sMRI: Using ANTS for registration¶
In this simple tutorial we will use the Registration interface from ANTS to coregister two T1 volumes.
- Tell python where to find the appropriate functions.
import os
import urllib2
from nipype.interfaces.ants import Registration
- Download T1 volumes into home directory
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'),
]
- Define the parameters of the registration
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.output_transform_prefix = "output_"
reg.inputs.transforms = ['Translation', 'Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.1,), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = ([[10000, 111110, 11110]]*3 +
[[100, 50, 30]])
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.metric = ['Mattes'] * 3 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 3 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 3 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 3 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 3 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 3 + [-0.01]
reg.inputs.convergence_window_size = [20] * 3 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 3 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 4
reg.inputs.shrink_factors = [[6, 4, 2]] + [[3, 2, 1]]*2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 4
reg.inputs.use_histogram_matching = [False] * 3 + [True]
reg.inputs.initial_moving_transform_com = True
print reg.cmdline
- Run the registration
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.