def denoising_diagnosis(raw_img,init_denoise,warped_mask,final_denoise, name_source, out_dir):
import os
import pathlib
import SimpleITK as sitk
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import matplotlib.pyplot as plt
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d,otsu_scaling
fig,axes = plt.subplots(nrows=3, ncols=4, figsize=(12*4,2*3))
plt.tight_layout()
scaled = otsu_scaling(raw_img)
axes[0,0].set_title('Raw EPI', fontsize=20)
plot_3d(axes[:,0],scaled,fig=fig,vmin=0,vmax=1,cmap='viridis')
scaled = otsu_scaling(init_denoise)
axes[0,1].set_title('Initial Denoising', fontsize=20)
plot_3d(axes[:,1],scaled,fig=fig,vmin=0,vmax=1,cmap='viridis')
axes[0,2].set_title('Resampled Mask', fontsize=20)
plot_3d(axes[:,2],scaled,fig=fig,vmin=0,vmax=1,cmap='viridis')
plot_3d(axes[:,2],sitk.ReadImage(warped_mask,sitk.sitkFloat32),fig=fig,vmin=-1,vmax=1,cmap='bwr', alpha=0.3, cbar=False)
scaled = otsu_scaling(final_denoise)
axes[0,3].set_title('Final Denoising', fontsize=20)
plot_3d(axes[:,3],scaled,fig=fig,vmin=0,vmax=1,cmap='viridis')
fig.savefig('%s_denoising.png' % (prefix), bbox_inches='tight')
def plot_reg(image1,image2, name_source, out_dir):
import os
import pathlib
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import matplotlib.pyplot as plt
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d,otsu_scaling
fig,axes = plt.subplots(nrows=2, ncols=3, figsize=(12*3,2*2))
plt.tight_layout()
scaled = otsu_scaling(image1)
display1,display2,display3 = plot_3d(scaled,axes[0,:], cmap='gray')
display1.add_edges(image2)
display2.add_edges(image2)
display3.add_edges(image2)
scaled = otsu_scaling(image2)
display1,display2,display3 = plot_3d(scaled,axes[1,:], cmap='gray')
display1.add_edges(image1)
display2.add_edges(image1)
display3.add_edges(image1)
fig.savefig('%s_registration.png' % (prefix), bbox_inches='tight')
def temporal_features(bold_file, confounds_csv, FD_csv, rabies_data_type, name_source, out_dir):
import os
import pathlib
filename_template = pathlib.Path(name_source).name.rsplit(".nii")[0]
os.makedirs(out_dir, exist_ok=True)
prefix = out_dir+'/'+ \
filename_template
import numpy as np
import SimpleITK as sitk
import matplotlib.pyplot as plt
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d
from rabies.preprocess_pkg.utils import copyInfo_3DImage
fig,axes = plt.subplots(nrows=3, ncols=3, figsize=(20,5))
# plot the motion timecourses
import pandas as pd
df = pd.read_csv(confounds_csv)
ax = axes[0,0]
ax.plot(df['mov1'])
ax.plot(df['mov2'])
ax.plot(df['mov3'])
ax.legend(['mov1','mov2','mov3'])
ax.set_title('Translation parameters', fontsize=20)
ax = axes[1,0]
ax.plot(df['rot1'])
ax.plot(df['rot2'])
ax.plot(df['rot3'])
ax.legend(['rot1','rot2','rot3'])
ax.set_title('Rotation parameters', fontsize=20)
df = pd.read_csv(FD_csv)
ax=axes[2,0]
ax.plot(df['Mean'], color='r')
ax.set_title('Framewise Displacement', fontsize=20)
plt.tight_layout()
# calculate STD and tSNR map on preprocessed timeseries
img = sitk.ReadImage(bold_file, rabies_data_type)
array = sitk.GetArrayFromImage(img)
mean = array.mean(axis=0)
std = array.std(axis=0)
std_filename = os.path.abspath('tSTD.nii.gz')
std_image = copyInfo_3DImage(
sitk.GetImageFromArray(std, isVector=False), img)
sitk.WriteImage(std_image, std_filename)
tSNR = np.divide(mean, std)
tSNR[np.isnan(tSNR)]=0
tSNR_filename = os.path.abspath('tSNR.nii.gz')
tSNR_image = copyInfo_3DImage(
sitk.GetImageFromArray(tSNR, isVector=False), img)
sitk.WriteImage(tSNR_image, tSNR_filename)
axes[0,1].set_title('Temporal STD', fontsize=20)
