def fmriqa(infile,
TR,
outdir=None,
maskfile=None,
motfile=None,
verbose=False,
plot_data=True):
save_sfnr = True
if os.path.dirname(infile) == '':
basedir = os.getcwd()
infile = os.path.join(basedir, infile)
elif os.path.dirname(infile) == '.':
basedir = os.getcwd()
infile = os.path.join(basedir, infile.replace('./', ''))
else:
basedir = os.path.dirname(infile)
if outdir == None:
outdir = basedir
qadir = os.path.join(outdir, 'QA')
if not infile.find('mcf.nii.gz') > 0:
error_and_exit('infile must be of form XXX_mcf.nii.gz')
if not os.path.exists(infile):
error_and_exit('%s does not exist!' % infile)
if maskfile == None:
maskfile = infile.replace('mcf.nii', 'mcf_brain_mask.nii')
if not os.path.exists(maskfile):
error_and_exit('%s does not exist!' % maskfile)
if motfile == None:
motfile = infile.replace('mcf.nii.gz', 'mcf.par')
if not os.path.exists(motfile):
error_and_exit('%s does not exist!' % motfile)
if not os.path.exists(qadir):
os.mkdir(qadir)
else:
print 'QA dir already exists - overwriting!'
if verbose:
print 'infile:', infile
print 'maskfile:', maskfile
print 'motfile:', motfile
print 'outdir:', outdir
print 'computing image stats'
img = nib.load(infile)
imgdata = img.get_data()
nslices = imgdata.shape[2]
ntp = imgdata.shape[3]
maskimg = nib.load(maskfile)
maskdata = maskimg.get_data()
maskvox = N.where(maskdata > 0)
nonmaskvox = N.where(maskdata == 0)
if verbose:
print 'nmaskvox:', len(maskvox[0])
# load motion parameters and compute FD and identify bad vols for
# potential scrubbing (ala Power et al.)
motpars = N.loadtxt(motfile)
fd = compute_fd(motpars)
N.savetxt(os.path.join(qadir, 'fd.txt'), fd)
voxmean = N.mean(imgdata, 3)
voxstd = N.std(imgdata, 3)
voxcv = voxstd / N.abs(voxmean)
voxcv[N.isnan(voxcv)] = 0
voxcv[voxcv > 1] = 1
# compute timepoint statistics
maskmedian = N.zeros(imgdata.shape[3])
maskmean = N.zeros(imgdata.shape[3])
maskmad = N.zeros(imgdata.shape[3])
maskcv = N.zeros(imgdata.shape[3])
imgsnr = N.zeros(imgdata.shape[3])
for t in range(imgdata.shape[3]):
tmp = imgdata[:, :, :, t]
tmp_brain = tmp[maskvox]
tmp_nonbrain = tmp[nonmaskvox]
maskmad[t] = MAD(tmp_brain)
maskmedian[t] = N.median(tmp_brain)
maskmean[t] = N.mean(tmp_brain)
maskcv[t] = maskmad[t] / maskmedian[t]
imgsnr[t] = maskmean[t] / N.std(tmp_nonbrain)
# perform Greve et al./fBIRN spike detection
#1. Remove mean and temporal trend from each voxel.
#2. Compute temporal Z-score for each voxel.
#3. Average the absolute Z-score (AAZ) within a each slice and time point separately.
# This gives a matrix with number of rows equal to the number of slices (nSlices)
# and number of columns equal to the number of time points (nFrames).
#4. Compute new Z-scores using a jackknife across the slices (JKZ). For a given time point,
