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):
MNI, MNI_brain, MNI_brain_mask, MNI_voxel_sizes = broccoli.load_MNI_templates(self.inputs.MNI_file, self.inputs.MNI_brain_file, self.inputs.MNI_brain_mask_file)
fMRI, fMRI_voxel_sizes = broccoli.load_EPI(self.inputs.fMRI_file, only_volume=False)
T1, T1_voxel_sizes = broccoli.load_T1(self.inputs.T1_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']]
X_GLM = self.load_regressors(fMRI.shape[3])
print("X_GLM.shape = %s", X_GLM.shape)
# print("X_GLM = %s", X_GLM)
xtx = np.linalg.inv(np.dot(X_GLM.T, X_GLM))
print("xtx.shape = %s", xtx.shape)
xtxxt_GLM = xtx.dot(X_GLM.T)
confounds = 1
if self.inputs.regress_confounds:
confounds = np.loadtxt(self.inputs.confounds_file)
contrasts = np.array([[1, 0]])
print("contrasts = %s" % contrasts)
ctxtxc_GLM = [contrasts[i:i+1].dot(xtx).dot(contrasts[i:i+1].T) for i in range(len(contrasts))]
print("ctxtxc_GLM = %s" % ctxtxc_GLM)
fMRI_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
T1_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
MNI_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
print("Parameters: %s" % self.inputs)
statistical_maps = broccoli.performFirstLevelAnalysis(
fMRI, fMRI_voxel_sizes, T1, T1_voxel_sizes, MNI, MNI_brain, MNI_brain_mask, MNI_voxel_sizes,
filters_parametric, filters_nonparametric, projection_tensor, filter_directions,
self.inputs.iterations_parametric, self.inputs.iterations_nonparametric, self.inputs.iterations_motion_correction, 4, 4, 0, 0,
self.inputs.regress_motion, self.inputs.EPI_smoothing, self.inputs.AR_smoothing, X_GLM, xtxxt_GLM.transpose(), contrasts, ctxtxc_GLM,
self.inputs.use_temporal_derivatives, getattr(broccoli, self.inputs.beta_space), confounds, self.inputs.regress_confounds,
self.inputs.opencl_platform, self.inputs.opencl_device, self.inputs.show_results,
)
EPI_nni = nb.load(self.inputs.fMRI_file)
aligned_EPI_nni = nb.Nifti1Image(statistical_maps, None, EPI_nni.get_header())
nb.save(aligned_EPI_nni, self._get_output_filename('statistical_map.nii'))
return runtime
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)
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
parser.add_argument('--mni-brain-file', type=str)
parser.add_argument('--mni-brain-mask-file', type=str)
parser.add_argument('--t1-file', type=str, required=True)
parser.add_argument('--iterations-parametric', type=int, default=10)
parser.add_argument('--iterations-nonparametric', type=int, default=15)
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,
def _run_interface(self, runtime):
MNI, MNI_brain, MNI_brain_mask, MNI_voxel_sizes = broccoli.load_MNI_templates(
self.inputs.MNI_file, self.inputs.MNI_brain_file,
self.inputs.MNI_brain_mask_file)
fMRI, fMRI_voxel_sizes = broccoli.load_EPI(self.inputs.fMRI_file,
only_volume=False)
T1, T1_voxel_sizes = broccoli.load_T1(self.inputs.T1_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']
]
X_GLM = self.load_regressors(fMRI.shape[3])
xtx = np.linalg.inv(np.dot(X_GLM.T, X_GLM))
# print(xtx)
xtxxt_GLM = xtx.dot(X_GLM.T)
confounds = 1
if self.inputs.regress_confounds:
confounds = np.loadtxt(self.inputs.confounds_file)
contrasts = np.array([[1, 0], [1, 0], [1, 0], [1, 0]])
ctxtxc_GLM = [
contrasts[i:i + 1].dot(xtx).dot(contrasts[i:i + 1].T)
for i in range(len(contrasts))
]
fMRI_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
T1_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
MNI_voxel_sizes = [int(round(v)) for v in T1_voxel_sizes]
statistical_maps = broccoli.performFirstLevelAnalysis(
fMRI,
fMRI_voxel_sizes,
T1,
T1_voxel_sizes,
MNI,
MNI_brain,
MNI_brain_mask,
MNI_voxel_sizes,
filters_parametric,
filters_nonparametric,
projection_tensor,
filter_directions,
self.inputs.iterations_parametric,
self.inputs.iterations_nonparametric,
self.inputs.iterations_motion_correction,
4,
4,
0,
0,
self.inputs.regress_motion,
self.inputs.EPI_smoothing,
self.inputs.AR_smoothing,
X_GLM,
xtxxt_GLM.transpose(),
contrasts,
ctxtxc_GLM,
self.inputs.use_temporal_derivatives,
getattr(broccoli, self.inputs.beta_space),
confounds,
self.inputs.regress_confounds,
self.inputs.opencl_platform,
self.inputs.opencl_device,
self.inputs.show_results,
)
EPI_nni = nb.load(self.inputs.fMRI_file)
aligned_EPI_nni = nb.Nifti1Image(statistical_maps, None,
EPI_nni.get_header())
nb.save(aligned_EPI_nni,
self._get_output_filename('statistical_map.nii'))
return runtime