def trace_multib(self, sc=True, mask=True):
from PythonDiffusion import rescaleImage
from ImProcessing import T2regression_basic
if sc:
print "Applying slope correction..."
rescaleImage(self)
shape = list(self.shape)
shape[
-1] = self.ndirs + 3 #ndirs+3 volumes: A0, trace_total, trace_average, and ndirs*traces
trace = np.zeros(shape)
a0 = np.average(self.pdata[:, :, :, :self.nA0],
axis=-1) #take mean of all A0s
#fit all directions at once
self.adc, self.stdevmap, self.si0map, self.adcfig = T2regression_basic(
self.pdata, np.array(self.bvals), mask=mask)
trace[:, :, :, 1] = 1.0 / self.adc
#and separate dirs
for i in range(self.ndirs):
print "dir", i + 1
bv = list(self.bvals[:self.nA0]) + list(
self.bvals[self.nA0 + i * self.ndirs:self.nA0 +
(i + 1) * self.nbvals])
si = np.concatenate(
(self.pdata[:, :, :, :self.nA0],
self.pdata[:, :, :, self.nA0 + i * self.ndirs:self.nA0 +
(i + 1) * self.nbvals]),
axis=-1)
self.adcn, self.stdevmapn, self.si0mapn, self.adcfign = T2regression_basic(
si, np.array(bv), mask=mask)
trace[:, :, :, 3 + i] = 1.0 / self.adcn
trace[:, :, :, 2] = np.average(trace[:, :, :, 3:], axis=-1)
trace[:, :, :, 0] = a0
self.trace = trace
def __init__(self, name):
#metadata
self.name=name
self.filetype=checkFileType(self.name)
self.img=nib.load(self.name+self.filetype)
self.hdr=self.img.get_header()
self.protocol=read_line("VisuAcquisitionProtocol=",self.name)
self.date=read_line("Subject_date=",self.name)
try:
self.nrep=int(list_values(read_line("NRepetitions=",self.name))[0])
except IndexError:
self.nrep=1
#arrays
try:
self.data=self.img.get_data()
except MemoryError:
self.data=None
warnings.warn('MemoryError. Image array too large for available memory.')
self.affine=self.img.get_affine()
#geometric
self.resolution=getResolution(self.name)
self.dim=int(list_values(read_line("VisuCoreDim=",self.name))[0])
# if self.dim==2:
# self.data=scipy.swapaxes(self.data,2,3)
self.shape=self.data.shape
self.position=list_values(read_line("VisuCorePosition=",self.name))
self.zposition=self.position[2]
if self.dim==3:
self.zthickness=self.resolution[2]
elif self.dim==2:
self.zthickness=list_values(read_line("VisuCoreFrameThickness=",self.name))[0]
self.slopes=list_values(read_line("VisuCoreDataSlope=",self.name))
self.slicegap=list_values(read_line("SliceGap=",self.name))[0]
self.slicedistance=list_values(read_line("SliceDistance=",self.name))[0]
#scan parameters
self.te=[list_values(read_line("EffectiveEchoTime=",self.name))]
if len(self.te)==1:
self.te=self.te[0]
self.tr=list_values(read_line("RepetitionTime=",self.name))
if len(self.tr)==1:
self.tr=self.tr[0]
self.flipangle=list_values(read_line("FlipAngle=",self.name))
#processing output
self.pdata=np.array(self.data)
if self.nrep>1:
from PythonDiffusion import rescaleImageC:\Users\s0203524\Desktop\test
print "Multiple repetitions. Correcting slope first."
rescaleImage(self)
print "Averaging across %r repetitions..." %self.nrep
interv=self.pdata.shape[3]/self.nrep
nz=np.zeros([self.shape[0],self.shape[1],self.shape[2],interv])
for i in range(self.nrep):
nz+=self.pdata[:,:,:,i*interv:(i+1)*interv]
self.pdata=nz/self.nrep
self.shape=self.pdata.shape
def processDiffusion(self,ec=False, bv=None):
print "Processing diffusion, full pipeline."
from PythonDiffusion import rescaleImage, eddyCorrection
from datetime import datetime
if self.nrep==1:
print "Applying slope correction..."
