def _run_interface(self, runtime):
# Get the mean EPI data and get it ready
epinii = nb.load(self.inputs.in_epi)
epidata = np.nan_to_num(epinii.get_data())
epidata = epidata.astype(np.float32)
epidata[epidata < 0] = 0
# Get EPI data (with mc done) and get it ready
hmcnii = nb.load(self.inputs.in_hmc)
hmcdata = np.nan_to_num(hmcnii.get_data())
hmcdata = hmcdata.astype(np.float32)
hmcdata[hmcdata < 0] = 0
# Get brain mask data
msknii = nb.load(self.inputs.in_mask)
mskdata = np.asanyarray(msknii.dataobj) > 0
mskdata = mskdata.astype(np.uint8)
if np.sum(mskdata) < 100:
raise RuntimeError(
"Detected less than 100 voxels belonging to the brain mask. "
"MRIQC failed to process this dataset.")
# Summary stats
stats = summary_stats(epidata, mskdata, erode=True)
self._results["summary"] = stats
# SNR
self._results["snr"] = snr(stats["fg"]["median"], stats["fg"]["stdv"],
stats["fg"]["n"])
# FBER
self._results["fber"] = fber(epidata, mskdata)
# EFC
self._results["efc"] = efc(epidata)
# GSR
self._results["gsr"] = {}
if self.inputs.direction == "all":
epidir = ["x", "y"]
else:
epidir = [self.inputs.direction]
for axis in epidir:
self._results["gsr"][axis] = gsr(epidata, mskdata, direction=axis)
# DVARS
dvars_avg = np.loadtxt(self.inputs.in_dvars,
skiprows=1,
usecols=list(range(3))).mean(axis=0)
dvars_col = ["std", "nstd", "vstd"]
self._results["dvars"] = {
key: float(val)
for key, val in zip(dvars_col, dvars_avg)
}
# tSNR
tsnr_data = nb.load(self.inputs.in_tsnr).get_data()
self._results["tsnr"] = float(np.median(tsnr_data[mskdata > 0]))
# FD
fd_data = np.loadtxt(self.inputs.in_fd, skiprows=1)
num_fd = np.float((fd_data > self.inputs.fd_thres).sum())
self._results["fd"] = {
"mean": float(fd_data.mean()),
"num": int(num_fd),
"perc": float(num_fd * 100 / (len(fd_data) + 1)),
}
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
hmcnii.header.get_zooms()[:3])
self._results["fwhm"] = {
"x": float(fwhm[0]),
"y": float(fwhm[1]),
"z": float(fwhm[2]),
"avg": float(np.average(fwhm)),
}
# Image specs
self._results["size"] = {
"x": int(hmcdata.shape[0]),
"y": int(hmcdata.shape[1]),
"z": int(hmcdata.shape[2]),
}
self._results["spacing"] = {
i: float(v)
for i, v in zip(["x", "y", "z"],
hmcnii.header.get_zooms()[:3])
}
try:
self._results["size"]["t"] = int(hmcdata.shape[3])
except IndexError:
pass
try:
self._results["spacing"]["tr"] = float(
hmcnii.header.get_zooms()[3])
except IndexError:
pass
self._results["out_qc"] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime): # pylint: disable=R0914
imnii = nb.load(self.inputs.in_file)
imdata = np.nan_to_num(imnii.get_data())
erode = np.all(
np.array(imnii.get_header().get_zooms()[:3], dtype=np.float32) <
1.2)
# Cast to float32
imdata = imdata.astype(np.float32)
# Remove negative values
imdata[imdata < 0] = 0
# Load image corrected for INU
inudata = np.nan_to_num(nb.load(self.inputs.in_noinu).get_data())
inudata[inudata < 0] = 0
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
headdata = nb.load(self.inputs.head_msk).get_data().astype(np.uint8)
# SNR
snrvals = []
self._results['snr'] = {}
for tlabel in ['csf', 'wm', 'gm']:
snrvals.append(snr(inudata, segdata, fglabel=tlabel, erode=erode))
self._results['snr'][tlabel] = snrvals[-1]
self._results['snr']['total'] = float(np.mean(snrvals))
snrvals = []
self._results['snrd'] = {
tlabel: snr_dietrich(inudata,
segdata,
airdata,
fglabel=tlabel,
erode=erode)
for tlabel in ['csf', 'wm', 'gm']
}
self._results['snrd']['total'] = float(
np.mean([val for _, val in list(self._results['snrd'].items())]))
# CNR
self._results['cnr'] = cnr(inudata, segdata)
# FBER
self._results['fber'] = fber(inudata, headdata)
