]) res_robust = least_squares(_loss_gaussian_citrate_4_dof, popt_default, loss='huber', f_scale=1., bounds=param_bounds, args=(ppm_interp, f(ppm_interp))) return res_robust.x path_mrsi = '/data/prostate/experiments/Patient 1036/MRSI/CSI_SE_3D_140ms_16c.rda' rda_mod = RDAModality(1250.) rda_mod.read_data_from_path(path_mrsi) phase_correction = MRSIPhaseCorrection(rda_mod) rda_mod = phase_correction.transform(rda_mod) freq_correction = MRSIFrequencyCorrection(rda_mod) rda_mod = freq_correction.fit(rda_mod).transform(rda_mod) baseline_correction = MRSIBaselineCorrection(rda_mod) rda_mod = baseline_correction.fit(rda_mod).transform(rda_mod) # x = 0 # y = 0 # z = 0 # out = _citrate_fitting(rda_mod.bandwidth_ppm[:, 5, 9, 5],
# Append for the GT data - Note that we need a list of gt path path_patients_list_gt.append( [os.path.join(path_patients, id_patient, path_gt)]) # List where to store the different minimum for id_p, (p_mrsi, p_gt) in enumerate( zip(path_patients_list_mrsi, path_patients_list_gt)): print 'Processing {}'.format(id_patient_list[id_p]) # Remove a part of the string to have only the id nb_patient = id_patient_list[id_p].replace('Patient ', '') # Read the image data rda_mod = RDAModality(1250.) rda_mod.read_data_from_path(os.path.join(p_mrsi, filename_mrsi)) # Correct the phase phase_correction = MRSIPhaseCorrection(rda_mod) rda_mod = phase_correction.transform(rda_mod) # Correct the frequency shift freq_correction = MRSIFrequencyCorrection(rda_mod) rda_mod = freq_correction.fit(rda_mod).transform(rda_mod) # Correct the baseline baseline_correction = MRSIBaselineCorrection(rda_mod) rda_mod = baseline_correction.fit(rda_mod).transform(rda_mod) # Read the GT gt_mod = GTModality()
from matplotlib.colors import colorConverter import matplotlib.pyplot as plt from protoclass.data_management import T2WModality from protoclass.data_management import RDAModality path_rda = '/data/prostate/experiments/Patient 996/MRSI/CSI_SE_3D_140ms_16c.rda' path_t2w = '/data/prostate/experiments/Patient 996/T2W' # Read the ground-truth t2w_mod = T2WModality() t2w_mod.read_data_from_path(path_t2w) # Read the rda rda_mod = RDAModality(1250.) rda_mod.read_data_from_path(path_rda) # Get the sitk image from the T2W # We need to convert from numpy array to ITK # Our convention was Y, X, Z # We need to convert it in Z, Y, X which will be converted in X, Y, Z by ITK t2w_img = sitk.GetImageFromArray(np.swapaxes( np.swapaxes(t2w_mod.data_, 0, 1), 0, 2)) # Put all the spatial information t2w_img.SetDirection(t2w_mod.metadata_['direction']) t2w_img.SetOrigin(t2w_mod.metadata_['origin']) t2w_img.SetSpacing(t2w_mod.metadata_['spacing']) # Get the sitk image from the rda rda_fake = np.random.randint(0, 255, size=(16, 16, 16)) rda_img = sitk.GetImageFromArray(rda_fake)
import numpy as np from protoclass.data_management import RDAModality from protoclass.preprocessing import MRSIPhaseCorrection from fdasrsf import srsf_align path_mrsi = '/data/prostate/experiments/Patient 1036/MRSI/CSI_SE_3D_140ms_16c.rda' rda_mod = RDAModality(1250.) rda_mod.read_data_from_path(path_mrsi) phase_correction = MRSIPhaseCorrection(rda_mod) rda_mod = phase_correction.transform(rda_mod) # Get the data data = np.reshape(rda_mod.data_, (rda_mod.data_.shape[0], rda_mod.data_.shape[1] * rda_mod.data_.shape[2] * rda_mod.data_.shape[3])) # Apply the curve alignment using the FDS-SRSF out = srsf_align(np.real(data), rda_mod.bandwidth_ppm, smoothdata=False)
from matplotlib.colors import colorConverter import matplotlib.pyplot as plt from protoclass.data_management import T2WModality from protoclass.data_management import RDAModality path_rda = '/data/prostate/experiments/Patient 996/MRSI/CSI_SE_3D_140ms_16c.rda' path_t2w = '/data/prostate/experiments/Patient 996/T2W' # Read the ground-truth t2w_mod = T2WModality() t2w_mod.read_data_from_path(path_t2w) # Read the rda rda_mod = RDAModality(1250.) rda_mod.read_data_from_path(path_rda) # Get the sitk image from the T2W # We need to convert from numpy array to ITK # Our convention was Y, X, Z # We need to convert it in Z, Y, X which will be converted in X, Y, Z by ITK t2w_img = sitk.GetImageFromArray( np.swapaxes(np.swapaxes(t2w_mod.data_, 0, 1), 0, 2)) # Put all the spatial information t2w_img.SetDirection(t2w_mod.metadata_['direction']) t2w_img.SetOrigin(t2w_mod.metadata_['origin']) t2w_img.SetSpacing(t2w_mod.metadata_['spacing']) # Get the sitk image from the rda rda_fake = np.random.randint(0, 255, size=(16, 16, 16)) rda_img = sitk.GetImageFromArray(rda_fake)