# remove one of the slices, compute the average and standard deviation of the AAZ across
# the remaining slices. Use these two numbers to compute a Z for the slice left out
# (this is the JKZ). The final Spike Measure is the absolute value of the JKZ (AJKZ).
# Repeat for all slices. This gives a new nSlices-by-nFrames matrix (see Figure 8).
# This procedure tends to remove components that are common across slices and so rejects motion.
if verbose:
print 'computing spike stats'
detrended_zscore = N.zeros(imgdata.shape)
detrended_data = N.zeros(imgdata.shape)
for i in range(len(maskvox[0])):
tmp = imgdata[maskvox[0][i], maskvox[1][i], maskvox[2][i], :]
tmp_detrended = detrend(tmp)
detrended_data[maskvox[0][i], maskvox[1][i],
maskvox[2][i], :] = tmp_detrended
detrended_zscore[maskvox[0][i], maskvox[1][i], maskvox[2][i], :] = (
tmp_detrended - N.mean(tmp_detrended)) / N.std(tmp_detrended)
loo = sklearn.cross_validation.LeaveOneOut(nslices)
AAZ = N.zeros((nslices, ntp))
for s in range(nslices):
for t in range(ntp):
AAZ[s, t] = N.mean(N.abs(detrended_zscore[:, :, s, t]))
JKZ = N.zeros((nslices, ntp))
if verbose:
print 'computing outliers'
for train, test in loo:
for tp in range(ntp):
train_mean = N.mean(AAZ[train, tp])
train_std = N.std(AAZ[train, tp])
JKZ[test, tp] = (AAZ[test, tp] - train_mean) / train_std
AJKZ = N.abs(JKZ)
spikes = []
if N.max(AJKZ) > AJKZ_thresh:
print 'Possible spike: Max AJKZ = %f' % N.max(AJKZ)
spikes = N.where(N.max(AJKZ, 0) > AJKZ_thresh)[0]
if len(spikes) > 0:
N.savetxt(os.path.join(qadir, 'spikes.txt'), spikes)
voxmean_detrended = N.mean(detrended_data, 3)
voxstd_detrended = N.std(detrended_data, 3)
voxsfnr = voxmean / voxstd
meansfnr = N.mean(voxsfnr[maskvox])
# create plots
#
#imgdata_flat=imgdata.reshape(N.prod(imgdata.shape))
#imgdata_nonzero=imgdata_flat[imgdata_flat>0.0]
scaledmean = (maskmean - N.mean(maskmean)) / N.std(maskmean)
mean_running_diff = N.zeros(maskmad.shape)
mean_running_diff = (maskmean[1:] - maskmean[:-1]) / (
(maskmean[1:] + maskmean[:-1]) / 2.0)
DVARS = N.zeros(fd.shape)
DVARS[1:] = N.sqrt(mean_running_diff**2) * 100.0
N.savetxt(os.path.join(qadir, 'dvars.txt'), DVARS)
badvol_index_orig = N.where((fd > FDthresh) * (DVARS > DVARSthresh))[
0] #bad volumes are those where both meanFD >0.5 mm AND DVARS >0.5.
print badvol_index_orig
print len(badvol_index_orig)
badvols = N.zeros(len(DVARS))
badvols[badvol_index_orig] = 1
badvols_expanded = badvols.copy()
for i in badvol_index_orig:
if i > (nback - 1):
start = i - nback
else:
start = 0
if i < (len(badvols) - nforward):
end = i + nforward + 1
else:
end = len(badvols)
#print i,start,end
badvols_expanded[start:end] = 1
badvols_expanded_index = N.where(badvols_expanded > 0)[0]
#print badvols_expanded_index
if len(badvols_expanded_index) > 0:
N.savetxt(os.path.join(qadir, 'scrubvols.txt'),
badvols_expanded_index,
fmt='%d')
# make scrubing design matrix - one colum per scrubbed timepoint
scrubdes = N.zeros((len(DVARS), len(badvols_expanded_index)))
for i in range(len(badvols_expanded_index)):
scrubdes[badvols_expanded_index[i], i] = 1
N.savetxt(os.path.join(qadir, 'scrubdes.txt'), scrubdes, fmt='%d')
else:
scrubdes = []
# save out complete confound file
confound_mtx = N.zeros((len(DVARS), 14))
confound_mtx[:, 0:6] = motpars
confound_mtx[1:, 6:12] = motpars[:-1, :] - motpars[1:, :] # derivs
confound_mtx[:, 12] = fd
confound_mtx[:, 13] = DVARS
if not scrubdes == []:
confound_mtx = N.hstack((confound_mtx, scrubdes))
N.savetxt(os.path.join(qadir, 'confound.txt'), confound_mtx)
#plot_timeseries(scaledmean,'Mean in-mask signal (Z-scored)',
# os.path.join(qadir,'scaledmaskmean.png'),spikes,'Potential spikes')
datavars = {
'imgsnr': imgsnr,
'meansfnr': meansfnr,
'spikes': spikes,
'badvols': badvols_expanded_index
}
if plot_data:
print 'before plot'
trend = plot_timeseries(maskmean,
'Mean signal (unfiltered)',
os.path.join(qadir, 'maskmean.png'),
plottrend=True,
ylabel='Mean MR signal')
print 'after plot'
datavars['trend'] = trend
plot_timeseries(maskmad,
'Median absolute deviation (robust SD)',
os.path.join(qadir, 'mad.png'),
ylabel='MAD')