# EFC
self._results['efc'] = efc(inudata)
# M2WM
self._results['wm2max'] = wm2max(imdata, segdata)
# Artifacts
self._results['qi_1'] = art_qi1(airdata, artdata)
# CJV
self._results['cjv'] = cjv(inudata, seg=segdata)
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
imnii.get_header().get_zooms()[:3])
self._results['fwhm'] = {
'x': float(fwhm[0]),
'y': float(fwhm[1]),
'z': float(fwhm[2]),
'avg': float(np.average(fwhm))
}
# ICVs
self._results['icvs'] = volume_fraction(pvmdata)
# RPVE
self._results['rpve'] = rpve(pvmdata, segdata)
# Summary stats
self._results['summary'] = summary_stats(imdata, pvmdata, airdata)
# Image specs
self._results['size'] = {
'x': int(imdata.shape[0]),
'y': int(imdata.shape[1]),
'z': int(imdata.shape[2])
}
self._results['spacing'] = {
i: float(v)
for i, v in zip(['x', 'y', 'z'],
imnii.get_header().get_zooms()[:3])
}
try:
self._results['size']['t'] = int(imdata.shape[3])
except IndexError:
pass
try:
self._results['spacing']['tr'] = float(
imnii.get_header().get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results['inu'] = {
'range':
float(np.abs(np.percentile(bias, 95.) - np.percentile(bias, 5.))),
'med':
float(np.median(bias))
} #pylint: disable=E1101
mni_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.mni_tpms]
in_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.in_pvms]
overlap = fuzzy_jaccard(in_tpms, mni_tpms)
self._results['tpm_overlap'] = {
'csf': overlap[0],
'gm': overlap[1],
'wm': overlap[2]
}
# Flatten the dictionary
self._results['out_qc'] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime):
# Get the mean EPI data and get it ready
epinii = nb.load(self.inputs.in_epi)
epidata = np.nan_to_num(epinii.get_data())
epidata = epidata.astype(np.float32)
epidata[epidata < 0] = 0
# Get EPI data (with mc done) and get it ready
hmcnii = nb.load(self.inputs.in_hmc)
hmcdata = np.nan_to_num(hmcnii.get_data())
hmcdata = hmcdata.astype(np.float32)
hmcdata[hmcdata < 0] = 0
# Get EPI data (with mc done) and get it ready
msknii = nb.load(self.inputs.in_mask)
mskdata = np.nan_to_num(msknii.get_data())
mskdata = mskdata.astype(np.uint8)
mskdata[mskdata < 0] = 0
mskdata[mskdata > 0] = 1
# SNR
self._results['snr'] = float(snr(epidata, mskdata, fglabel=1))
# FBER
self._results['fber'] = fber(epidata, mskdata)
# EFC
self._results['efc'] = efc(epidata)
# GSR
self._results['gsr'] = {}
if self.inputs.direction == 'all':
epidir = ['x', 'y']
else:
epidir = [self.inputs.direction]
for axis in epidir:
self._results['gsr'][axis] = gsr(epidata, mskdata, direction=axis)
# Summary stats
self._results['summary'] = summary_stats(epidata, mskdata)
# DVARS
dvars_avg = np.loadtxt(self.inputs.in_dvars,
skiprows=1,
usecols=list(range(3))).mean(axis=0)
dvars_col = ['std', 'nstd', 'vstd']
self._results['dvars'] = {
key: float(val)
for key, val in zip(dvars_col, dvars_avg)
}
# tSNR
tsnr_data = nb.load(self.inputs.in_tsnr).get_data()
self._results['tsnr'] = float(np.median(tsnr_data[mskdata > 0]))
# GCOR
self._results['gcor'] = gcor(hmcdata, mskdata)
# FD
fd_data = np.loadtxt(self.inputs.in_fd, skiprows=1)
num_fd = np.float((fd_data > self.inputs.fd_thres).sum())
self._results['fd'] = {
'mean': float(fd_data.mean()),
'num': int(num_fd),
'perc': float(num_fd * 100 / (len(fd_data) + 1))
}
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
hmcnii.get_header().get_zooms()[:3])
self._results['fwhm'] = {
'x': float(fwhm[0]),
'y': float(fwhm[1]),
'z': float(fwhm[2]),
'avg': float(np.average(fwhm))
}
# Image specs
self._results['size'] = {
'x': int(hmcdata.shape[0]),
'y': int(hmcdata.shape[1]),
'z': int(hmcdata.shape[2])
}
self._results['spacing'] = {
i: float(v)
for i, v in zip(['x', 'y', 'z'],
