interfaces.niftyreg.reg¶
RegAladin¶
Wraps command reg_aladin
Interface for executable reg_aladin from NiftyReg platform.
Block Matching algorithm for symmetric global registration. Based on Modat et al., “Global image registration using asymmetric block-matching approach” J. Med. Img. 1(2) 024003, 2014, doi: 10.1117/1.JMI.1.2.024003
Examples¶
>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegAladin()
>>> node.inputs.ref_file = 'im1.nii'
>>> node.inputs.flo_file = 'im2.nii'
>>> node.inputs.rmask_file = 'mask.nii'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_aladin -aff im2_aff.txt -flo im2.nii -omp 4 -ref im1.nii -res im2_res.nii.gz -rmask mask.nii'
Inputs:
[Mandatory]
flo_file: (an existing file name)
The input floating/source image
flag: -flo %s
ref_file: (an existing file name)
The input reference/target image
flag: -ref %s
[Optional]
aff_direct_flag: (a boolean)
Directly optimise the affine parameters
flag: -affDirect
aff_file: (a file name)
The output affine matrix file
flag: -aff %s
args: (a unicode string)
Additional parameters to the command
flag: %s
cog_flag: (a boolean)
Use the masks centre of mass to initialise the transformation
flag: -cog
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
flo_low_val: (a float)
Lower threshold value on floating image
flag: -floLowThr %f
flo_up_val: (a float)
Upper threshold value on floating image
flag: -floUpThr %f
fmask_file: (an existing file name)
The input floating mask
flag: -fmask %s
gpuid_val: (an integer (int or long))
Device to use id
flag: -gpuid %i
i_val: (a long integer >= 0)
Percent of inlier blocks
flag: -pi %d
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_aff_file: (an existing file name)
The input affine transformation
flag: -inaff %s
ln_val: (a long integer >= 0)
Number of resolution levels to create
flag: -ln %d
lp_val: (a long integer >= 0)
Number of resolution levels to perform
flag: -lp %d
maxit_val: (a long integer >= 0)
Maximum number of iterations
flag: -maxit %d
nac_flag: (a boolean)
Use nifti header to initialise transformation
flag: -nac
nosym_flag: (a boolean)
Turn off symmetric registration
flag: -noSym
omp_core_val: (an integer (int or long), nipype default value: 1)
Number of openmp thread to use
flag: -omp %i
platform_val: (an integer (int or long))
Platform index
flag: -platf %i
ref_low_val: (a float)
Lower threshold value on reference image
flag: -refLowThr %f
ref_up_val: (a float)
Upper threshold value on reference image
flag: -refUpThr %f
res_file: (a file name)
The affine transformed floating image
flag: -res %s
rig_only_flag: (a boolean)
Do only a rigid registration
flag: -rigOnly
rmask_file: (an existing file name)
The input reference mask
flag: -rmask %s
smoo_f_val: (a float)
Amount of smoothing to apply to floating image
flag: -smooF %f
smoo_r_val: (a float)
Amount of smoothing to apply to reference image
flag: -smooR %f
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
v_val: (a long integer >= 0)
Percent of blocks that are active
flag: -pv %d
verbosity_off_flag: (a boolean)
Turn off verbose output
flag: -voff
Outputs:
aff_file: (a file name)
The output affine file
avg_output: (a string)
Output string in the format for reg_average
res_file: (a file name)
The output transformed image
RegF3D¶
Wraps command reg_f3d
Interface for executable reg_f3d from NiftyReg platform.
