def qap_functional_spatial(mean_epi,
func_brain_mask,
direction,
subject_id,
session_id,
scan_id,
site_name=None,
out_vox=True):
import os
import sys
from qap.spatial_qc import summary_mask, snr, fber, efc, fwhm, \
ghost_direction
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(mean_epi)
fg_mask = load_mask(func_brain_mask, mean_epi)
bg_mask = 1 - fg_mask
# Initialize QC
qc = dict(subject=subject_id, session=session_id, scan=scan_id)
if site_name:
qc['site'] = site_name
# FBER
qc['fber'] = fber(anat_data, fg_mask)
# EFC
qc['efc'] = efc(anat_data)
# Smoothness in voxels
tmp = fwhm(mean_epi, func_brain_mask, out_vox=out_vox)
qc['fwhm_x'], qc['fwhm_y'], qc['fwhm_z'], qc['fwhm'] = tmp
# Ghosting
if (direction == "all"):
qc['ghost_x'] = ghost_direction(anat_data, fg_mask, "x")
qc['ghost_y'] = ghost_direction(anat_data, fg_mask, "y")
qc['ghost_z'] = ghost_direction(anat_data, fg_mask, "z")
else:
qc['ghost_%s' % direction] = ghost_direction(anat_data, fg_mask,
direction)
# Summary Measures
qc['fg_mean'], qc['fg_std'], qc['fg_size'] = summary_mask(
anat_data, fg_mask)
qc['bg_mean'], qc['bg_std'], qc['bg_size'] = summary_mask(
anat_data, bg_mask)
qc['snr'] = None
# SNR
qc['snr'] = snr(qc['fg_mean'], qc['bg_std'])
return qc
def qap_functional_spatial(
mean_epi, func_brain_mask, direction, subject_id, session_id, scan_id, site_name=None, out_vox=True
):
import os
import sys
from qap.spatial_qc import summary_mask, snr, fber, efc, fwhm, ghost_direction
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(mean_epi)
fg_mask = load_mask(func_brain_mask, mean_epi)
bg_mask = 1 - fg_mask
# Initialize QC
qc = dict()
qc["subject"] = subject_id
qc["session"] = session_id
qc["scan"] = scan_id
if site_name:
qc["site"] = site_name
# FBER
qc["fber"] = fber(anat_data, fg_mask)
# EFC
qc["efc"] = efc(anat_data)
# Smoothness in voxels
tmp = fwhm(mean_epi, func_brain_mask, out_vox=out_vox)
qc["fwhm_x"], qc["fwhm_y"], qc["fwhm_z"], qc["fwhm"] = tmp
# Ghosting
if direction == "all":
qc["ghost_x"] = ghost_direction(anat_data, fg_mask, "x")
qc["ghost_y"] = ghost_direction(anat_data, fg_mask, "y")
qc["ghost_z"] = ghost_direction(anat_data, fg_mask, "z")
else:
qc["ghost_%s" % direction] = ghost_direction(anat_data, fg_mask, direction)
# Summary Measures
qc["fg_mean"], qc["fg_std"], qc["fg_size"] = summary_mask(anat_data, fg_mask)
qc["bg_mean"], qc["bg_std"], qc["bg_size"] = summary_mask(anat_data, bg_mask)
qc["snr"] = None
# SNR
qc["snr"] = snr(qc["fg_mean"], qc["bg_std"])
return qc
def test_summary_mask():
import os
import pickle
import pkg_resources as p
from qap.spatial_qc import summary_mask
from qap.qap_utils import load_image, load_mask
anat_reorient = p.resource_filename("qap", os.path.join(test_sub_dir, \
"anat_1", \
"anatomical_reorient", \
"mprage_resample.nii.gz"))
head_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"anat_1", \
"qap_head_mask", \
"mprage_resample_thresh_maths_maths_" \
"maths.nii.gz"))
anat_data = load_image(anat_reorient)
mask_data = load_mask(head_mask, anat_reorient)
summary_tuple = summary_mask(anat_data, mask_data)
assert summary_tuple == (252.25360846882191, 288.84526666983822, 6449165)
def test_summary_mask():
import os
import pickle
import pkg_resources as p
from qap.spatial_qc import summary_mask
from qap.qap_utils import load_image, load_mask
anat_reorient = p.resource_filename("qap", os.path.join(test_sub_dir, \
