sMRI: Using ANTS for registration

In this simple tutorial we will use the Registration interface from ANTS to coregister two T1 volumes.

1. Tell python where to find the appropriate functions.

import os
import urllib2
from nipype.interfaces.ants import Registration

2. 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'),
]

3. 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

3. 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.