plot_timeseries(
DVARS,
'DVARS (root mean squared signal derivative over brain mask)',
os.path.join(qadir, 'DVARS.png'),
plotline=0.5,
ylabel='DVARS')
plot_timeseries(fd,
'Framewise displacement',
os.path.join(qadir, 'fd.png'),
badvols_expanded_index,
'Timepoints to scrub (%d total)' % len(badvols),
plotline=0.5,
ylims=[0, 1],
ylabel='FD')
psd = matplotlib.mlab.psd(maskmean, NFFT=128, noverlap=96, Fs=1 / TR)
plt.clf()
fig = plt.figure(figsize=[10, 3])
fig.subplots_adjust(bottom=0.15)
plt.plot(psd[1][2:], N.log(psd[0][2:]))
plt.title('Log power spectrum of mean signal across mask')
plt.xlabel('frequency (secs)')
plt.ylabel('log power')
plt.savefig(os.path.join(qadir, 'meanpsd.png'), bbox_inches='tight')
plt.close()
plt.clf()
plt.imshow(AJKZ, vmin=0, vmax=AJKZ_thresh)
plt.xlabel('timepoints')
plt.ylabel('slices')
plt.title('Spike measure (absolute jackknife Z)')
plt.savefig(os.path.join(qadir, 'spike.png'), bbox_inches='tight')
plt.close()
if img.shape[0] < img.shape[1] and img.shape[0] < img.shape[2]:
orientation = 'saggital'
else:
orientation = 'axial'
mk_slice_mosaic(voxmean,
os.path.join(qadir, 'voxmean.png'),
'Image mean (with mask)',
contourdata=maskdata)
mk_slice_mosaic(voxcv, os.path.join(qadir, 'voxcv.png'), 'Image CV')
mk_slice_mosaic(voxsfnr, os.path.join(qadir, 'voxsfnr.png'),
'Image SFNR')
mk_report(infile, qadir, datavars)
# def save_vars(infile,qadir,datavars):
datafile = os.path.join(qadir, 'qadata.csv')
f = open(datafile, 'w')
f.write('SNR,%f\n' % N.mean(datavars['imgsnr']))
f.write('SFNR,%f\n' % datavars['meansfnr'])
#f.write('drift,%f\n'%datavars['trend'].params[1])
f.write('nspikes,%d\n' % len(datavars['spikes']))
f.write('nscrub,%d\n' % len(datavars['badvols']))
f.close()
if save_sfnr:
sfnrimg = nib.Nifti1Image(voxsfnr, img.get_affine())
sfnrimg.to_filename(os.path.join(qadir, 'voxsfnr.nii.gz'))
return qadir
def fmriqa(infile,TR,outdir=None,maskfile=None,motfile=None,verbose=False,plot_data=True):
save_sfnr=True
if os.path.dirname(infile)=='':
basedir=os.getcwd()
infile=os.path.join(basedir,infile)
elif os.path.dirname(infile)=='.':
basedir=os.getcwd()
infile=os.path.join(basedir,infile.replace('./',''))
else:
basedir=os.path.dirname(infile)
if outdir==None:
outdir=basedir
qadir=os.path.join(outdir,'QA')
if not infile.find('mcf.nii.gz')>0:
error_and_exit('infile must be of form XXX_mcf.nii.gz')
if not os.path.exists(infile):
error_and_exit('%s does not exist!'%infile)
if maskfile==None:
maskfile=infile.replace('mcf.nii','mcf_brain_mask.nii')
if not os.path.exists(maskfile):
error_and_exit('%s does not exist!'%maskfile)
if motfile==None:
motfile=infile.replace('mcf.nii.gz','mcf.par')
if not os.path.exists(motfile):
error_and_exit('%s does not exist!'%motfile)
if not os.path.exists(qadir):
os.mkdir(qadir)
else:
print 'QA dir already exists - overwriting!'
if verbose:
print 'infile:',infile
print 'maskfile:',maskfile
print 'motfile:',motfile
print 'outdir:',outdir
print 'computing image stats'
img=nib.load(infile)
imgdata=img.get_data()
nslices=imgdata.shape[2]
ntp=imgdata.shape[3]
maskimg=nib.load(maskfile)
maskdata=maskimg.get_data()
maskvox=N.where(maskdata>0)
nonmaskvox=N.where(maskdata==0)
if verbose:
print 'nmaskvox:',len(maskvox[0])
# load motion parameters and compute FD and identify bad vols for
# potential scrubbing (ala Power et al.)
motpars=N.loadtxt(motfile)
fd=compute_fd(motpars)
N.savetxt(os.path.join(qadir,'fd.txt'),fd)
voxmean=N.mean(imgdata,3)
voxstd=N.std(imgdata,3)
voxcv=voxstd/N.abs(voxmean)
voxcv[N.isnan(voxcv)]=0
voxcv[voxcv>1]=1
# compute timepoint statistics
maskmedian=N.zeros(imgdata.shape[3])
maskmean=N.zeros(imgdata.shape[3])
maskmad=N.zeros(imgdata.shape[3])
maskcv=N.zeros(imgdata.shape[3])
imgsnr=N.zeros(imgdata.shape[3])
for t in range(imgdata.shape[3]):
tmp=imgdata[:,:,:,t]
tmp_brain=tmp[maskvox]
tmp_nonbrain=tmp[nonmaskvox]
maskmad[t]=MAD(tmp_brain)
maskmedian[t]=N.median(tmp_brain)
maskmean[t]=N.mean(tmp_brain)
maskcv[t]=maskmad[t]/maskmedian[t]
imgsnr[t]=maskmean[t]/N.std(tmp_nonbrain)
# perform Greve et al./fBIRN spike detection
#1. Remove mean and temporal trend from each voxel.
