def obtain_image(img, Cnt=[], imtype='', verbose=False):
'''
Obtain the image (hardware or object mu-map) from file,
numpy array, dictionary or empty list (assuming blank then).
The image has to have the dimensions of the PET image used as in Cnt['SO_IM[X-Z]'].
'''
if Cnt: verbose = Cnt['VERBOSE']
# establishing what and if the image object has been provided
# all findings go to the output dictionary
output = {}
if isinstance(img, dict):
if Cnt and img['im'].shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'],
Cnt['SO_IMX']):
print 'e> provided ' + imtype + ' via the dictionary has inconsistent dimensions compared to Cnt.'
raise ValueError('Wrong dimensions of the mu-map')
else:
output['im'] = img['im']
output['exists'] = True
output['fim'] = img['fim']
if 'faff' in img: output['faff'] = img['faff']
if 'fmuref' in img: output['fmuref'] = img['fmuref']
if 'affine' in img: output['affine'] = img['affine']
if verbose: print 'i> using ' + imtype + ' from dictionary.'
elif isinstance(img, (np.ndarray, np.generic)):
if Cnt and img.shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']):
print 'e> provided ' + imtype + ' via the numpy array has inconsistent dimensions compared to Cnt.'
raise ValueError('Wrong dimensions of the mu-map')
else:
output['im'] = img
output['exists'] = True
output['fim'] = ''
if verbose: print 'i> using hardware mu-map from numpy array.'
elif isinstance(img, basestring):
if os.path.isfile(img):
imdct = nimpa.getnii(img, output='all')
output['im'] = imdct['im']
output['affine'] = imdct['affine']
if Cnt and output['im'].shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'],
Cnt['SO_IMX']):
print 'e> provided ' + imtype + ' via file has inconsistent dimensions compared to Cnt.'
raise ValueError('Wrong dimensions of the mu-map')
else:
output['exists'] = True
output['fim'] = img
if verbose: print 'i> using ' + imtype + ' from NIfTI file.'
else:
print 'e> provided ' + imtype + ' path is invalid.'
return None
elif isinstance(img, list):
output['im'] = np.zeros((Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']),
dtype=np.float32)
if verbose:
print 'w> ' + imtype + ' has not been provided -> using blank.'
output['fim'] = ''
output['exists'] = False
#------------------------------------------------------------------------
return output
def mmrchain(
datain, # all input data in a dictionary
scanner_params, # all scanner parameters in one dictionary
# containing constants, transaxial and axial
# LUTs.
outpath='', # output path for results
frames=['fluid', [0, 0]], # definition of time frames.
mu_h=[], # hardware mu-map.
mu_o=[], # object mu-map.
tAffine=[], # affine transformations for the mu-map for
# each time frame separately.
itr=4, # number of OSEM iterations
fwhm=0., # Gaussian Smoothing FWHM
recmod=-1, # reconstruction mode: -1: undefined, chosen
# automatically. 3: attenuation and scatter
# correction, 1: attenuation correction
# only, 0: no correction (randoms only).
histo=[], # input histogram (from list-mode data);
# if not given, it will be performed.
trim=False,
trim_scale=2,
trim_interp=1, # interpolation for upsampling used in PVC
trim_memlim=True, # reduced use of memory for machines
# with limited memory (slow though)
pvcroi=[], # ROI used for PVC. If undefined no PVC
# is performed.
pvcreg_tool='niftyreg', # the registration tool used in PVC
store_rois=False, # stores the image of PVC ROIs
# as defined in pvcroi.
psfkernel=[],
pvcitr=5,
fcomment='', # text comment used in the file name of
# generated image files
ret_sinos=False, # return prompt, scatter and randoms
# sinograms for each reconstruction
store_img=True,
store_img_intrmd=False,
store_itr=[], # store any reconstruction iteration in
# the list. ignored if the list is empty.
del_img_intrmd=False):
log = logging.getLogger(__name__)
# decompose all the scanner parameters and constants
Cnt = scanner_params['Cnt']
txLUT = scanner_params['txLUT']
axLUT = scanner_params['axLUT']
# -------------------------------------------------------------------------
# FRAMES
# check for the provided dynamic frames
if isinstance(frames, list):
# Can be given in three ways:
# * a 1D list (duration of each frame is listed)
# * a more concise 2D list--repetition and duration lists in
# each entry. Must start with the 'def' entry.
# * a 2D list with fluid timings: must start with the string
# 'fluid' or 'timings'. a 2D list with consecutive lists
# describing start and end of the time frame, [t0, t1];
# The number of time frames for this option is unlimited,
# provided the t0 and t1 are within the acquisition times.
# 2D starting with entry 'fluid' or 'timings'
if isinstance(frames[0], basestring) and (frames[0]=='fluid' or frames[0]=='timings') \
and all([isinstance(t,list) and len(t)==2 for t in frames[1:]]):
t_frms = frames[1:]
# if 2D definitions, starting with entry 'def':
elif isinstance(frames[0], basestring) and frames[0]=='def' \
and all([isinstance(t,list) and len(t)==2 for t in frames[1:]]):
# get total time and list of all time frames
dfrms = dynamic_timings(frames)
t_frms = dfrms[1:]
# if 1D:
elif all([isinstance(t, integers) for t in frames]):
# get total time and list of all time frames
dfrms = dynamic_timings(frames)
t_frms = dfrms[1:]
else:
log.error(
'osemdyn: frames definitions are not given in the correct list format: 1D [15,15,30,30,...] or 2D list [[2,15], [2,30], ...]'
)
else:
log.error(
'osemdyn: provided dynamic frames definitions are not in either Python list or nympy array.'