"anat_reorient.nii.gz"))
head_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"qap_head_mask.nii.gz"))
anat_data = load_image(anat_reorient)
mask_data = load_mask(head_mask, anat_reorient)
summary_tuple = summary_mask(anat_data, mask_data)
assert int(summary_tuple[0]) == 230
assert int(summary_tuple[1]) == 233
assert int(summary_tuple[2]) == 157221
def test_summary_mask():
import os
import pickle
import pkg_resources as p
from qap.spatial_qc import summary_mask
from qap.qap_utils import load_image, load_mask
anat_reorient = p.resource_filename("qap", os.path.join(test_sub_dir, \
"anat_reorient.nii.gz"))
head_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"qap_head_mask.nii.gz"))
anat_data = load_image(anat_reorient)
mask_data = load_mask(head_mask, anat_reorient)
summary_tuple = summary_mask(anat_data, mask_data)
assert int(summary_tuple[0]) == 230
assert int(summary_tuple[1]) == 233
assert int(summary_tuple[2]) == 157221
def qap_anatomical_spatial(
anatomical_reorient,
head_mask_path,
anatomical_gm_mask,
anatomical_wm_mask,
anatomical_csf_mask,
subject_id,
session_id,
scan_id,
site_name=None,
out_vox=True,
):
import os
import sys
from qap.spatial_qc import summary_mask, snr, cnr, fber, efc, artifacts, fwhm
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(anatomical_reorient)
fg_mask = load_mask(head_mask_path, anatomical_reorient)
bg_mask = 1 - fg_mask
gm_mask = load_mask(anatomical_gm_mask, anatomical_reorient)
wm_mask = load_mask(anatomical_wm_mask, anatomical_reorient)
csf_mask = load_mask(anatomical_csf_mask, anatomical_reorient)
# Initialize QC
qc = dict()
qc["subject"] = subject_id
qc["session"] = session_id
qc["scan"] = scan_id
if site_name:
qc["site"] = site_name
# FBER
qc["fber"] = fber(anat_data, fg_mask)
# EFC
qc["efc"] = efc(anat_data)
# Artifact
qc["qi1"], _ = artifacts(anat_data, fg_mask, calculate_qi2=False)
# Smoothness in voxels
tmp = fwhm(anatomical_reorient, head_mask_path, out_vox=out_vox)
qc["fwhm_x"], qc["fwhm_y"], qc["fwhm_z"], qc["fwhm"] = tmp
# Summary Measures
qc["fg_mean"], qc["fg_std"], qc["fg_size"] = summary_mask(anat_data, fg_mask)
qc["bg_mean"], qc["bg_std"], qc["bg_size"] = summary_mask(anat_data, bg_mask)
qc["gm_mean"], qc["gm_std"], qc["gm_size"] = (None, None, None)
qc["wm_mean"], qc["wm_std"], qc["wm_size"] = (None, None, None)
qc["csf_mean"], qc["csf_std"], qc["csf_size"] = (None, None, None)
qc["cnr"] = None
qc["snr"] = None
# More Summary Measures
qc["gm_mean"], qc["gm_std"], qc["gm_size"] = summary_mask(anat_data, gm_mask)
qc["wm_mean"], qc["wm_std"], qc["wm_size"] = summary_mask(anat_data, wm_mask)
qc["csf_mean"], qc["csf_std"], qc["csf_size"] = summary_mask(anat_data, csf_mask)
# SNR
qc["snr"] = snr(qc["fg_mean"], qc["bg_std"])
# CNR
qc["cnr"] = cnr(qc["gm_mean"], qc["wm_mean"], qc["bg_std"])
return qc
def qap_functional_spatial(mean_epi, func_brain_mask, direction, subject_id,
session_id, scan_id, site_name=None, out_vox=True,
starter=None):
""" Calculate the functional spatial QAP measures for a functional scan.
- The inclusion of the starter node allows several QAP measure pipelines
which are not dependent on one another to be executed as one pipeline.
This allows the MultiProc Nipype plugin to efficiently manage
resources when parallelizing.
:type mean_epi: str
:param mean_epi: Filepath to the mean of the functional timeseries image
(should be 3D).
