solver_params = dict(alpha=alpha,
l1_ratio=l1_ratio,
fit_intercept=False,
positive=True)
SD_1 = ssd.SparseDeconvolutionModel(*data[0],
mask=mask,
params_file="temp",
solver_params=solver_params,
axial_diffusivity=ad_rd[0]["AD"],
radial_diffusivity=ad_rd[0]["RD"])
SD_2 = ssd.SparseDeconvolutionModel(*data[1],
mask=mask,
params_file="temp",
solver_params=solver_params,
axial_diffusivity=ad_rd[1]["AD"],
radial_diffusivity=ad_rd[1]["RD"])
rrmse = oza.cross_predict(SD_1, SD_2)
rel = tensor_reliability(SD_1, SD_2, mask)
rrmse_fname = "%s/%s/RMSE_b%s_l1ratio%s_alpha%s.nii.gz" % (
data_path, subject, b, l1_ratio, alpha)
rel_fname = "%s/%s/PDD_angle_b%s_l1ratio%s_alpha%s.nii.gz" % (
data_path, subject, b, l1_ratio, alpha)
ni.Nifti1Image(rrmse, SD_1.affine).to_filename(rrmse_fname)
ni.Nifti1Image(rel, SD_1.affine).to_filename(rel_fname)
SD_1 = ssd.SparseDeconvolutionModel(*data[0],
mask=mask,
params_file="temp",
solver_params=solver_params,
axial_diffusivity=ad_rd[0]["AD"],
radial_diffusivity=ad_rd[0]["RD"])
SD_2 = ssd.SparseDeconvolutionModel(*data[1],
mask=mask,
params_file="temp",
solver_params=solver_params,
axial_diffusivity=ad_rd[1]["AD"],
radial_diffusivity=ad_rd[1]["RD"])
rrmse = oza.cross_predict(SD_1, SD_2)
rel = tensor_reliability(SD_1, SD_2, mask)
rrmse_fname = "%s/%s/RMSE_b%s_l1ratio%s_alpha%s.nii.gz"%(data_path,
subject,
b,
l1_ratio,
alpha)
rel_fname = "%s/%s/PDD_angle_b%s_l1ratio%s_alpha%s.nii.gz"%(data_path,
subject,
b,
l1_ratio,
alpha)
ni.Nifti1Image(rrmse, SD_1.affine).to_filename(rrmse_fname)
ni.Nifti1Image(rel, SD_1.affine).to_filename(rel_fname)
rrmse_dti = {}
rrmse_ssd = {}
for subject in ['FP', 'HT']:
subject_path = os.path.join(data_path, subject)
wm_mask_file = os.path.join(subject_path, '%s_wm_mask.nii.gz'%subject)
wm_nifti = ni.load(wm_mask_file).get_data()
wm_mask = np.zeros(wm_nifti.shape)
wm_mask[np.where(wm_nifti==1)] = 1
wm_idx = np.where(wm_nifti>0)
rrmse_dti[subject] = {}
rrmse_ssd[subject] = {}
for b in [1000, 2000, 4000]:
data_1, data_2 = oio.get_dwi_data(b, subject=subject)
TM1 = dti.TensorModel(*data_1, mask=wm_mask)
TM2 = dti.TensorModel(*data_2, mask=wm_mask)
rrmse_dti[subject][b] = ozm.cross_predict(TM1, TM2)
print subject
print b
rmse_mask = rrmse_dti[subject][b][wm_idx]
print "DTI: %s voxels above 1"%(len(np.where(rmse_mask>1)[0])/float(len(rmse_mask)))
print "Median rRMSE: %s"%np.median(rmse_mask)
ad_rd = oio.get_ad_rd(subject, b)
SD1 = ssd.SparseDeconvolutionModel(*data_1, mask=wm_mask,
axial_diffusivity=ad_rd[0]['AD'],
radial_diffusivity=ad_rd[0]['RD'])
SD2 = ssd.SparseDeconvolutionModel(*data_2, mask=wm_mask,
axial_diffusivity=ad_rd[1]['AD'],
radial_diffusivity=ad_rd[1]['RD'])
rrmse_ssd[subject][b] = ozm.cross_predict(SD1, SD2)
rmse_mask = rrmse_ssd[subject][b][wm_idx]
print "SSD: %s voxels above 1"%(len(np.where(rmse_mask>1)[0])/float(len(rmse_mask)))
