

Usage: version [-t <string>] [-V] 

Print BART version. The version string is of the form
TAG or TAG-COMMITS-SHA as produced by 'git describe'. It
specifies the last release (TAG), and (if git is used)
the number of commits (COMMITS) since this release and
the abbreviated hash of the last commit (SHA). If there
are local changes '-dirty' is added at the end.

-t version    Check minimum version
-V            Output verbose info
-h            help


--walsh--

Usage: walsh [-r d:d:d] [-b d:d:d] <input> <output> 

Estimate coil sensitivities using walsh method (use with ecaltwo).

-r cal_size      Limits the size of the calibration region.
-b block_size    Block size.
-h               help


--wave--

Usage: wave [-r f] [-b d] [-i d] [-s f] [-c f] [-t f] [-e f] [-g] [-f] [-H] [-v] [-w] [-l] <maps> <wave> <kspace> <output> 

Perform a wave-caipi reconstruction.

Conventions:
  * (sx, sy, sz) - Spatial dimensions.
  * wx           - Extended FOV in READ_DIM due to
                   wave's voxel spreading.
  * (nc, md)     - Number of channels and ESPIRiT's 
                   extended-SENSE model operator
                   dimensions (or # of maps).
Expected dimensions:
  * maps    - ( sx, sy, sz, nc, md)
  * wave    - ( wx, sy, sz,  1,  1)
  * kspace  - ( wx, sy, sz, nc,  1)
  * output  - ( sx, sy, sz,  1, md)

-r lambda    Soft threshold lambda for wavelet or locally low rank.
-b blkdim    Block size for locally low rank.
-i mxiter    Maximum number of iterations.
-s stepsz    Step size for iterative method.
-c cntnu     Continuation value for IST/FISTA.
-t toler     Tolerance convergence condition for iterative method.
-e eigvl     Maximum eigenvalue of normal operator, if known.
-g           use GPU
-f           Reconstruct using FISTA instead of IST.
-H           Use hogwild in IST/FISTA.
-v           Split result to real and imaginary components.
-w           Use wavelet.
-l           Use locally low rank across the real and imaginary components.
-h           help


--wavelet--

Usage: wavelet [-a] [-H] [-D] [-C] bitmask [dim1 ... dimN ] <input> <output> 

Perform wavelet transform.

-a    adjoint (specify dims)
-H    type: Haar
-D    type: Dau2
-C    type: CDF44
-h    help


--wavepsf--

Usage: wavepsf [-c] [-x d] [-y d] [-r f] [-a d] [-t f] [-g f] [-s f] [-n d] <output> 

Generate a wave PSF in hybrid space.
- Assumes the first dimension is the readout dimension.
- Only generates a 2 dimensional PSF.
- Use reshape and fmac to generate a 3D PSF.

3D PSF Example:
bart wavepsf		-x 768 -y 128 -r 0.1 -a 3000 -t 0.00001 -g 0.8 -s 17000 -n 6 wY
bart wavepsf -c -x 768 -y 128 -r 0.1 -a 3000 -t 0.00001 -g 0.8 -s 17000 -n 6 wZ
bart reshape 7 wZ 768 1 128 wZ wZ
bart fmac wY wZ wYZ

-c           Set to use a cosine gradient wave
-x RO_dim    Number of readout points
-y PE_dim    Number of phase encode points
-r PE_res    Resolution of phase encode in cm
-a ADC_T     Readout duration in microseconds.
-t ADC_dt    ADC sampling rate in seconds
-g gMax      Maximum gradient amplitude in Gauss/cm
-s sMax      Maximum gradient slew rate in Gauss/cm/second
-n ncyc      Number of cycles in the gradient wave
-h           help


--whiten--

Usage: whiten [-o <file>] [-c <file>] [-n] <input> <ndata> <output> [<optmat_out>] [<covar_out>] 

Apply multi-channel noise pre-whitening on <input> using noise data <ndata>.
Optionally output whitening matrix and noise covariance matrix

-o <optmat_in>    use external whitening matrix <optmat_in>
-c <covar_in>     use external noise covariance matrix <covar_in>
-n                normalize variance to 1 using noise data <ndata>
-h                help


--window--

Usage: window [-H] flags <input> <output> 

Apply Hamming (Hann) window to <input> along dimensions specified by flags

-H    Hann window
-h    help


--wshfl--

Usage: wshfl [-R ...] [-b d] [-i d] [-j d] [-s f] [-e f] [-F <file>] [-O <file>] [-t f] [-g] [-K] [-H] [-v] <maps> <wave> <phi> <reorder> <table> <output> 

Perform a wave-shuffling reconstruction.

Conventions:
  * (sx, sy, sz) - Spatial dimensions.
  * wx           - Extended FOV in READ_DIM due to
                   wave's voxel spreading.
  * (nc, md)     - Number of channels and ESPIRiT's 
                   extended-SENSE model operator
                   dimensions (or # of maps).
  * (tf, tk)     - Turbo-factor and the rank
                   of the temporal basis used in
                   shuffling.
  * ntr          - Number of TRs, or the number of
                   (ky, kz) points acquired of one
                   echo image.
  * n            - Total number of (ky, kz) points
                   acquired. This is equal to the
                   product of ntr and tf.

Descriptions:
  * reorder is an (n by 3) index matrix such that
    [ky, kz, t] = reorder(i, :) represents the
    (ky, kz) kspace position of the readout line
    acquired at echo number (t), and 0 <= ky < sy,
    0 <= kz < sz, 0 <= t < tf).
  * table is a (wx by nc by n) matrix such that
    table(:, :, k) represents the kth multichannel
    kspace line.

Expected dimensions:
  * maps    - (   sx, sy, sz, nc, md,  1,  1)
  * wave    - (   wx, sy, sz,  1,  1,  1,  1)
  * phi     - (    1,  1,  1,  1,  1, tf, tk)
  * output  - (   sx, sy, sz,  1, md,  1, tk)
  * reorder - (    n,  3,  1,  1,  1,  1,  1)
  * table   - (   wx, nc,  n,  1,  1,  1,  1)

-R <T>:A:B:C    Generalized regularization options. (-Rh for help)
-b blkdim       Block size for locally low rank.
-i mxiter       Maximum number of iterations.
-j cgiter       Maximum number of CG iterations in ADMM.
-s admrho       ADMM Rho value.
-e eigval       Eigenvalue to scale step size. (Optional.)
-F frwrd        Go from shfl-coeffs to data-table. Pass in coeffs path.
-O initl        Initialize reconstruction with guess.
-t toler        Tolerance convergence condition for FISTA.
-g              Use GPU.
-K              Go from data-table to shuffling basis k-space.
-H              Use hogwild.
-v              Split coefficients to real and imaginary components.
-h              help


--zeros--

Usage: zeros dims dim1 ... dimN <output> 

Create a zero-filled array with {dims} dimensions of size {dim1} to {dimn}.

-h  help


--zexp--

Usage: zexp [-i] <input> <output> 

Point-wise complex exponential.

-i    imaginary
-h    help
