args = parser.parse_args() (MNI, MNI_brain, MNI_brain_mask, MNI_voxel_sizes) = broccoli.load_MNI_templates(args.mni_file) (T1, T1_voxel_sizes) = broccoli.load_T1(args.t1_file) coarsest_scale = int(round(8 / MNI_voxel_sizes[0])) filters_parametric_mat = scipy.io.loadmat(args.filters_parametric_file) filters_nonparametric_mat = scipy.io.loadmat( args.filters_nonparametric_file) parametric_filters = [ filters_parametric_mat['f%d_parametric_registration' % (i + 1)] for i in range(3) ] nonparametric_filters = [ filters_nonparametric_mat['f%d_nonparametric_registration' % (i + 1)] for i in range(6) ] results = broccoli.registerT1MNI( T1, T1_voxel_sizes, MNI, MNI_voxel_sizes, MNI_brain, MNI_brain_mask, parametric_filters, nonparametric_filters, [filters_nonparametric_mat['m%d' % (i + 1)][0] for i in range(6)], [ filters_nonparametric_mat['filter_directions_%s' % d][0] for d in ['x', 'y', 'z'] ], args.iterations_parametric, args.iterations_nonparametric, coarsest_scale, args.mm_t1_z_cut, args.opencl_platform, args.opencl_device, args.show_results)
parser.add_argument('--filters-parametric-file', type=str, default="../Matlab_Wrapper/filters_for_parametric_registration.mat") parser.add_argument('--filters-nonparametric-file', type=str, default="../Matlab_Wrapper/filters_for_nonparametric_registration.mat") parser.add_argument('--mm-t1-z-cut', type=int, default=30) parser.add_argument('--show-results', action='store_true') args = parser.parse_args() (MNI, MNI_brain, MNI_brain_mask, MNI_voxel_sizes) = broccoli.load_MNI_templates(args.mni_file) (T1, T1_voxel_sizes) = broccoli.load_T1(args.t1_file) coarsest_scale = int(round(8 / MNI_voxel_sizes[0])) filters_parametric_mat = scipy.io.loadmat(args.filters_parametric_file) filters_nonparametric_mat = scipy.io.loadmat(args.filters_nonparametric_file) parametric_filters = [filters_parametric_mat['f%d_parametric_registration' % (i+1)] for i in range(3)] nonparametric_filters = [filters_nonparametric_mat['f%d_nonparametric_registration' % (i+1)] for i in range(6)] results = broccoli.registerT1MNI(T1, T1_voxel_sizes, MNI, MNI_voxel_sizes, MNI_brain, MNI_brain_mask, parametric_filters, nonparametric_filters, [filters_nonparametric_mat['m%d' % (i+1)][0] for i in range(6)], [filters_nonparametric_mat['filter_directions_%s' % d][0] for d in ['x', 'y', 'z']], args.iterations_parametric, args.iterations_nonparametric, coarsest_scale, args.mm_t1_z_cut, args.opencl_platform, args.opencl_device, args.show_results)
def _run_interface(self, runtime): T1_data, T1_voxel_sizes = broccoli.load_T1(self.inputs.t1_file) MNI_data, MNI_brain_data, MNI_brain_mask_data, MNI_voxel_sizes = broccoli.load_MNI_templates( self.inputs.mni_file) filters_parametric_mat = scipy.io.loadmat( self.inputs.filters_parametric) filters_nonparametric_mat = scipy.io.loadmat( self.inputs.filters_nonparametric) filters_parametric = [ filters_parametric_mat['f%d_parametric_registration' % (i + 1)] for i in range(3) ] filters_nonparametric = [ filters_nonparametric_mat['f%d_nonparametric_registration' % (i + 1)] for i in range(6) ] projection_tensor = [ filters_nonparametric_mat['m%d' % (i + 1)][0] for i in range(6) ] filter_directions = [ filters_nonparametric_mat['filter_directions_%s' % d][0] for d in ['x', 'y', 'z'] ] (Aligned_T1_Volume, Aligned_T1_Volume_NonParametric, Skullstripped_T1_Volume, Interpolated_T1_Volume, Registration_Parameters, Phase_Differences, Phase_Certainties, Phase_Gradients, Slice_Sums, Top_Slice, A_Matrix, h_Vector) = broccoli.registerT1MNI( T1_data, T1_voxel_sizes, MNI_data, MNI_voxel_sizes, MNI_brain_data, MNI_brain_mask_data, filters_parametric, filters_nonparametric, projection_tensor, filter_directions, 10, 15, int(round(8 / MNI_voxel_sizes[0])), 30, self.inputs.opencl_platform, self.inputs.opencl_device, self.inputs.show_results, ) MNI_nni = nb.load(self.inputs.mni_file) aligned_T1_nni = nb.Nifti1Image(Aligned_T1_Volume, None, MNI_nni.get_header()) nb.save(aligned_T1_nni, self._get_output_filename('_aligned.nii')) interpolated_T1_nni = nb.Nifti1Image(Interpolated_T1_Volume, None, MNI_nni.get_header()) nb.save(interpolated_T1_nni, self._get_output_filename('_interpolated.nii')) return runtime
def _run_interface(self, runtime): T1_data, T1_voxel_sizes = broccoli.load_T1(self.inputs.t1_file) MNI_data, MNI_brain_data, MNI_brain_mask_data, MNI_voxel_sizes = broccoli.load_MNI_templates(self.inputs.mni_file) filters_parametric_mat = scipy.io.loadmat(self.inputs.filters_parametric) filters_nonparametric_mat = scipy.io.loadmat(self.inputs.filters_nonparametric) filters_parametric = [filters_parametric_mat['f%d_parametric_registration' % (i+1)] for i in range(3)] filters_nonparametric = [filters_nonparametric_mat['f%d_nonparametric_registration' % (i+1)] for i in range(6)] projection_tensor = [filters_nonparametric_mat['m%d' % (i+1)][0] for i in range(6)] filter_directions = [filters_nonparametric_mat['filter_directions_%s' % d][0] for d in ['x', 'y', 'z']] (Aligned_T1_Volume, Aligned_T1_Volume_NonParametric, Skullstripped_T1_Volume, Interpolated_T1_Volume, Registration_Parameters, Phase_Differences, Phase_Certainties, Phase_Gradients, Slice_Sums, Top_Slice, A_Matrix, h_Vector) = broccoli.registerT1MNI( T1_data, T1_voxel_sizes, MNI_data, MNI_voxel_sizes, MNI_brain_data, MNI_brain_mask_data, filters_parametric, filters_nonparametric, projection_tensor, filter_directions, 10, 15, int(round(8 / MNI_voxel_sizes[0])), 30, self.inputs.opencl_platform, self.inputs.opencl_device, self.inputs.show_results, ) MNI_nni = nb.load(self.inputs.mni_file) aligned_T1_nni = nb.Nifti1Image(Aligned_T1_Volume, None, MNI_nni.get_header()) nb.save(aligned_T1_nni, self._get_output_filename('_aligned.nii')) interpolated_T1_nni = nb.Nifti1Image(Interpolated_T1_Volume, None, MNI_nni.get_header()) nb.save(interpolated_T1_nni, self._get_output_filename('_interpolated.nii')) return runtime