In this simple tutorial we will use the Registration interface from ANTS to coregister two T1 volumes.
import os
import urllib2
from nipype.interfaces.ants import Registration
homeDir=os.getenv("HOME")
requestedPath=os.path.join(homeDir,'nipypeTestPath')
mydatadir=os.path.realpath(requestedPath)
if not os.path.exists(mydatadir):
os.makedirs(mydatadir)
print mydatadir
MyFileURLs=[
('http://slicer.kitware.com/midas3/download?bitstream=13121','01_T1_half.nii.gz'),
('http://slicer.kitware.com/midas3/download?bitstream=13122','02_T1_half.nii.gz'),
]
for tt in MyFileURLs:
myURL=tt[0]
localFilename=os.path.join(mydatadir,tt[1])
if not os.path.exists(localFilename):
remotefile = urllib2.urlopen(myURL)
localFile = open(localFilename, 'wb')
localFile.write(remotefile.read())
localFile.close()
print("Downloaded file: {0}".format(localFilename))
else:
print("File previously downloaded {0}".format(localFilename))
input_images=[
os.path.join(mydatadir,'01_T1_half.nii.gz'),
os.path.join(mydatadir,'02_T1_half.nii.gz'),
]
reg = Registration()
reg.inputs.fixed_image = input_images[0]
reg.inputs.moving_image = input_images[1]
reg.inputs.output_transform_prefix = 'thisTransform'
reg.inputs.output_warped_image = 'INTERNAL_WARPED.nii.gz'
reg.inputs.transforms = ['Translation', 'Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.3, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 0, 0], [10000, 0, 0], [10000, 0, 0], [10, 0, 0]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.metric = ['Mattes']*4
reg.inputs.metric_weight = [1]*4 # Default (value ignored currently by ANTs)
reg.inputs.radius_or_number_of_bins = [32]*4
reg.inputs.sampling_strategy = ['Regular']*3 + [None]
reg.inputs.sampling_percentage = [0.1]*3 + [None]
reg.inputs.convergence_threshold = [1.e-8]*4
reg.inputs.convergence_window_size = [20]*4
reg.inputs.smoothing_sigmas = [[4,2,1]]*3 + [[2,1,0]]
reg.inputs.sigma_units = ['vox']*4
reg.inputs.shrink_factors = [[6,4,2]]*3 + [[4,2,1]]
reg.inputs.use_estimate_learning_rate_once = [True, True, True, True]
reg.inputs.use_histogram_matching = [False]*3 + [True] # This is the default
reg.inputs.initial_moving_transform_com = True
reg.inputs.output_warped_image = True
reg.cmdline
reg.run()
Example source code
You can download the full source code of this example. This same script is also included in the Nipype source distribution under the examples directory.