#2. Compute temporal Z-score for each voxel.
#3. Average the absolute Z-score (AAZ) within a each slice and time point separately.
# This gives a matrix with number of rows equal to the number of slices (nSlices)
# and number of columns equal to the number of time points (nFrames).
#4. Compute new Z-scores using a jackknife across the slices (JKZ). For a given time point,
# remove one of the slices, compute the average and standard deviation of the AAZ across
# the remaining slices. Use these two numbers to compute a Z for the slice left out
# (this is the JKZ). The final Spike Measure is the absolute value of the JKZ (AJKZ).
# Repeat for all slices. This gives a new nSlices-by-nFrames matrix (see Figure 8).
# This procedure tends to remove components that are common across slices and so rejects motion.
if verbose:
print 'computing spike stats'
detrended_zscore=N.zeros(imgdata.shape)
detrended_data=N.zeros(imgdata.shape)
for i in range(len(maskvox[0])):
tmp=imgdata[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]
tmp_detrended=detrend(tmp)
detrended_data[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=tmp_detrended
detrended_zscore[maskvox[0][i],maskvox[1][i],maskvox[2][i],:]=(tmp_detrended - N.mean(tmp_detrended))/N.std(tmp_detrended)
loo=sklearn.cross_validation.LeaveOneOut(nslices)
AAZ=N.zeros((nslices,ntp))
for s in range(nslices):
for t in range(ntp):
AAZ[s,t]=N.mean(N.abs(detrended_zscore[:,:,s,t]))
JKZ=N.zeros((nslices,ntp))
if verbose:
print 'computing outliers'
for train,test in loo:
for tp in range(ntp):
train_mean=N.mean(AAZ[train,tp])
train_std=N.std(AAZ[train,tp])
JKZ[test,tp]=(AAZ[test,tp] - train_mean)/train_std
AJKZ=N.abs(JKZ)
spikes=[]
if N.max(AJKZ)>AJKZ_thresh:
print 'Possible spike: Max AJKZ = %f'%N.max(AJKZ)
spikes=N.where(N.max(AJKZ,0)>AJKZ_thresh)[0]
if len(spikes)>0:
N.savetxt(os.path.join(qadir,'spikes.txt'),spikes)
voxmean_detrended=N.mean(detrended_data,3)
voxstd_detrended=N.std(detrended_data,3)
voxsfnr=voxmean/voxstd
meansfnr=N.mean(voxsfnr[maskvox])
# create plots
#
#imgdata_flat=imgdata.reshape(N.prod(imgdata.shape))
#imgdata_nonzero=imgdata_flat[imgdata_flat>0.0]
scaledmean=(maskmean - N.mean(maskmean))/N.std(maskmean)
mean_running_diff=N.zeros(maskmad.shape)
mean_running_diff=(maskmean[1:]-maskmean[:-1])/((maskmean[1:]+maskmean[:-1])/2.0)
DVARS=N.zeros(fd.shape)
DVARS[1:]=N.sqrt(mean_running_diff**2)*100.0
N.savetxt(os.path.join(qadir,'dvars.txt'),DVARS)
badvol_index_orig=N.where((fd>FDthresh)*(DVARS>DVARSthresh))[0]
#print badvol_index_orig
badvols=N.zeros(len(DVARS))
badvols[badvol_index_orig]=1
badvols_expanded=badvols.copy()
for i in badvol_index_orig:
if i>(nback-1):
start=i-nback
else:
start=0
if i<(len(badvols)-nforward):
end=i+nforward+1
else:
end=len(badvols)
#print i,start,end
badvols_expanded[start:end]=1
badvols_expanded_index=N.where(badvols_expanded>0)[0]
#print badvols_expanded_index
if len(badvols_expanded_index)>0:
N.savetxt(os.path.join(qadir,'scrubvols.txt'),badvols_expanded_index,fmt='%d')
# make scrubing design matrix - one colum per scrubbed timepoint
scrubdes=N.zeros((len(DVARS),len(badvols_expanded_index)))
for i in range(len(badvols_expanded_index)):
scrubdes[badvols_expanded_index[i],i]=1
N.savetxt(os.path.join(qadir,'scrubdes.txt'),scrubdes,fmt='%d')
else:
scrubdes=[]
# save out complete confound file
confound_mtx=N.zeros((len(DVARS),14))
confound_mtx[:,0:6]=motpars
confound_mtx[1:,6:12]=motpars[:-1,:]-motpars[1:,:] # derivs
confound_mtx[:,12]=fd
confound_mtx[:,13]=DVARS
if not scrubdes==[]:
confound_mtx=N.hstack((confound_mtx,scrubdes))
N.savetxt(os.path.join(qadir,'confound.txt'),confound_mtx)
#plot_timeseries(scaledmean,'Mean in-mask signal (Z-scored)',