rescaleImage(self)
if ec:
print "ec=True. Applying eddy current correction."
eddyCorrection(self,self.name+'_EC', protocol='eddy_v')
self.tensorFit(bv=bv)
def __init__(self, name, repetition_avg=True):
#metadata
Br2AInfo.__init__(self, name)
self.img = nib.load(self.name + self.filetype)
#arrays
try:
self.data = self.img.get_data()
except MemoryError:
self.data = None
warnings.warn(
'MemoryError. Image array too large for available memory.')
self.affine = self.img.get_affine()
#geometric
self.resolution = getResolution(self.name)
self.dim = int(list_values(read_line("VisuCoreDim=", self.name))[0])
# if self.dim==2:
# self.data=scipy.swapaxes(self.data,2,3)
self.shape = self.data.shape
self.position = list_values(read_line("VisuCorePosition=", self.name))
self.zposition = self.position[2]
if self.dim == 3:
self.zthickness = self.resolution[-1]
elif self.dim == 2:
self.zthickness = list_values(
read_line("VisuCoreFrameThickness=", self.name))[0]
self.slopes = list_values(read_line("VisuCoreDataSlope=", self.name))
self.slicegap = list_values(read_line("SliceGap=", self.name))[0]
self.slicedistance = list_values(read_line("SliceDistance=",
self.name))[0]
#processing output
self.pdata = np.array(self.data)
if repetition_avg:
if self.nrep > 1:
from PythonDiffusion import rescaleImage
print "Multiple repetitions. Correcting slope first."
rescaleImage(self)
print "Averaging across %r repetitions..." % self.nrep
interv = self.pdata.shape[3] / self.nrep
nz = np.zeros(
[self.shape[0], self.shape[1], self.shape[2], interv])
for i in range(self.nrep):
nz += self.pdata[:, :, :, i * interv:(i + 1) * interv]
self.pdata = nz / self.nrep
self.shape = self.pdata.shape
def trace_1b(self, sc=True):
'''Calculates diffusion trace. ln(S/S0)=exp(-b*ADC)'''
from PythonDiffusion import rescaleImage
if sc:
print "Applying slope correction..."
rescaleImage(self)
shape = list(self.shape)
shape[
-1] = self.ndirs + 2 #ndirs+2 volumes: A0, trace_average, and ndirs*traces
trace = np.zeros(shape)
a0 = np.average(self.pdata[:, :, :, :self.nA0],
axis=-1) #take mean of all A0s
for i in range(self.ndirs):
dwi = self.pdata[:, :, :, self.nA0 + i]
trace[:, :, :,
2 + i] = -np.log(dwi / a0) / self.bvals[self.nA0 + i]
trace[:, :, :, 0] = a0 #to draw rois
trace[:, :, :, 1] = np.average(trace[:, :, :, 2:], axis=-1)
self.trace = trace
def processDiffusion(self,
ec=False,
bv=None,
mode='dti',
removea0=0,
mask=True):
print "Processing diffusion, full pipeline."
from PythonDiffusion import rescaleImage, eddyCorrection
from datetime import datetime
if self.nrep == 1:
print "Applying slope correction..."
rescaleImage(self)
if ec:
print "ec=True. Applying eddy current correction."
eddyCorrection(self, self.name + '_EC', protocol='eddy_correct')
if mode == 'dti' or mode == 'RESTORE':
self.tensorFit(bv=bv, removea0=removea0, m=mode, mask=mask)
elif mode == 'dki':
self.processKurtosis()
if os.path.isfile(dtimg.name+'_EC.nii.gz') or os.path.isfile(dtimg.name+'_EC.nii'):
print "Found EC file. Not running new eddy current correction."
if os.path.isfile(dtimg.name+'_EC.nii.gz'):
dtimg.pdata=nib.load(dtimg.name+'_EC.nii.gz').get_data()
elif os.path.isfile(dtimg.name+'_EC.nii'):
dtimg.pdata=nib.load(dtimg.name+'_EC.nii').get_data()
try:
dtimg.tensorFitAllBvs()
except ValueError:
print "Matrices not aligned."
