Merge pull request #3 from mgxd/enh/antsupdate

ENH: Registration settings

Author: satra

mindboggle/mindboggle123

@@ -360,32 +360,35 @@ else:
                       name="register")
         reg.inputs.fixed_image = IMAGE
         reg.inputs.moving_image = T1s[:N]
-        reg.inputs.output_transform_prefix = "output_"
-        reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
-        reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
-        reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[
-            100, 30, 20
-        ]]
+        
+        # mimic fmriprep's T1 - MNI registration
         reg.inputs.dimension = 3
-        reg.inputs.write_composite_transform = True
-        reg.inputs.collapse_output_transforms = True
-        reg.inputs.initial_moving_transform_com = True
-        reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
-        reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
-        reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
-        reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
-        reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
-        reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
-        reg.inputs.convergence_window_size = [20] * 2 + [5]
-        reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
-        reg.inputs.sigma_units = ['vox'] * 3
-        reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
-        reg.inputs.use_estimate_learning_rate_once = [True] * 3
-        reg.inputs.use_histogram_matching = [False] * 2 + [True]
-        reg.inputs.winsorize_lower_quantile = 0.005
+        reg.inputs.convergence_threshold = [1e-06, 1e-06, 1e-06]
+        reg.inputs.convergence_window_size = [20, 20, 10]
+        reg.inputs.metric = ["Mattes", "Mattes", "CC"]
+        reg.inputs.metric_weight = [1, 1, 1]
+        reg.inputs.radius_or_number_of_bins = [56, 56, 4]
+        reg.inputs.transforms = ["Rigid", "Affine", "SyN"]
+        reg.inputs.transform_parameters = [(0.05,), (0.08,), (0.1, 3.0, 0.0)]
+        reg.inputs.number_of_iterations = [[100, 100], [100, 100], [100, 70, 50, 20]]
+        reg.inputs.sampling_strategy = ["Regular", "Regular", "None"]
+        reg.inputs.sampling_percentage = [0.25, 0.25, 1]
+        reg.inputs.smoothing_sigmas = [[2, 1], [1, 0], [3, 2, 1, 0]]
+        reg.inputs.sigma_units = ["vox", "vox", "vox"]
+        reg.inputs.shrink_factors = [[2, 1], [2, 1], [8, 4, 2, 1]]
         reg.inputs.winsorize_upper_quantile = 0.995
+        reg.inputs.winsorize_lower_quantile = 0.005
+        reg.inputs.use_estimate_learning_rate_once = [True, True, True]
+        reg.inputs.use_histogram_matching = [True, True, True]
+        reg.inputs.collapse_output_transforms = True
+        reg.inputs.write_composite_transform = True
+        reg.inputs.output_transform_prefix = "output_"
+        reg.inputs.output_warped_image = True
+        reg.inputs.output_warped_image = "output_warped_image.nii.gz"
+        reg.inputs.interpolation = "LanczosWindowedSinc"
         reg.inputs.float = True
-        reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
+        reg.inputs.initial_moving_transform_com = 0
+
         if args.num_threads and args.num_threads > 1:
             reg.inputs.num_threads = args.num_threads