# os.path.join(qadir,'scaledmaskmean.png'),spikes,'Potential spikes')
datavars={'imgsnr':imgsnr,'meansfnr':meansfnr,'spikes':spikes,'badvols':badvols_expanded_index}
if plot_data:
print 'before plot'
trend=plot_timeseries(maskmean,'Mean signal (unfiltered)',os.path.join(qadir,'maskmean.png'),
plottrend=True,ylabel='Mean MR signal')
print 'after plot'
datavars['trend']=trend
plot_timeseries(maskmad,'Median absolute deviation (robust SD)',
os.path.join(qadir,'mad.png'),ylabel='MAD')
plot_timeseries(DVARS,'DVARS (root mean squared signal derivative over brain mask)',
os.path.join(qadir,'DVARS.png'),plotline=0.5,ylabel='DVARS')
plot_timeseries(fd,'Framewise displacement',os.path.join(qadir,'fd.png'),
badvols_expanded_index,'Timepoints to scrub (%d total)'%len(badvols),
plotline=0.5,ylims=[0,1],ylabel='FD')
psd=matplotlib.mlab.psd(maskmean,NFFT=128,noverlap=96,Fs=1/TR)
plt.clf()
fig=plt.figure(figsize=[10,3])
fig.subplots_adjust(bottom=0.15)
plt.plot(psd[1][2:],N.log(psd[0][2:]))
plt.title('Log power spectrum of mean signal across mask')
plt.xlabel('frequency (secs)')
plt.ylabel('log power')
plt.savefig(os.path.join(qadir,'meanpsd.png'),bbox_inches='tight')
plt.close()
plt.clf()
plt.imshow(AJKZ,vmin=0,vmax=AJKZ_thresh)
plt.xlabel('timepoints')
plt.ylabel('slices')
plt.title('Spike measure (absolute jackknife Z)')
plt.savefig(os.path.join(qadir,'spike.png'),bbox_inches='tight')
plt.close()
if img.shape[0]<img.shape[1] and img.shape[0]<img.shape[2]:
orientation='saggital'
else:
orientation='axial'
mk_slice_mosaic(voxmean,os.path.join(qadir,'voxmean.png'),'Image mean (with mask)',contourdata=maskdata)
mk_slice_mosaic(voxcv,os.path.join(qadir,'voxcv.png'),'Image CV')
mk_slice_mosaic(voxsfnr,os.path.join(qadir,'voxsfnr.png'),'Image SFNR')
mk_report(infile,qadir,datavars)
# def save_vars(infile,qadir,datavars):
datafile=os.path.join(qadir,'qadata.csv')
f=open(datafile,'w')
f.write('SNR,%f\n'%N.mean(datavars['imgsnr']))
f.write('SFNR,%f\n'%datavars['meansfnr'])
#f.write('drift,%f\n'%datavars['trend'].params[1])
f.write('nspikes,%d\n'%len(datavars['spikes']))
f.write('nscrub,%d\n'%len(datavars['badvols']))
f.close()
if save_sfnr:
sfnrimg=nib.Nifti1Image(voxsfnr,img.get_affine())
sfnrimg.to_filename(os.path.join(qadir,'voxsfnr.nii.gz'))
return qadir
def fmriqa(infile,
TR,
outdir,
maskfile,
motfile,
verbose=True,
plot_data=True):
from statsmodels.tsa.tsatools import detrend
import ctypes, sys, os
flags = sys.getdlopenflags()
sys.setdlopenflags(flags|ctypes.RTLD_GLOBAL)
import numpy as N
import nibabel as nib
from compute_fd import *
#from statsmodels.tsa.tsatools import detrend
import statsmodels.api
import matplotlib
import numpy as np
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sklearn.cross_validation
from matplotlib.backends.backend_pdf import PdfPages
from mk_slice_mosaic import *
from matplotlib.mlab import psd
from plot_timeseries import plot_timeseries
from MAD import MAD
from mk_report import mk_report
from fmriqa import error_and_exit
sys.setdlopenflags(flags)
# thresholds for scrubbing and spike detection
FDthresh=0.5 #for a volume to be scrubbed both FD and DVARS need to be above 0.5
DVARSthresh=0.5
AJKZ_thresh=25
# number of timepoints forward and back to scrub
nback=1
nforward=2
## function starts here
save_sfnr = True
if os.path.dirname(infile) == '':
basedir = os.getcwd()
infile = os.path.join(basedir, infile)
elif os.path.dirname(infile) == '.':
basedir = os.getcwd()
infile = os.path.join(basedir, infile.replace('./', ''))
else:
basedir = os.path.dirname(infile)
if outdir == None:
outdir = basedir
qadir = os.path.join(outdir)
print "%s" %(qadir)
#very stupid!!
