def test_ghost_direction():
import os
import pkg_resources as p
import numpy.testing as nt
from qap.spatial_qc import ghost_direction
from qap.qap_utils import load_image, load_mask
mean_epi = p.resource_filename("qap", os.path.join(test_sub_dir, \
"mean_functional.nii.gz"))
func_brain_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"functional_brain_mask" \
".nii.gz"))
mean_epi_data = load_image(mean_epi)
funcmask_data = load_mask(func_brain_mask, mean_epi)
gsr_out_x = ghost_direction(mean_epi_data, funcmask_data, "x")
gsr_out_y = ghost_direction(mean_epi_data, funcmask_data, "y")
gsr_out_z = ghost_direction(mean_epi_data, funcmask_data, "z")
gsr_out_all = (gsr_out_x, gsr_out_y, gsr_out_z)
nt.assert_almost_equal(gsr_out_all[0], -0.018987976014614105, decimal=4)
nt.assert_almost_equal(gsr_out_all[1], 0.020795321092009544, decimal=4)
nt.assert_almost_equal(gsr_out_all[2], 0.06708560138940811, decimal=4)
def test_ghost_direction():
import os
import pickle
import pkg_resources as p
from qap.spatial_qc import ghost_direction
from qap.qap_utils import load_image, load_mask
mean_epi = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"mean_functional", \
"rest_calc_tshift_resample_volreg_" \
"tstat.nii.gz"))
func_brain_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"rest_1", \
"functional_brain_mask", \
"rest_calc_tshift_resample_volreg" \
"_mask.nii.gz"))
mean_epi_data = load_image(mean_epi)
funcmask_data = load_mask(func_brain_mask, mean_epi)
gsr_out_x = ghost_direction(mean_epi_data, funcmask_data, "x")
gsr_out_y = ghost_direction(mean_epi_data, funcmask_data, "y")
gsr_out_z = ghost_direction(mean_epi_data, funcmask_data, "z")
gsr_out_all = (gsr_out_x, gsr_out_y, gsr_out_z)
print gsr_out_all
assert gsr_out_all == (-0.013489312, 0.016911652, 0.080058813)
def test_ghost_direction():
import os
import pkg_resources as p
import numpy.testing as nt
from qap.spatial_qc import ghost_direction
from qap.qap_utils import load_image, load_mask
mean_epi = p.resource_filename("qap", os.path.join(test_sub_dir, \
"mean_functional.nii.gz"))
func_brain_mask = p.resource_filename("qap", os.path.join(test_sub_dir, \
"functional_brain_mask" \
".nii.gz"))
mean_epi_data = load_image(mean_epi)
funcmask_data = load_mask(func_brain_mask, mean_epi)
gsr_out_x = ghost_direction(mean_epi_data, funcmask_data, "x")
gsr_out_y = ghost_direction(mean_epi_data, funcmask_data, "y")
gsr_out_z = ghost_direction(mean_epi_data, funcmask_data, "z")
gsr_out_all = (gsr_out_x, gsr_out_y, gsr_out_z)
nt.assert_almost_equal(gsr_out_all[0], -0.018987976014614105, decimal=4)
nt.assert_almost_equal(gsr_out_all[1], 0.020795321092009544, decimal=4)
nt.assert_almost_equal(gsr_out_all[2], 0.06708560138940811, decimal=4)
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 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_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
