SHM1 = ozm.SphericalHarmonicsModel( dwi1, bvecs1, bvals1, mask=brain_mask, model_coeffs=csd_coeffs[0], response_file=response_files[0], #axial_diffusivity=AD1, #radial_diffusivity=RD1 ) SHM2 = ozm.SphericalHarmonicsModel( dwi2, bvecs2, bvals2, #mask = mask_array, mask=brain_mask, model_coeffs=csd_coeffs[1], response_file=response_files[1], #axial_diffusivity=AD2, #radial_diffusivity=RD2 ) rmse_file_name = '%s%s_relative_rmse_b%s.nii.gz' % ( data_path, 'SphericalHarmonicsModel', bval) if not os.path.isfile(rmse_file_name): relative_rmse = ozm.relative_rmse(SHM1, SHM2) io.nii_from_volume(relative_rmse, rmse_file_name, ni.load(dwi1).get_affine())
mask=brain_mask) SphereModel1 = ozm.SphereModel(dwi1, bvecs1, bvals1, mask=brain_mask) SphereModel2 = ozm.SphereModel(dwi2, bvecs2, bvals2, mask=brain_mask) ModelFest = zip([ TensorModel1, CanonicalTensorModel1, MultiCanonicalTensorModel1, SparseDeconvolutionModel1, SphereModel1, PointyMultiCanonicalTensorModel1, PointyCanonicalTensorModel1 ], [ TensorModel2, CanonicalTensorModel2, MultiCanonicalTensorModel2, SparseDeconvolutionModel2, SphereModel2, PointyMultiCanonicalTensorModel2, PointyCanonicalTensorModel2 ], [ 'TensorModel', 'CanonicalTensorModel', 'MultiCanonicalTensorModel', 'SparseDeconvolutionModel', 'SphereModel', 'PointyMultiCanonicalTensorModel', 'PointyCanonicalTensorModel' ]) # Compute the relative RMSE for each model: relative_rmse = [] for Model1, Model2, model_name in ModelFest: rmse_file_name = '%s%s_relative_rmse_b%s.nii.gz' % (data_path, model_name, bval) # Only do this if you have to: if not os.path.isfile(rmse_file_name): relative_rmse = ozm.relative_rmse(Model1, Model2) io.nii_from_volume(relative_rmse, rmse_file_name, ni.load(dwi1).get_affine())
csd_coeffs.append(d + "/" + f.split(".")[0] + "_CSD.nii.gz") response_files.append(d + "/" + f.split(".")[0] + "_ER.mif") SHM1 = ozm.SphericalHarmonicsModel( dwi1, bvecs1, bvals1, mask=brain_mask, model_coeffs=csd_coeffs[0], response_file=response_files[0], # axial_diffusivity=AD1, # radial_diffusivity=RD1 ) SHM2 = ozm.SphericalHarmonicsModel( dwi2, bvecs2, bvals2, # mask = mask_array, mask=brain_mask, model_coeffs=csd_coeffs[1], response_file=response_files[1], # axial_diffusivity=AD2, # radial_diffusivity=RD2 ) rmse_file_name = "%s%s_relative_rmse_b%s.nii.gz" % (data_path, "SphericalHarmonicsModel", bval) if not os.path.isfile(rmse_file_name): relative_rmse = ozm.relative_rmse(SHM1, SHM2) io.nii_from_volume(relative_rmse, rmse_file_name, ni.load(dwi1).get_affine())
ModelFest = zip([TensorModel1,CanonicalTensorModel1, MultiCanonicalTensorModel1,SparseDeconvolutionModel1, SphereModel1,PointyMultiCanonicalTensorModel1, PointyCanonicalTensorModel1], [TensorModel2,CanonicalTensorModel2, MultiCanonicalTensorModel2,SparseDeconvolutionModel2, SphereModel2,PointyMultiCanonicalTensorModel2, PointyCanonicalTensorModel2], ['TensorModel', 'CanonicalTensorModel', 'MultiCanonicalTensorModel','SparseDeconvolutionModel', 'SphereModel', 'PointyMultiCanonicalTensorModel', 'PointyCanonicalTensorModel' ]) # Compute the relative RMSE for each model: relative_rmse = [] for Model1,Model2,model_name in ModelFest: rmse_file_name = '%s%s_relative_rmse_b%s.nii.gz'%(data_path, model_name, bval) # Only do this if you have to: if not os.path.isfile(rmse_file_name): relative_rmse = ozm.relative_rmse(Model1, Model2) io.nii_from_volume(relative_rmse, rmse_file_name, ni.load(dwi1).get_affine())