#if not infile.find('rest2anat_denoised_scrubbed_intep.nii.gz') > 0:
# print "error in rest file"
# error_and_exit('infile must be of form rest2anat_denoised_scrubbed_intep.nii.gz')
if not os.path.exists(infile):
print "error in infile"
error_and_exit('%s does not exist!' % infile)
if maskfile == None:
maskfile = infile.replace('mcf.nii', 'mcf_brain_mask.nii')
if not os.path.exists(maskfile):
error_and_exit('%s does not exist!' % maskfile)
if motfile == None:
motfile = infile.replace('mcf.nii.gz', 'mcf.par')
if not os.path.exists(motfile):
error_and_exit('%s does not exist!' % motfile)
if not os.path.exists(qadir):
os.mkdir(qadir)
else:
print 'QA dir already exists - overwriting!'
if verbose:
print 'infile:', infile
print 'maskfile:', maskfile
print 'motfile:', motfile
print 'outdir:', outdir
print 'computing image stats'
img = nib.load(infile)
imgdata = img.get_data()
#print np.shape(imgdata)
#print imgdata.shape
nslices = imgdata.shape[2]
ntp = imgdata.shape[3]
maskimg = nib.load(maskfile)
maskdata = maskimg.get_data()
maskvox = N.where(maskdata > 0)
nonmaskvox = N.where(maskdata == 0)
if verbose:
print 'nmaskvox:', len(maskvox[0])
# load motion parameters and compute FD and identify bad vols for
# potential scrubbing (ala Power et al.)
motpars = N.loadtxt(motfile)
fd = compute_fd(motpars)
N.savetxt(os.path.join(qadir, 'fd.txt'), fd)
voxmean = N.mean(imgdata, 3) #temporal mean for each voxel 3 =4th dimension = time
voxstd = N.std(imgdata, 3) #temporal standard deviation for each voxel
voxcv = voxstd / N.abs(voxmean)
voxcv[N.isnan(voxcv)] = 0
voxcv[voxcv > 1] = 1
# compute timepoint statistics
maskmedian = N.zeros(imgdata.shape[3])
maskmean = N.zeros(imgdata.shape[3])
maskmad = N.zeros(imgdata.shape[3]) #median absolute deviation = median(abs(a-median(a)/const)
maskcv = N.zeros(imgdata.shape[3])
imgsnr = N.zeros(imgdata.shape[3])
for t in range(imgdata.shape[3]):
tmp = imgdata[:, :, :, t]
tmp_brain = tmp[maskvox]
tmp_nonbrain = tmp[nonmaskvox]
maskmad[t] = MAD(tmp_brain)
maskmedian[t] = N.median(tmp_brain)
maskmean[t] = N.mean(tmp_brain)
print maskmean[t]
maskcv[t] = maskmad[t] / maskmedian[t]
imgsnr[t] = maskmean[t] / N.std(tmp_nonbrain) #classical definition of standard deviation
# perform Greve et al./fBIRN spike detection
# 1. Remove mean and temporal trend from each voxel.
# 2. Compute temporal Z-score for each voxel. (z = (value of voxel(t)-temporal mean of voxel/standard deviation of voxel))
# 3. Average the absolute Z-score (AAZ) within a each slice and time point separately.
# This gives a matrix with number of rows equal to the number of slices (nSlices)
# and number of columns equal to the number of time points (nFrames).
# 4. Compute new Z-scores using a jackknife across the slices (JKZ).
#WIKIPEDIA: The jackknife estimator of a parameter is found by systematically leaving out each
#observation from a dataset and calculating the estimate and then finding the average of
#these calculations.
#For a given time point, remove one of the slices, compute the average and standard deviation of the AAZ across
