def qap_functional_temporal(
func_motion_correct,
func_brain_mask,
coord_xfm_matrix,
subject_id,
session_id,
scan_id,
site_name=None,
motion_threshold=1.0,
):
import sys
from qap.temporal_qc import (
mean_dvars_wrapper,
summarize_fd,
mean_outlier_timepoints,
mean_quality_timepoints,
global_correlation,
)
# DVARS
mean_dvars = mean_dvars_wrapper(func_motion_correct, func_brain_mask)
# Mean FD (Jenkinson)
(mean_fd, num_fd, percent_fd) = summarize_fd(coord_xfm_matrix, threshold=motion_threshold)
# 3dTout
mean_outlier = mean_outlier_timepoints(func_motion_correct, func_brain_mask)
# 3dTqual
mean_quality = mean_quality_timepoints(func_motion_correct)
# new thing
gcor = global_correlation(func_motion_correct, func_brain_mask)
# Compile
qc = {
"subject": subject_id,
"session": session_id,
"scan": scan_id,
"dvars": mean_dvars,
"mean_fd": mean_fd,
"num_fd": num_fd,
"perc_fd": percent_fd,
"outlier": mean_outlier,
"quality": mean_quality,
"gcor": gcor,
}
if site_name:
qc["site"] = site_name
return qc
def qap_functional_temporal(func_motion_correct,
func_brain_mask,
tsnr_volume,
fd_file,
subject_id,
session_id,
scan_id,
site_name=None,
motion_threshold=1.0):
import sys
import nibabel as nb
import numpy as np
from qap.temporal_qc import mean_dvars_wrapper, mean_outlier_timepoints, \
mean_quality_timepoints, global_correlation
# DVARS
mean_dvars = mean_dvars_wrapper(func_motion_correct, func_brain_mask)
# Mean FD (Jenkinson)
fd = np.loadtxt(fd_file)
# Calculate Outliers
# Number and Percent of frames (time points) where
# movement (FD) exceeded threshold
num_fd = np.float((fd > motion_threshold).sum())
percent_fd = (num_fd * 100) / (len(fd) + 1)
# 3dTout
mean_outlier = mean_outlier_timepoints(func_motion_correct,
func_brain_mask)
# 3dTqual
mean_quality = mean_quality_timepoints(func_motion_correct)
# new thing
gcor = global_correlation(func_motion_correct, func_brain_mask)
# tSNR
tsnr_data = nb.load(tsnr_volume).get_data()
msk_data = np.ones_like(tsnr_data)
try:
msk_data = nb.load(func_brain_mask).get_data()
except:
pass
# Compile
qc = {
"subject": subject_id,
"session": session_id,
"scan": scan_id,
"dvars": mean_dvars,
"m_tsnr": np.median(tsnr_data[msk_data > 0]),
"mean_fd": fd.mean(),
'num_fd': num_fd,
'perc_fd': percent_fd,
"outlier": mean_outlier,
"quality": mean_quality,
"gcor": gcor
}
if site_name:
qc['site'] = site_name
return qc
def qap_functional_temporal(
func_motion_correct,
func_brain_mask,
tsnr_volume,
fd_file,
subject_id,
session_id,
scan_id,
site_name=None,
motion_threshold=1.0,
):
import sys
import nibabel as nb
import numpy as np
from qap.temporal_qc import mean_dvars_wrapper, mean_outlier_timepoints, mean_quality_timepoints, global_correlation
# DVARS
mean_dvars = mean_dvars_wrapper(func_motion_correct, func_brain_mask)
# Mean FD (Jenkinson)
fd = np.loadtxt(fd_file)
# Calculate Outliers
# Number and Percent of frames (time points) where
# movement (FD) exceeded threshold
num_fd = np.float((fd > motion_threshold).sum())
percent_fd = (num_fd * 100) / (len(fd) + 1)
# 3dTout
mean_outlier = mean_outlier_timepoints(func_motion_correct, func_brain_mask)
# 3dTqual
mean_quality = mean_quality_timepoints(func_motion_correct)
# new thing
gcor = global_correlation(func_motion_correct, func_brain_mask)
# tSNR
tsnr_data = nb.load(tsnr_volume).get_data()
msk_data = np.ones_like(tsnr_data)
try:
msk_data = nb.load(func_brain_mask).get_data()
except:
pass
# Compile
qc = {
"subject": subject_id,
"session": session_id,
"scan": scan_id,
"dvars": mean_dvars,
"m_tsnr": np.median(tsnr_data[msk_data > 0]),
"mean_fd": fd.mean(),
"num_fd": num_fd,
"perc_fd": percent_fd,
"outlier": mean_outlier,
"quality": mean_quality,
"gcor": gcor,
}
if site_name:
qc["site"] = site_name
return qc