else:
try:
from PythonDiffusion import rescaleImage, eddyCorrection
if dtimg.nrep==1:
print "Applying slope correction..."
rescaleImage(dtimg)
if ec:
print "ec=True. Applying eddy current correction."
eddyCorrection(dtimg,dtimg.name+'_EC', protocol='eddy_correct')
dtimg.tensorFitAllBvs()
except ValueError:
print "Matrices not aligned."
pl.close()
else:
print "Can't process this image, sorry."
timecomp = datetime.now()-startTime
print "Process took %rs to complete." %timecomp.seconds
def main():
print "Enter the scan directory where the diffusion-weighted images are stored."
while True:
rootdir = tkFileDialog.askdirectory(initialdir="/",
title='Please select a directory')
if os.path.isdir(rootdir) is True: #Checks if entered dir exists
os.chdir(rootdir)
root.destroy()
break
else:
print "Pathname invalid. Try again."
continue
os.chdir(rootdir)
print "Found %r Analyze images in directory." % len(glob.glob('*.img'))
print "Scan types found: ", list_scans()
filelist = [x.replace('.img', '') for x in glob.glob('*.img')
] #cool list comprehension that gets all files
for ind, filen in enumerate(filelist):
startTime = datetime.now()
print "\nFile %r of %r. Filename: %s" % (ind + 1, len(filelist), filen)
try:
im = Br2AInfo(filen)
except AttributeError:
print "Error. No text file found."
continue
except IOError:
warnings.warn("Cannot find .hdr or .img file.")
continue
print "Scan type: ", im.protocol
if ("dti" in im.protocol.lower() or "dki" in im.protocol.lower()
or "diff" in im.protocol.lower()) and im.name[-1] == '1':
dtimg = DiffusionImg(filen)
if not (len(dtimg.bvals) >= 1 and len(dtimg.dwdir) > 1):
print "Diffusion image found, but insufficient diffusion dirs or b-vals."
continue
#eddy current correction step
if os.path.isfile(dtimg.name +
'_EC.nii.gz') or os.path.isfile(dtimg.name +
'_EC.nii'):
print "Found EC file. Not running new eddy current correction."
if os.path.isfile(dtimg.name + '_EC.nii.gz'):
dtimg.pdata = nib.load(dtimg.name +
'_EC.nii.gz').get_data()
elif os.path.isfile(dtimg.name + '_EC.nii'):
dtimg.pdata = nib.load(dtimg.name + '_EC.nii').get_data()
else:
try:
if dtimg.nrep == 1:
from PythonDiffusion import rescaleImage
print "Applying slope correction..."
rescaleImage(dtimg)
dtimg.eddyCorrection(FILENAME, REF, PROTOCOL)
except ValueError:
print "Matrices not aligned."
#saving output
save_dsi_src(dtimg, SRC_FILENAME, REMOVE_A0)
#performing command line stuff
if DSI_STUDIO_PROCESS:
command = ""
subprocess.call(command, shell=True)
del dtimg
else:
print "Can't process this image, sorry."
elif self.dim == 2:
rslopes = np.reshape(self.slopes, self.shape[:-3:-1])
rslopes = scipy.swapaxes(rslopes, 0, 1)
for j in range(self.shape[2]):
for i in range(self.shape[3]):
self.pdata[:, :, j, i] = self.pdata[:, :, j, i] * rslopes[j, i]
else:
print "No b-values found or cannot process slope formatting."
print "Enter the scan directory."
root = Tkinter.Tk()
while True:
rootdir = tkFileDialog.askdirectory(initialdir="/",
title='Please select a directory')
if os.path.isdir(rootdir) is True: #Checks if entered dir exists
os.chdir(rootdir)
root.destroy()
break
else:
print "Pathname invalid. Try again."
continue
os.chdir(rootdir)
filelist = glob.glob('*.hdr')
for f in filelist:
i = Bruker2AnalyzeImg(f[:-4])
rescaleImage(i)
im = nib.AnalyzeImage(i.pdata, i.affine)
nib.save(im, f[:-4] + '_RS.hdr')