# the remaining slices. Use these two numbers to compute a Z for the slice left out
# (this is the JKZ). The final Spike Measure is the absolute value of the JKZ (AJKZ).
# Repeat for all slices. This gives a new nSlices-by-nFrames matrix (see Figure 8).
# This procedure tends to remove components that are common across slices and so rejects motion.
if verbose:
print 'computing spike stats'
detrended_zscore = N.zeros(imgdata.shape)
detrended_data = N.zeros(imgdata.shape)
for i in range(len(maskvox[0])):
tmp = imgdata[maskvox[0][i], maskvox[1][i], maskvox[2][i], :]
tmp_detrended = detrend(tmp)
detrended_data[maskvox[0][i], maskvox[1][i], maskvox[2][i], :] = tmp_detrended
detrended_zscore[maskvox[0][i], maskvox[1][i], maskvox[2][i], :] = (tmp_detrended - N.mean(
tmp_detrended)) / N.std(tmp_detrended)
loo = sklearn.cross_validation.LeaveOneOut(nslices)#creates 30 pairs of trains and left-out slices
AAZ = N.zeros((nslices, ntp))
for s in range(nslices):
for t in range(ntp):
AAZ[s, t] = N.mean(N.abs(detrended_zscore[:, :, s, t]))
JKZ = N.zeros((nslices, ntp))
if verbose:
print 'computing outliers'
for train, test in loo: #train=other slices, test=left-out slice
for tp in range(ntp):
train_mean = N.mean(AAZ[train, tp])#mean of absolute Z-values of all other slices
train_std = N.std(AAZ[train, tp]) #std of absolute Z-values of all other slices
JKZ[test, tp] = (AAZ[test, tp] - train_mean) / train_std #new value for left-out slice is the deviation from all other slices.
AJKZ = N.abs(JKZ)
spikes = []
if N.max(AJKZ) > AJKZ_thresh:
print 'Possible spike: Max AJKZ = %f' % N.max(AJKZ)
spikes = N.where(N.max(AJKZ, 0) > AJKZ_thresh)[0]
if len(spikes) > 0:
N.savetxt(os.path.join(qadir, 'spikes.txt'), spikes)
voxmean_detrended = N.mean(detrended_data, 3)
voxstd_detrended = N.std(detrended_data, 3)
voxsfnr = voxmean/ voxstd #needs to keep the same shape for later plotting
voxsfnr_withoutz = voxmean[voxstd>0] / voxstd[voxstd>0]
#temporal tsnr for each voxel
#only use voxels where voxstd >0
#previously it was like this -> creates Nans sometimes
#voxsfnr = voxmean/ voxstd
print "size of voxel standard deviation"
print np.shape
print np.size(voxstd[maskvox])
print np.count_nonzero(voxstd[maskvox])
print np.size(voxstd[voxstd>0])
print "resulting voxsfnr"
print N.mean(voxsfnr)
meansfnr = N.mean(voxsfnr_withoutz)#mean tsnr across all voxels in mask = same as in Julias scripts
# create plots
#
# imgdata_flat=imgdata.reshape(N.prod(imgdata.shape))
# imgdata_nonzero=imgdata_flat[imgdata_flat>0.0]
scaledmean = (maskmean - N.mean(maskmean)) / N.std(maskmean)
mean_running_diff = N.zeros(maskmad.shape)
mean_running_diff = (maskmean[1:] - maskmean[:-1]) / ((maskmean[1:] + maskmean[:-1]) / 2.0)
DVARS = N.zeros(fd.shape)
DVARS[1:] = N.sqrt(mean_running_diff ** 2) * 100.0
N.savetxt(os.path.join(qadir, 'dvars.txt'), DVARS)
badvol_index_orig = N.where((fd > FDthresh) * (DVARS > DVARSthresh))[0] #FD and DVARS simultaneously have to be bigger than 0.5!!
#print badvol_index_orig
#print len(badvol_index_orig)
badvols = N.zeros(len(DVARS))
badvols[badvol_index_orig] = 1
badvols_expanded = badvols.copy()
for i in badvol_index_orig:
if i > (nback - 1):
start = i - nback
else:
start = 0
if i < (len(badvols) - nforward):
end = i + nforward + 1
else:
end = len(badvols)
# print i,start,end
badvols_expanded[start:end] = 1
badvols_expanded_index = N.where(badvols_expanded > 0)[0]
# print badvols_expanded_index
if len(badvols_expanded_index) > 0:
N.savetxt(os.path.join(qadir, 'scrubvols.txt'), badvols_expanded_index, fmt='%d')
# make scrubing design matrix - one colum per scrubbed timepoint
scrubdes = N.zeros((len(DVARS), len(badvols_expanded_index)))
for i in range(len(badvols_expanded_index)):
scrubdes[badvols_expanded_index[i], i] = 1
N.savetxt(os.path.join(qadir, 'scrubdes.txt'), scrubdes, fmt='%d')
else:
scrubdes = []
# save out complete confound file
confound_mtx = N.zeros((len(DVARS), 14))