:type func_brain_mask: str
:param func_brain_mask: Filepath to the binary mask defining the brain
within the functional image.
:type direction: str
:param direction: For ghost-to-signal ratio; the phase-encoding direction
of the image - this is often "y".
:type subject_id: str
:param subject_id: The participant ID.
:type session_id: str
:param session_id: The session ID.
:type scan_id: str
:param scan_id: The scan ID.
:type site_name: str
:param site_name: (default: None) The name of the site where the scan was
acquired.
:type out_vox: bool
:param out_vox: (default: True) For FWHM measure: output the FWHM as
number of voxels (otherwise as mm).
:type starter: str
:param starter: (default: None) If this function is being pulled into a
Nipype pipeline, this is the dummy input for the function
node.
:rtype: dict
:return: A dictionary mapping out the QAP measure values for the current
participant.
"""
from time import strftime
import qap
from qap.spatial_qc import summary_mask, snr, fber, efc, fwhm, \
ghost_direction
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(mean_epi)
fg_mask = load_mask(func_brain_mask, mean_epi)
bg_mask = 1 - fg_mask
# FBER
fber_out = fber(anat_data, fg_mask, bg_mask)
# EFC
efc_out = efc(anat_data)
# Smoothness in voxels
tmp = fwhm(mean_epi, func_brain_mask, out_vox=out_vox)
fwhm_x, fwhm_y, fwhm_z, fwhm_out = tmp
# Summary Measures
fg_mean, fg_std, fg_size = summary_mask(anat_data, fg_mask)
bg_mean, bg_std, bg_size = summary_mask(anat_data, bg_mask)
# SNR
snr_out = snr(fg_mean, bg_std)
id_string = "%s %s %s" % (subject_id, session_id, scan_id)
qc = {
id_string:
{
"QAP_pipeline_id": "QAP version %s" % qap.__version__,
"Time": strftime("%Y-%m-%d %H:%M:%S"),
"Participant": str(subject_id),
"Session": str(session_id),
"Series": str(scan_id),
"functional_spatial":
{
"FBER": fber_out,
"EFC": efc_out,
"FWHM": fwhm_out,
"FWHM_x": fwhm_x,
"FWHM_y": fwhm_y,
"FWHM_z": fwhm_z,
"SNR": snr_out
}
}
}
# Ghosting
if (direction == "all"):
qc[id_string]["functional_spatial"]['Ghost_x'] = \
ghost_direction(anat_data, fg_mask, "x")
qc[id_string]["functional_spatial"]['Ghost_y'] = \
ghost_direction(anat_data, fg_mask, "y")
qc[id_string]["functional_spatial"]['Ghost_z'] = \
ghost_direction(anat_data, fg_mask, "z")
else:
qc[id_string]["functional_spatial"]['Ghost_%s' % direction] = \
ghost_direction(anat_data, fg_mask, direction)
if site_name:
qc[id_string]['Site'] = str(site_name)
for key in qc[id_string]["functional_spatial"].keys():
qc[id_string]["functional_spatial"][key] = \
str(qc[id_string]["functional_spatial"][key])
return qc
def qap_anatomical_spatial(anatomical_reorient, qap_head_mask_path,
whole_head_mask_path, skull_mask_path,
anatomical_gm_mask, anatomical_wm_mask,
anatomical_csf_mask, subject_id, session_id,
scan_id, site_name=None, exclude_zeroes=False,
out_vox=True, starter=None):
"""Calculate the anatomical spatial QAP measures for an anatomical scan.
- The exclude_zeroes flag is useful for when a large amount of zero
values have been artificially injected into the image, for example,
when removing the faces and ears in scans for privacy compliance
reasons; these zeroes can artificially skew the spatial quality metric
results and make it seem that there is far less noise or artifacts in
the image than there really is.
- The inclusion of the starter node allows several QAP measure pipelines
which are not dependent on one another to be executed as one pipeline.
This allows the MultiProc Nipype plugin to efficiently manage
resources when parallelizing.
:type anatomical_reorient: str
:param anatomical_reorient: Filepath to the reoriented anatomical scan.
:type qap_head_mask_path: str
:param qap_head_mask_path: Filepath to mask of the head, plus the slice
covering the region below the nose and in
front of the mouth.