Fast Free-Form Deformation (F3D) algorithm for non-rigid registration. Initially based on Modat et al., “Fast Free-Form Deformation using graphics processing units”, CMPB, 2010
Examples¶
>>> from nipype.interfaces import niftyreg
>>> node = niftyreg.RegF3D()
>>> node.inputs.ref_file = 'im1.nii'
>>> node.inputs.flo_file = 'im2.nii'
>>> node.inputs.rmask_file = 'mask.nii'
>>> node.inputs.omp_core_val = 4
>>> node.cmdline
'reg_f3d -cpp im2_cpp.nii.gz -flo im2.nii -omp 4 -ref im1.nii -res im2_res.nii.gz -rmask mask.nii'
Inputs:
[Mandatory]
flo_file: (an existing file name)
The input floating/source image
flag: -flo %s
ref_file: (an existing file name)
The input reference/target image
flag: -ref %s
[Optional]
aff_file: (an existing file name)
The input affine transformation file
flag: -aff %s
amc_flag: (a boolean)
Use additive NMI
flag: -amc
args: (a unicode string)
Additional parameters to the command
flag: %s
be_val: (a float)
Bending energy value
flag: -be %f
cpp_file: (a file name)
The output CPP file
flag: -cpp %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
fbn2_val: (a tuple of the form: (a long integer >= 0, a long integer
>= 0))
Number of bins in the histogram for reference image for given time
point
flag: -fbn %d %d
fbn_val: (a long integer >= 0)
Number of bins in the histogram for reference image
flag: --fbn %d
flo_smooth_val: (a float)
Smoothing kernel width for floating image
flag: -smooF %f
flwth2_thr_val: (a tuple of the form: (a long integer >= 0, a float))
Lower threshold for floating image at the specified time point
flag: -fLwTh %d %f
flwth_thr_val: (a float)
Lower threshold for floating image
flag: --fLwTh %f
fmask_file: (an existing file name)
Floating image mask
flag: -fmask %s
fupth2_thr_val: (a tuple of the form: (a long integer >= 0, a float))
Upper threshold for floating image at the specified time point
flag: -fUpTh %d %f
fupth_thr_val: (a float)
Upper threshold for floating image
flag: --fUpTh %f
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
incpp_file: (an existing file name)
The input cpp transformation file
flag: -incpp %s
jl_val: (a float)
Log of jacobian of deformation penalty value
flag: -jl %f
kld2_flag: (a long integer >= 0)
Use KL divergence as the similarity measure for a given time point
flag: -kld %d
kld_flag: (a boolean)
Use KL divergence as the similarity measure
flag: --kld
le_val: (a float)
Linear elasticity penalty term
flag: -le %f
ln_val: (a long integer >= 0)
Number of resolution levels to create
flag: -ln %d
lncc2_val: (a tuple of the form: (a long integer >= 0, a float))
SD of the Gaussian for computing LNCC for a given time point
flag: -lncc %d %f
lncc_val: (a float)
SD of the Gaussian for computing LNCC
flag: --lncc %f
lp_val: (a long integer >= 0)
Number of resolution levels to perform
flag: -lp %d
maxit_val: (a long integer >= 0)
Maximum number of iterations per level
flag: -maxit %d
nmi_flag: (a boolean)
use NMI even when other options are specified
flag: --nmi
no_app_jl_flag: (a boolean)
Do not approximate the log of jacobian penalty at control points
only
flag: -noAppJL
noconj_flag: (a boolean)
Use simple GD optimization
flag: -noConj
nopy_flag: (a boolean)
Do not use the multiresolution approach
flag: -nopy
nox_flag: (a boolean)
Don't optimise in x direction
flag: -nox
noy_flag: (a boolean)
Don't optimise in y direction
flag: -noy
noz_flag: (a boolean)
Don't optimise in z direction
flag: -noz
omp_core_val: (an integer (int or long), nipype default value: 1)
Number of openmp thread to use
flag: -omp %i
pad_val: (a float)
Padding value
flag: -pad %f
pert_val: (a long integer >= 0)
Add perturbation steps after each optimization step
flag: -pert %d
rbn2_val: (a tuple of the form: (a long integer >= 0, a long integer
>= 0))
Number of bins in the histogram for reference image for given time
point
flag: -rbn %d %d
rbn_val: (a long integer >= 0)
Number of bins in the histogram for reference image
flag: --rbn %d
ref_smooth_val: (a float)
Smoothing kernel width for reference image
flag: -smooR %f
res_file: (a file name)
The output resampled image
flag: -res %s
rlwth2_thr_val: (a tuple of the form: (a long integer >= 0, a float))
Lower threshold for reference image at the specified time point
flag: -rLwTh %d %f
rlwth_thr_val: (a float)
Lower threshold for reference image
flag: --rLwTh %f
rmask_file: (an existing file name)
Reference image mask
flag: -rmask %s
rupth2_thr_val: (a tuple of the form: (a long integer >= 0, a float))
Upper threshold for reference image at the specified time point
flag: -rUpTh %d %f
rupth_thr_val: (a float)
Upper threshold for reference image
flag: --rUpTh %f
smooth_grad_val: (a float)
Kernel width for smoothing the metric gradient
flag: -smoothGrad %f
ssd2_flag: (a long integer >= 0)
Use SSD as the similarity measure for a given time point
flag: -ssd %d
ssd_flag: (a boolean)
Use SSD as the similarity measure
flag: --ssd
sx_val: (a float)
Final grid spacing along the x axes
flag: -sx %f
sy_val: (a float)
Final grid spacing along the y axes
flag: -sy %f
sz_val: (a float)
Final grid spacing along the z axes
flag: -sz %f
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
vel_flag: (a boolean)
Use velocity field integration
flag: -vel
verbosity_off_flag: (a boolean)
Turn off verbose output
flag: -voff
Outputs:
avg_output: (a string)
Output string in the format for reg_average
cpp_file: (a file name)
The output CPP file
invcpp_file: (a file name)
The output inverse CPP file
invres_file: (a file name)
The output inverse res file
res_file: (a file name)
The output resampled image