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