confound_mtx[:, 0:6] = motpars
confound_mtx[1:, 6:12] = motpars[:-1, :] - motpars[1:, :] # derivs
confound_mtx[:, 12] = fd
confound_mtx[:, 13] = DVARS
if not scrubdes == []:
confound_mtx = N.hstack((confound_mtx, scrubdes))
N.savetxt(os.path.join(qadir, 'confound.txt'), confound_mtx)
# plot_timeseries(scaledmean,'Mean in-mask signal (Z-scored)',
# os.path.join(qadir,'scaledmaskmean.png'),spikes,'Potential spikes')
datavars = {'imgsnr': imgsnr, 'meansfnr': meansfnr, 'spikes': spikes, 'badvols': badvols_expanded_index}
if plot_data:
print 'before plot'
trend = plot_timeseries(maskmean, 'Mean signal (unfiltered)', os.path.join(qadir, 'maskmean.png'),
plottrend=True, ylabel='Mean MR signal')
print 'after plot'
datavars['trend'] = trend
plot_timeseries(maskmad, 'Median absolute deviation (robust SD)',
os.path.join(qadir, 'mad.png'), ylabel='MAD')
plot_timeseries(DVARS, 'DVARS (root mean squared signal derivative over brain mask)',
os.path.join(qadir, 'DVARS.png'), plotline=0.5, ylabel='DVARS')
plot_timeseries(fd, 'Framewise displacement', os.path.join(qadir, 'fd.png'),
badvols_expanded_index, 'Timepoints to scrub (%d total)' % len(badvols),
plotline=0.5, ylims=[0, 1], ylabel='FD')
psd = matplotlib.mlab.psd(maskmean, NFFT=128, noverlap=96, Fs=1 / TR)
plt.clf()
fig = plt.figure(figsize=[10, 3])
fig.subplots_adjust(bottom=0.15)
plt.plot(psd[1][2:], N.log(psd[0][2:]))
plt.title('Log power spectrum of mean signal across mask')
plt.xlabel('frequency (secs)')
plt.ylabel('log power')
plt.savefig(os.path.join(qadir, 'meanpsd.png'), bbox_inches='tight')
plt.close()
plt.clf()
plt.imshow(AJKZ, vmin=0, vmax=AJKZ_thresh)
plt.xlabel('timepoints')
plt.ylabel('slices')
plt.title('Spike measure (absolute jackknife Z)')
plt.savefig(os.path.join(qadir, 'spike.png'), bbox_inches='tight')
plt.close()
if img.shape[0] < img.shape[1] and img.shape[0] < img.shape[2]:
orientation = 'saggital'
else:
orientation = 'axial'
mk_slice_mosaic(voxmean, os.path.join(qadir, 'voxmean.png'), 'Image mean (with mask)', contourdata=maskdata)
mk_slice_mosaic(voxcv, os.path.join(qadir, 'voxcv.png'), 'Image CV')
mk_slice_mosaic(voxsfnr, os.path.join(qadir, 'voxsfnr.png'), 'Image SFNR')
mk_report(infile, qadir, datavars)
# def save_vars(infile,qadir,datavars):
datafile = os.path.join(qadir, 'qadata.csv')
f = open(datafile, 'w')
f.write('SNR,%f\n' % N.mean(datavars['imgsnr']))
f.write('SFNR,%f\n' % datavars['meansfnr'])
# f.write('drift,%f\n'%datavars['trend'].params[1])
f.write('nspikes,%d\n' % len(datavars['spikes']))
f.write('nscrub,%d\n' % len(datavars['badvols']))
f.close()
if save_sfnr:
sfnrimg = nib.Nifti1Image(voxsfnr, img.get_affine())
sfnrimg.to_filename(os.path.join(qadir, 'voxsfnr.nii.gz'))
return qadir
plt.close()
# make report
plot=1
if plot:
plot_timeseries(fd,'Framewise displacement',os.path.join(qadir,'fd.png'),
ylabel='FD',xlabel='Gradient directions')
plot_timeseries(snr,'SNR',os.path.join(qadir,'snr.png'),
ylabel='SNR',xlabel='Gradient directions')
plot_timeseries(interleavecorr,'Interleave correlation',os.path.join(qadir,'interleavecorr.png'),
ylabel='Correlation between odd/even slices',xlabel='Gradient directions')
fa_img=nib.load(os.path.join(dtidir,fafile))
fa_data=fa_img.get_data()
mk_slice_mosaic(fa_data,os.path.join(qadir,'FA.png'),'FA (with mask)',contourdata=maskdata)
sse_img=nib.load(os.path.join(dtidir,fafile.replace('FA','sse')))
sse_data=sse_img.get_data()
mk_slice_mosaic(sse_data,os.path.join(qadir,'sse.png'),'SSE of dtifit')
mk_slice_mosaic(ecorr_data[:,:,:,worst_gradient],
os.path.join(qadir,'worst_gradient.png'),
'worst gradient (%d: intcorr=%0.2f)'%(worst_gradient,interleavecorr[worst_gradient]))
# spit out the info about the scan
#qafile=open(os.path.join(dtifile.replace('.nii.gz','.QAreport')),'w')
#qafile.write('DTI QA Report\n')
#qafile.write('Directory: %s\n'%dtidir)
#qafile.write('File: %s\n'%dtifile)