:type whole_head_mask_path: str
:param whole_head_mask_path: Filepath to mask of the entire head only (no
slice in front of head).
:type skull_mask_path: str
:param skull_mask_path: Filepath to the mask of the upper portion of the
head only (the whole head mask subtracted by the
slice mask).
:type anatomical_gm_mask: str
:param anatomical_gm_mask: Filepath to the binary mask of the gray matter.
:type anatomical_wm_mask: str
:param anatomical_wm_mask: Filepath to the binary mask of the white
matter.
:type anatomical_csf_mask: str
:param anatomical_csf_mask: Filepath to the binary mask of the CSF.
:type subject_id: str
:param subject_id: The participant ID.
:type session_id: str
:param session_id: The session ID.
:type scan_id: str
:param scan_id: The scan ID.
:type site_name: str
:param site_name: (default: None) The name of the site where the scan was
acquired.
:type exclude_zeroes: bool
:param exclude_zeroes: (default: False) Whether or not to exclude the
pure zero values when defining the background mask.
:type out_vox: bool
:param out_vox: (default: True) For FWHM measure: output the FWHM as
number of voxels (otherwise as mm).
:type starter: str
:param starter: (default: None) If this function is being pulled into a
Nipype pipeline, this is the dummy input for the function
node.
:rtype: dict
:return: A dictionary mapping out the QAP measure values for the current
participant.
"""
from time import strftime
import qap
from qap.spatial_qc import summary_mask, snr, cnr, fber, efc, \
artifacts, fwhm, cortical_contrast
from qap.qap_utils import load_image, load_mask, \
create_anatomical_background_mask
# Load the data
anat_data = load_image(anatomical_reorient)
fg_mask = load_mask(qap_head_mask_path, anatomical_reorient)
# bg_mask is the inversion of the "qap_head_mask"
bg_mask = create_anatomical_background_mask(anat_data, fg_mask,
exclude_zeroes)
whole_head_mask = load_mask(whole_head_mask_path, anatomical_reorient)
skull_mask = load_mask(skull_mask_path, anatomical_reorient)
gm_mask = load_mask(anatomical_gm_mask, anatomical_reorient)
wm_mask = load_mask(anatomical_wm_mask, anatomical_reorient)
csf_mask = load_mask(anatomical_csf_mask, anatomical_reorient)
# FBER
fber_out = fber(anat_data, skull_mask, bg_mask)
# EFC
efc_out = efc(anat_data)
# Artifact
qi1, _ = artifacts(anat_data, fg_mask, bg_mask, calculate_qi2=False)
# Smoothness in voxels
tmp = fwhm(anatomical_reorient, whole_head_mask_path, out_vox=out_vox)
fwhm_x, fwhm_y, fwhm_z, fwhm_out = tmp
# Summary Measures
fg_mean, fg_std, fg_size = summary_mask(anat_data, whole_head_mask)
bg_mean, bg_std, bg_size = summary_mask(anat_data, bg_mask)
gm_mean, gm_std, gm_size = (None, None, None)
wm_mean, wm_std, wm_size = (None, None, None)
csf_mean, csf_std, csf_size = (None, None, None)
# More Summary Measures
gm_mean, gm_std, gm_size = summary_mask(anat_data, gm_mask)
wm_mean, wm_std, wm_size = summary_mask(anat_data, wm_mask)
csf_mean, csf_std, csf_size = summary_mask(anat_data, csf_mask)
# SNR
snr_out = snr(fg_mean, bg_std)
# CNR
cnr_out = cnr(gm_mean, wm_mean, bg_std)
# Cortical contrast
cort_out = cortical_contrast(gm_mean, wm_mean)
id_string = "%s %s %s" % (subject_id, session_id, scan_id)
qc = {
id_string:
{
"QAP_pipeline_id": "QAP version %s" % qap.__version__,
"Time": strftime("%Y-%m-%d %H:%M:%S"),
"Participant": str(subject_id),
"Session": str(session_id),
"Series": str(scan_id),
"anatomical_spatial":
{
"FBER": fber_out,
"EFC": efc_out,
"Qi1": qi1,
"FWHM_x": fwhm_x,
"FWHM_y": fwhm_y,
"FWHM_z": fwhm_z,
"FWHM": fwhm_out,
"CNR": cnr_out,
"SNR": snr_out,
"Cortical Contrast": cort_out
}
}
}
if site_name:
qc[id_string]['Site'] = str(site_name)
if exclude_zeroes:
qc[id_string]['_zeros_excluded'] = "True"
for key in qc[id_string]["anatomical_spatial"].keys():
qc[id_string]["anatomical_spatial"][key] = \
str(qc[id_string]["anatomical_spatial"][key])
return qc
def qap_functional_spatial(mean_epi,
func_brain_mask,
direction,
subject_id,
session_id,
scan_id,
site_name=None,
out_vox=True,
starter=None):
""" Calculate the functional spatial QAP measures for a functional scan.