std=std.flatten()
std.sort()
std_vmax = std[int(len(std)*0.95)]
plot_3d(axes[:,1],std_image,fig=fig,vmin=0,vmax=std_vmax,cmap='inferno', cbar=True)
axes[0,2].set_title('Temporal SNR', fontsize=20)
plot_3d(axes[:,2],tSNR_image,fig=fig,vmin=0,vmax=tSNR.max(),cmap='Spectral', cbar=True)
fig.savefig('%s_temporal_features.png' % (prefix), bbox_inches='tight')
return std_filename, tSNR_filename
def template_info(anat_template, opts, out_dir):
import os
from nilearn import plotting
import matplotlib.pyplot as plt
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d,otsu_scaling
brain_mask = str(opts.brain_mask)
WM_mask = str(opts.WM_mask)
CSF_mask = str(opts.CSF_mask)
vascular_mask = str(opts.vascular_mask)
labels = str(opts.labels)
os.makedirs(out_dir, exist_ok=True)
import SimpleITK as sitk
# make sure that masks are binary
for mask in [brain_mask,WM_mask,vascular_mask]:
img = sitk.ReadImage(mask)
array = sitk.GetArrayFromImage(img)
if ((array!=1)*(array!=0)).sum()>0:
raise ValueError("The file %s is not a binary mask. Non-binary masks cannot be processed." % (mask))
scaled = otsu_scaling(anat_template)
fig,axes = plt.subplots(nrows=3, ncols=6, figsize=(4*6,2*2))
axes[0,0].set_title('Anatomical Template', fontsize=20)
plot_3d(axes[:,0],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
# plot brain mask
mask = brain_mask
sitk_mask = sitk.ReadImage(
mask, sitk.sitkFloat32)
axes[0,1].set_title('Brain Mask', fontsize=20)
plot_3d(axes[:,1],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
plot_3d(axes[:,1],sitk_mask,fig=fig,vmin=-1,vmax=1,cmap='bwr', alpha=0.3, cbar=False)
# plot WM mask
mask = WM_mask
sitk_mask = sitk.ReadImage(
mask, sitk.sitkFloat32)
axes[0,2].set_title('WM Mask', fontsize=20)
plot_3d(axes[:,2],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
plot_3d(axes[:,2],sitk_mask,fig=fig,vmin=-1,vmax=1,cmap='bwr', alpha=0.5, cbar=False)
# plot CSF mask
mask = CSF_mask
sitk_mask = sitk.ReadImage(
mask, sitk.sitkFloat32)
axes[0,3].set_title('CSF Mask', fontsize=20)
plot_3d(axes[:,3],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
plot_3d(axes[:,3],sitk_mask,fig=fig,vmin=-1,vmax=1,cmap='bwr', alpha=0.5, cbar=False)
# plot VASC mask
mask = vascular_mask
sitk_mask = sitk.ReadImage(
mask, sitk.sitkFloat32)
axes[0,4].set_title('Vascular Mask', fontsize=20)
plot_3d(axes[:,4],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
plot_3d(axes[:,4],sitk_mask,fig=fig,vmin=-1,vmax=1,cmap='bwr', alpha=0.5, cbar=False)
# plot labels
mask = labels
sitk_mask = sitk.ReadImage(
mask, sitk.sitkFloat32)
axes[0,5].set_title('Atlas Labels', fontsize=20)
plot_3d(axes[:,5],scaled,fig=fig,vmin=0,vmax=1,cmap='gray')
plot_3d(axes[:,5],sitk_mask,fig=fig,vmin=1,vmax=sitk.GetArrayFromImage(sitk_mask).max(),cmap='rainbow', alpha=0.5, cbar=False)
plt.tight_layout()
fig.savefig(out_dir+'/template_files.png', bbox_inches='tight')
def scan_diagnosis(bold_file,
mask_file_dict,
temporal_info,
spatial_info,
confounds_csv,
regional_grayplot=False):
template_file = mask_file_dict['template_file']
from mpl_toolkits.axes_grid1 import make_axes_locatable
fig, axCenter = plt.subplots(figsize=(6, 18))
fig.subplots_adjust(.2, .1, .8, .95)
divider = make_axes_locatable(axCenter)
ax1 = divider.append_axes('bottom', size='25%', pad=0.5)
ax1_ = divider.append_axes('bottom', size='25%', pad=0.1)
ax2 = divider.append_axes('bottom', size='50%', pad=0.5)
ax3 = divider.append_axes('bottom', size='50%', pad=0.5)
ax4 = divider.append_axes('bottom', size='50%', pad=0.5)
if regional_grayplot:
im, slice_alt, region_mask_label = grayplot_regional(