hmcnii.get_header().get_zooms()[:3])
}
try:
self._results['size']['t'] = int(hmcdata.shape[3])
except IndexError:
pass
try:
self._results['spacing']['tr'] = float(
hmcnii.get_header().get_zooms()[3])
except IndexError:
pass
self._results['out_qc'] = _flatten_dict(self._results)
return runtime
def _run_interface(self, runtime): # pylint: disable=R0914,E1101
imnii = nb.load(self.inputs.in_noinu)
erode = np.all(
np.array(imnii.header.get_zooms()[:3], dtype=np.float32) < 1.9)
# Load image corrected for INU
inudata = np.nan_to_num(imnii.get_data())
inudata[inudata < 0] = 0
# Load binary segmentation from FSL FAST
segnii = nb.load(self.inputs.in_segm)
segdata = segnii.get_data().astype(np.uint8)
# Load air, artifacts and head masks
airdata = nb.load(self.inputs.air_msk).get_data().astype(np.uint8)
artdata = nb.load(self.inputs.artifact_msk).get_data().astype(np.uint8)
headdata = nb.load(self.inputs.head_msk).get_data().astype(np.uint8)
rotdata = nb.load(self.inputs.rot_msk).get_data().astype(np.uint8)
# Load Partial Volume Maps (pvms) from FSL FAST
pvmdata = []
for fname in self.inputs.in_pvms:
pvmdata.append(nb.load(fname).get_data().astype(np.float32))
# Summary stats
stats = summary_stats(inudata, pvmdata, airdata, erode=erode)
self._results["summary"] = stats
# SNR
snrvals = []
self._results["snr"] = {}
for tlabel in ["csf", "wm", "gm"]:
snrvals.append(
snr(
stats[tlabel]["median"],
stats[tlabel]["stdv"],
stats[tlabel]["n"],
))
self._results["snr"][tlabel] = snrvals[-1]
self._results["snr"]["total"] = float(np.mean(snrvals))
snrvals = []
self._results["snrd"] = {
tlabel: snr_dietrich(stats[tlabel]["median"], stats["bg"]["mad"])
for tlabel in ["csf", "wm", "gm"]
}
self._results["snrd"]["total"] = float(
np.mean([val for _, val in list(self._results["snrd"].items())]))
# CNR
self._results["cnr"] = cnr(
stats["wm"]["median"],
stats["gm"]["median"],
sqrt(sum(stats[k]["stdv"]**2 for k in ["bg", "gm", "wm"])),
)
# FBER
self._results["fber"] = fber(inudata, headdata, rotdata)
# EFC
self._results["efc"] = efc(inudata, rotdata)
# M2WM
self._results["wm2max"] = wm2max(inudata, stats["wm"]["median"])
# Artifacts
self._results["qi_1"] = art_qi1(airdata, artdata)
# CJV
self._results["cjv"] = cjv(
# mu_wm, mu_gm, sigma_wm, sigma_gm
stats["wm"]["median"],
stats["gm"]["median"],
stats["wm"]["mad"],
stats["gm"]["mad"],
)
# FWHM
fwhm = np.array(self.inputs.in_fwhm[:3]) / np.array(
imnii.header.get_zooms()[:3])
self._results["fwhm"] = {
"x": float(fwhm[0]),
"y": float(fwhm[1]),
"z": float(fwhm[2]),
"avg": float(np.average(fwhm)),
}
# ICVs
self._results["icvs"] = volume_fraction(pvmdata)
# RPVE
self._results["rpve"] = rpve(pvmdata, segdata)
# Image specs
self._results["size"] = {
"x": int(inudata.shape[0]),
"y": int(inudata.shape[1]),
"z": int(inudata.shape[2]),
}
self._results["spacing"] = {
i: float(v)
for i, v in zip(["x", "y", "z"],
imnii.header.get_zooms()[:3])
}
try:
self._results["size"]["t"] = int(inudata.shape[3])
except IndexError:
pass
try:
self._results["spacing"]["tr"] = float(imnii.header.get_zooms()[3])
except IndexError:
pass
# Bias
bias = nb.load(self.inputs.in_bias).get_data()[segdata > 0]
self._results["inu"] = {
"range":
float(np.abs(np.percentile(bias, 95.0) -
np.percentile(bias, 5.0))),
"med":
float(np.median(bias)),
} # pylint: disable=E1101
mni_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.mni_tpms]
in_tpms = [nb.load(tpm).get_data() for tpm in self.inputs.in_pvms]
overlap = fuzzy_jaccard(in_tpms, mni_tpms)
self._results["tpm_overlap"] = {
"csf": overlap[0],
"gm": overlap[1],
"wm": overlap[2],
}
# Flatten the dictionary
self._results["out_qc"] = _flatten_dict(self._results)
return runtime