- The inclusion of the starter node allows several QAP measure pipelines
which are not dependent on one another to be executed as one pipeline.
This allows the MultiProc Nipype plugin to efficiently manage
resources when parallelizing.
:type mean_epi: str
:param mean_epi: Filepath to the mean of the functional timeseries image
(should be 3D).
:type func_brain_mask: str
:param func_brain_mask: Filepath to the binary mask defining the brain
within the functional image.
:type direction: str
:param direction: For ghost-to-signal ratio; the phase-encoding direction
of the image - this is often "y".
:type subject_id: str
:param subject_id: The participant ID.
:type session_id: str
:param session_id: The session ID.
:type scan_id: str
:param scan_id: The scan ID.
:type site_name: str
:param site_name: (default: None) The name of the site where the scan was
acquired.
:type out_vox: bool
:param out_vox: (default: True) For FWHM measure: output the FWHM as
number of voxels (otherwise as mm).
:type starter: str
:param starter: (default: None) If this function is being pulled into a
Nipype pipeline, this is the dummy input for the function
node.
:rtype: dict
:return: A dictionary mapping out the QAP measure values for the current
participant.
"""
from time import strftime
import qap
from qap.spatial_qc import summary_mask, snr, fber, efc, fwhm, \
ghost_direction
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(mean_epi)
fg_mask = load_mask(func_brain_mask, mean_epi)
bg_mask = 1 - fg_mask
# FBER
fber_out = fber(anat_data, fg_mask, bg_mask)
# EFC
efc_out = efc(anat_data)
# Smoothness in voxels
tmp = fwhm(mean_epi, func_brain_mask, out_vox=out_vox)
fwhm_x, fwhm_y, fwhm_z, fwhm_out = tmp
# Summary Measures
fg_mean, fg_std, fg_size = summary_mask(anat_data, fg_mask)
bg_mean, bg_std, bg_size = summary_mask(anat_data, bg_mask)
# SNR
snr_out = snr(fg_mean, bg_std)
id_string = "%s %s %s" % (subject_id, session_id, scan_id)
qc = {
id_string: {
"QAP_pipeline_id": "QAP version %s" % qap.__version__,
"Time": strftime("%Y-%m-%d %H:%M:%S"),
"Participant": str(subject_id),
"Session": str(session_id),
"Series": str(scan_id),
"functional_spatial": {
"FBER": fber_out,
"EFC": efc_out,
"FWHM": fwhm_out,
"FWHM_x": fwhm_x,
"FWHM_y": fwhm_y,
"FWHM_z": fwhm_z,
"SNR": snr_out
}
}
}
# Ghosting
if (direction == "all"):
qc[id_string]["functional_spatial"]['Ghost_x'] = \
ghost_direction(anat_data, fg_mask, "x")
qc[id_string]["functional_spatial"]['Ghost_y'] = \
ghost_direction(anat_data, fg_mask, "y")
qc[id_string]["functional_spatial"]['Ghost_z'] = \
ghost_direction(anat_data, fg_mask, "z")
else:
qc[id_string]["functional_spatial"]['Ghost_%s' % direction] = \
ghost_direction(anat_data, fg_mask, direction)
if site_name:
qc[id_string]['Site'] = str(site_name)
for key in qc[id_string]["functional_spatial"].keys():
qc[id_string]["functional_spatial"][key] = \
str(qc[id_string]["functional_spatial"][key])
return qc
def qap_anatomical_spatial(anatomical_reorient,
qap_head_mask_path,
whole_head_mask_path,
skull_mask_path,
anatomical_gm_mask,
anatomical_wm_mask,
anatomical_csf_mask,
subject_id,
session_id,
scan_id,
site_name=None,
exclude_zeroes=False,
out_vox=True,
starter=None):
"""Calculate the anatomical spatial QAP measures for an anatomical scan.