bold_file, mask_file_dict, fig, axCenter)
axCenter.yaxis.labelpad = 40
ax_slice = divider.append_axes('left', size='5%', pad=0.0)
ax_label = divider.append_axes('left', size='5%', pad=0.0)
ax_slice.imshow(slice_alt.reshape(-1, 1),
cmap='gray',
vmin=0,
vmax=1.1,
aspect='auto')
ax_label.imshow(region_mask_label.reshape(-1, 1),
cmap='Spectral',
aspect='auto')
ax_slice.axis('off')
ax_label.axis('off')
else:
im = grayplot(bold_file, mask_file_dict, fig, axCenter)
axCenter.set_ylabel('Voxels', fontsize=20)
axCenter.spines['right'].set_visible(False)
axCenter.spines['top'].set_visible(False)
axCenter.spines['bottom'].set_visible(False)
axCenter.spines['left'].set_visible(False)
axCenter.axes.get_yaxis().set_ticks([])
plt.setp(axCenter.get_xticklabels(), visible=False)
# plot the motion timecourses
import pandas as pd
df = pd.read_csv(confounds_csv)
ax1.plot(df['mov1'])
ax1.plot(df['mov2'])
ax1.plot(df['mov3'])
ax1.legend(['translation 1', 'translation 2', 'translation 3'],
loc='center left',
bbox_to_anchor=(1, 0.5))
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1_.plot(df['rot1'])
ax1_.plot(df['rot2'])
ax1_.plot(df['rot3'])
ax1_.legend(['rotation 1', 'rotation 2', 'rotation 3'],
loc='center left',
bbox_to_anchor=(1, 0.5))
plt.setp(ax1_.get_xticklabels(), visible=False)
ax1_.spines['right'].set_visible(False)
ax1_.spines['top'].set_visible(False)
#ax1.set_title(name, fontsize=15)
y = temporal_info['FD_trace']
ax2.plot(y, 'r')
ax2.set_xlim([0, len(y)])
ax2.legend(['Framewise Displacement (FD)'], loc='upper right')
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.set_ylim([0.0, 0.1])
plt.setp(ax2.get_xticklabels(), visible=False)
DVARS = temporal_info['DVARS']
DVARS[0] = None
y = DVARS
ax3.plot(y)
ax3.set_xlim([0, len(y)])
ax3.plot(temporal_info['edge_trace'])
ax3.plot(temporal_info['WM_trace'])
ax3.plot(temporal_info['CSF_trace'])
ax3.plot(temporal_info['VE_temporal'])
ax3.legend(['DVARS', 'Edge Mask', 'WM Mask', 'CSF Mask', 'CR R^2'],
loc='center left',
bbox_to_anchor=(1, 0.5))
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.set_ylim([0.0, 1.5])
plt.setp(ax3.get_xticklabels(), visible=False)
y = temporal_info['signal_trace']
ax4.plot(y)
ax4.set_xlim([0, len(y)])
ax4.plot(temporal_info['noise_trace'])
ax4.legend(['BOLD components', 'Confound components'], loc='upper right')
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_ylim([0.0, 4.0])
ax4.set_xlabel('Timepoint', fontsize=15)
dr_maps = spatial_info['DR_BOLD']
mask_file = mask_file_dict['brain_mask']
nrows = 5 + dr_maps.shape[0]
fig2, axes2 = plt.subplots(nrows=nrows,
ncols=3,
figsize=(12 * 3, 2 * nrows))
plt.tight_layout()
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d, otsu_scaling
axes = axes2[0, :]
scaled = otsu_scaling(template_file)
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['temporal_std']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=0,
vmax=1,
cmap='inferno',
alpha=1,
cbar=True,
num_slices=6)
for ax in axes:
ax.set_title('Temporal STD', fontsize=25)
axes = axes2[1, :]
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['VE_spatial']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=0,
vmax=1,
cmap='inferno',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6)
for ax in axes:
ax.set_title('CR R^2', fontsize=25)
axes = axes2[2, :]
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['GS_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=-1,
vmax=1,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6)
for ax in axes:
ax.set_title('Global Signal Correlation', fontsize=25)
axes = axes2[3, :]
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['DVARS_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=-1,
vmax=1,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6)
for ax in axes:
ax.set_title('DVARS Correlation', fontsize=25)
axes = axes2[4, :]
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, spatial_info['FD_corr']).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=-1,
vmax=1,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6)
for ax in axes:
ax.set_title('FD Correlation', fontsize=25)
for i in range(dr_maps.shape[0]):
axes = axes2[i + 5, :]
plot_3d(axes,
scaled,
fig2,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6)
analysis_functions.recover_3D(
mask_file, dr_maps[i, :]).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d(axes,
sitk_img,
fig2,
vmin=-1,
vmax=1,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6)
for ax in axes:
ax.set_title('BOLD component %s' % (i), fontsize=25)
return fig, fig2
def _run_interface(self, runtime):
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(self.inputs.spatial_info_list))
if len(merged) < 3:
import logging
log = logging.getLogger('root')
log.warning(
"Cannot run statistics on a sample size smaller than 3, so an empty figure is generated."
)
fig, axes = plt.subplots()
fig.savefig(os.path.abspath('empty_dataset_diagnosis.png'),
bbox_inches='tight')
setattr(self, 'figure_dataset_diagnosis',
os.path.abspath('empty_dataset_diagnosis.png'))
return runtime
dict_keys = [
'temporal_std', 'VE_spatial', 'GS_corr', 'DVARS_corr', 'FD_corr',
'DR_BOLD', 'prior_modeling_maps'
]
voxelwise_list = []
for spatial_info in merged:
sub_list = [spatial_info[key] for key in dict_keys]
voxelwise_sub = np.array(sub_list[:5])
if len(sub_list[6]) > 0:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[5]), np.array(
sub_list[6])),
axis=0)
else:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[5])), axis=0)
voxelwise_list.append(voxelwise_sub)
num_DR_maps = len(sub_list[5])
num_prior_maps = len(sub_list[6])
voxelwise_array = np.array(voxelwise_list)
label_name = [
'temporal_std', 'VE_spatial', 'GS_corr', 'DVARS_corr', 'FD_corr'
]
label_name += [
'BOLD Dual Regression map %s' % (i) for i in range(num_DR_maps)
]
label_name += [
'BOLD Dual Convergence map %s' % (i) for i in range(num_prior_maps)
]
template_file = self.inputs.mask_file_dict['template_file']
mask_file = self.inputs.mask_file_dict['brain_mask']
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d, otsu_scaling
scaled = otsu_scaling(template_file)
ncols = 5
fig, axes = plt.subplots(nrows=voxelwise_array.shape[1],
ncols=ncols,
figsize=(12 * ncols,
2 * voxelwise_array.shape[1]))
for i, x_label in zip(range(voxelwise_array.shape[1]), label_name):
for j, y_label in zip(range(ncols), label_name[:ncols]):
ax = axes[i, j]
if i <= j:
ax.axis('off')
continue
X = voxelwise_array[:, i, :]
Y = voxelwise_array[:, j, :]
corr = elementwise_corrcoef(X, Y)
plot_3d([ax],
scaled,
fig,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6,
planes=('coronal'))
analysis_functions.recover_3D(
mask_file, corr).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d([ax],
sitk_img,
fig,
vmin=-0.7,
vmax=0.7,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6,
planes=('coronal'))
ax.set_title('Cross-correlation for %s and %s' %
(x_label, y_label),
fontsize=15)
fig.savefig(os.path.abspath('dataset_diagnosis.png'),
bbox_inches='tight')
setattr(self, 'figure_dataset_diagnosis',
os.path.abspath('dataset_diagnosis.png'))
return runtime