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):
# 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
mean, stdv, p95, p05 = summary_stats(epidata, mskdata)
self._results['summary'] = {'mean': mean,
'stdv': stdv,
'p95': p95,
'p05': p05}
# DVARS
self._results['dvars'] = float(np.loadtxt(
self.inputs.in_dvars).mean())
# tSNR
tsnr_data = nb.load(self.inputs.in_tsnr).get_data()
self._results['m_tsnr'] = float(np.median(tsnr_data[mskdata > 0]))
# GCOR
self._results['gcor'] = gcor(hmcdata, mskdata)
# FD
self._results['fd_stats'] = summary_fd(self.inputs.fd_movpar)
# 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):
# 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):
# 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
mean, stdv, p95, p05 = summary_stats(epidata, mskdata)
self._results["summary"] = {"mean": mean, "stdv": stdv, "p95": p95, "p05": p05}
# DVARS
dvars_avg = np.loadtxt(self.inputs.in_dvars).mean(axis=0)
dvars_col = ["std", "nstd", "vstd"]
self._results["dvars"] = {dvars_col[i]: float(val) for i, val in enumerate(dvars_avg)}
# tSNR
tsnr_data = nb.load(self.inputs.in_tsnr).get_data()
self._results["m_tsnr"] = float(np.median(tsnr_data[mskdata > 0]))
# GCOR
self._results["gcor"] = gcor(hmcdata, mskdata)
# FD
fd_data = np.loadtxt(self.inputs.in_fd)
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)),
}
# 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