- The exclude_zeroes flag is useful for when a large amount of zero
values have been artificially injected into the image, for example,
when removing the faces and ears in scans for privacy compliance
reasons; these zeroes can artificially skew the spatial quality metric
results and make it seem that there is far less noise or artifacts in
the image than there really is.
- The inclusion of the starter node allows several QAP measure pipelines
which are not dependent on one another to be executed as one pipeline.
This allows the MultiProc Nipype plugin to efficiently manage
resources when parallelizing.
:type anatomical_reorient: str
:param anatomical_reorient: Filepath to the reoriented anatomical scan.
:type qap_head_mask_path: str
:param qap_head_mask_path: Filepath to mask of the head, plus the slice
covering the region below the nose and in
front of the mouth.
:type whole_head_mask_path: str
:param whole_head_mask_path: Filepath to mask of the entire head only (no
slice in front of head).
:type skull_mask_path: str
:param skull_mask_path: Filepath to the mask of the upper portion of the
head only (the whole head mask subtracted by the
slice mask).
:type anatomical_gm_mask: str
:param anatomical_gm_mask: Filepath to the binary mask of the gray matter.
:type anatomical_wm_mask: str
:param anatomical_wm_mask: Filepath to the binary mask of the white
matter.
:type anatomical_csf_mask: str
:param anatomical_csf_mask: Filepath to the binary mask of the CSF.
:type subject_id: str
:param subject_id: The participant ID.
:type session_id: str
:param session_id: The session ID.
:type scan_id: str
:param scan_id: The scan ID.
:type site_name: str
:param site_name: (default: None) The name of the site where the scan was
acquired.
:type exclude_zeroes: bool
:param exclude_zeroes: (default: False) Whether or not to exclude the
pure zero values when defining the background mask.
:type out_vox: bool
:param out_vox: (default: True) For FWHM measure: output the FWHM as
number of voxels (otherwise as mm).
:type starter: str
:param starter: (default: None) If this function is being pulled into a
Nipype pipeline, this is the dummy input for the function
node.
:rtype: dict
:return: A dictionary mapping out the QAP measure values for the current
participant.
"""
from time import strftime
import qap
from qap.spatial_qc import summary_mask, snr, cnr, fber, efc, \
artifacts, fwhm, cortical_contrast
from qap.qap_utils import load_image, load_mask, \
create_anatomical_background_mask
# Load the data
anat_data = load_image(anatomical_reorient)
fg_mask = load_mask(qap_head_mask_path, anatomical_reorient)
# bg_mask is the inversion of the "qap_head_mask"
bg_mask = create_anatomical_background_mask(anat_data, fg_mask,
exclude_zeroes)
whole_head_mask = load_mask(whole_head_mask_path, anatomical_reorient)
skull_mask = load_mask(skull_mask_path, anatomical_reorient)
gm_mask = load_mask(anatomical_gm_mask, anatomical_reorient)
wm_mask = load_mask(anatomical_wm_mask, anatomical_reorient)
csf_mask = load_mask(anatomical_csf_mask, anatomical_reorient)
# FBER
fber_out = fber(anat_data, skull_mask, bg_mask)
# EFC
efc_out = efc(anat_data)
# Artifact
qi1, _ = artifacts(anat_data, fg_mask, bg_mask, calculate_qi2=False)
# Smoothness in voxels
tmp = fwhm(anatomical_reorient, whole_head_mask_path, out_vox=out_vox)
fwhm_x, fwhm_y, fwhm_z, fwhm_out = tmp
# Summary Measures
fg_mean, fg_std, fg_size = summary_mask(anat_data, whole_head_mask)
bg_mean, bg_std, bg_size = summary_mask(anat_data, bg_mask)
gm_mean, gm_std, gm_size = (None, None, None)
wm_mean, wm_std, wm_size = (None, None, None)
csf_mean, csf_std, csf_size = (None, None, None)
# More Summary Measures
gm_mean, gm_std, gm_size = summary_mask(anat_data, gm_mask)
wm_mean, wm_std, wm_size = summary_mask(anat_data, wm_mask)
csf_mean, csf_std, csf_size = summary_mask(anat_data, csf_mask)
# SNR
snr_out = snr(fg_mean, bg_std)
# CNR
cnr_out = cnr(gm_mean, wm_mean, bg_std)
# Cortical contrast
cort_out = cortical_contrast(gm_mean, wm_mean)
id_string = "%s %s %s" % (subject_id, session_id, scan_id)
qc = {
id_string: {
"QAP_pipeline_id": "QAP version %s" % qap.__version__,
"Time": strftime("%Y-%m-%d %H:%M:%S"),
"Participant": str(subject_id),
"Session": str(session_id),
"Series": str(scan_id),
"anatomical_spatial": {
"FBER": fber_out,
"EFC": efc_out,
"Qi1": qi1,
"FWHM_x": fwhm_x,
"FWHM_y": fwhm_y,
"FWHM_z": fwhm_z,
"FWHM": fwhm_out,
"CNR": cnr_out,
"SNR": snr_out,
"Cortical Contrast": cort_out
}
}
}
if site_name:
qc[id_string]['Site'] = str(site_name)
if exclude_zeroes:
qc[id_string]['_zeros_excluded'] = "True"
for key in qc[id_string]["anatomical_spatial"].keys():
qc[id_string]["anatomical_spatial"][key] = \
str(qc[id_string]["anatomical_spatial"][key])
return qc
def qap_anatomical_spatial(anatomical_reorient,
head_mask_path,
anatomical_gm_mask,
anatomical_wm_mask,
anatomical_csf_mask,
subject_id,
session_id,
scan_id,
site_name=None,
out_vox=True):
import os
import sys
from qap.spatial_qc import summary_mask, snr, cnr, fber, efc, \
artifacts, fwhm
from qap.qap_utils import load_image, load_mask
# Load the data
anat_data = load_image(anatomical_reorient)
fg_mask = load_mask(head_mask_path, anatomical_reorient)
bg_mask = 1 - fg_mask
gm_mask = load_mask(anatomical_gm_mask, anatomical_reorient)
wm_mask = load_mask(anatomical_wm_mask, anatomical_reorient)
csf_mask = load_mask(anatomical_csf_mask, anatomical_reorient)
# Initialize QC
qc = dict()
qc['subject'] = subject_id
qc['session'] = session_id
qc['scan'] = scan_id
if site_name:
qc['site'] = site_name
# FBER
qc['fber'] = fber(anat_data, fg_mask)
# EFC
qc['efc'] = efc(anat_data)
# Artifact
qc['qi1'], _ = artifacts(anat_data, fg_mask, calculate_qi2=False)
# Smoothness in voxels
tmp = fwhm(anatomical_reorient, head_mask_path, out_vox=out_vox)
qc['fwhm_x'], qc['fwhm_y'], qc['fwhm_z'], qc['fwhm'] = tmp
# Summary Measures
qc['fg_mean'], qc['fg_std'], qc['fg_size'] = summary_mask(
anat_data, fg_mask)
qc['bg_mean'], qc['bg_std'], qc['bg_size'] = summary_mask(
anat_data, bg_mask)
qc['gm_mean'], qc['gm_std'], qc['gm_size'] = (None, None, None)
qc['wm_mean'], qc['wm_std'], qc['wm_size'] = (None, None, None)
qc['csf_mean'], qc['csf_std'], qc['csf_size'] = (None, None, None)
qc['cnr'] = None
qc['snr'] = None
# More Summary Measures
qc['gm_mean'], qc['gm_std'], qc['gm_size'] = summary_mask(
anat_data, gm_mask)
qc['wm_mean'], qc['wm_std'], qc['wm_size'] = summary_mask(
anat_data, wm_mask)
qc['csf_mean'], qc['csf_std'], qc['csf_size'] = summary_mask(
anat_data, csf_mask)
# SNR
qc['snr'] = snr(qc['fg_mean'], qc['bg_std'])
# CNR
qc['cnr'] = cnr(qc['gm_mean'], qc['wm_mean'], qc['bg_std'])
return qc