)
raise TypeError('Wrong data type for dynamic frames')
# number of dynamic time frames
nfrm = len(t_frms)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# create folders for results
if outpath == '':
petdir = os.path.join(datain['corepath'], 'reconstructed')
fmudir = os.path.join(datain['corepath'], 'mumap-obj')
pvcdir = os.path.join(datain['corepath'], 'PRCL')
else:
petdir = os.path.join(outpath, 'PET')
fmudir = os.path.join(outpath, 'mumap-obj')
pvcdir = os.path.join(outpath, 'PRCL')
# folder for co-registered mu-maps (for motion compensation)
fmureg = os.path.join(fmudir, 'registered')
# folder for affine transformation MR/CT->PET
petaff = os.path.join(petdir, 'faffine')
# folder for reconstructed images (dynamic or static depending on number of frames).
if nfrm > 1:
petimg = os.path.join(petdir, 'multiple-frames')
pvcdir = os.path.join(pvcdir, 'multiple-frames')
elif nfrm == 1:
petimg = os.path.join(petdir, 'single-frame')
pvcdir = os.path.join(pvcdir, 'single-frame')
else:
log.error('confused!')
raise TypeError('Unrecognised time frames!')
# create now the folder
nimpa.create_dir(petimg)
# create folder
nimpa.create_dir(petdir)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# MU-MAPS
# get the mu-maps, if given; otherwise will use blank mu-maps.
if tAffine:
muod = obtain_image(mu_o, imtype='object mu-map')
else:
muod = obtain_image(mu_o, Cnt=Cnt, imtype='object mu-map')
# hardware mu-map
muhd = obtain_image(mu_h, Cnt, imtype='hardware mu-map')
# choose the mode of reconstruction based on the provided (or not) mu-maps
if recmod == -1:
if muod['exists'] and muhd['exists']:
recmod = 3
elif muod['exists'] or muhd['exists']:
recmod = 1
log.warning('partial mu-map: scatter correction is switched off.')
else:
recmod = 0
log.warning(
'no mu-map provided: scatter and attenuation corrections are switched off.'
)
# -------------------------------------------------------------------------
#import pdb; pdb.set_trace()
# output dictionary
output = {}
output['recmod'] = recmod
output['frames'] = t_frms
output['#frames'] = nfrm
# if affine transformation is given the baseline mu-map in NIfTI file or dictionary has to be given
if not tAffine:
log.debug('using the provided mu-map the same way for all frames.')
else:
if len(tAffine) != nfrm:
log.error(
'the number of affine transformations in the list has to be the same as the number of dynamic frames!'
)
raise IndexError('Inconsistent number of frames.')
elif not isinstance(tAffine, list):
log.error(
'tAffine has to be a list of either 4x4 numpy arrays of affine transformations or a list of file path strings!'
)
raise IndexError('Expecting a list.')
elif not 'fim' in muod:
log.error(
'when tAffine is given, the object mu-map has to be provided either as a dictionary or NIfTI file!'
)
raise NameError('No path to object mu-map.')
# check if all are file path strings to the existing files
if all([isinstance(t, basestring) for t in tAffine]):
if all([os.path.isfile(t) for t in tAffine]):
# the internal list of affine transformations
faff_frms = tAffine
log.debug(
'using provided paths to affine transformations for each dynamic frame.'
)
else:
log.error('not all provided paths are valid!')
raise IOError('Wrong paths.')
# check if all are numpy arrays
elif all([isinstance(t, (np.ndarray, np.generic)) for t in tAffine]):
# create the folder for dynamic affine transformations
nimpa.create_dir(petaff)
faff_frms = []
for i in range(nfrm):
fout = os.path.join(petaff, 'affine_frame(' + str(i) + ').txt')
np.savetxt(fout, tAffine[i], fmt='%3.9f')
faff_frms.append(fout)
log.debug(
'using provided numpy arrays affine transformations for each dynamic frame.'
)
else:
raise StandardError(
'Affine transformations for each dynamic frame could not be established.'
)
# -------------------------------------------------------------------------------------
# get ref image for mu-map resampling
# -------------------------------------------------------------------------------------
if 'fmuref' in muod:
fmuref = muod['fmuref']
log.debug(
'reusing the reference mu-map from the object mu-map dictionary.'
)
else:
# create folder if doesn't exists
nimpa.create_dir(fmudir)
# ref file name
fmuref = os.path.join(fmudir, 'muref.nii.gz')
# ref affine
B = image_affine(datain, Cnt, gantry_offset=False)
# ref image (blank)
im = np.zeros((Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']),
dtype=np.float32)
# store ref image
nimpa.array2nii(im, B, fmuref)
log.debug('generated a reference mu-map in' + fmuref)
# -------------------------------------------------------------------------------------
output['fmuref'] = fmuref
output['faffine'] = faff_frms
# output list of intermidiate file names for mu-maps and PET images (useful for dynamic imaging)
if tAffine: output['fmureg'] = []
if store_img_intrmd: output['fpeti'] = []
# dynamic images in one numpy array
dynim = np.zeros((nfrm, Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMY']),
dtype=np.float32)
#if asked, output only scatter+randoms sinogram for each frame
if ret_sinos and itr > 1 and recmod > 2:
dynmsk = np.zeros((nfrm, Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']),
dtype=np.float32)
dynrsn = np.zeros((nfrm, Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']),
dtype=np.float32)
dynssn = np.zeros((nfrm, Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']),
dtype=np.float32)
dynpsn = np.zeros((nfrm, Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']),
dtype=np.float32)
# import pdb; pdb.set_trace()
# starting frame index with reasonable prompt data
ifrmP = 0
# iterate over frame index
for ifrm in range(nfrm):
# start time of a current (ifrm-th) dynamic frame
t0 = int(t_frms[ifrm][0])
# end time of a current (ifrm-th) dynamic frame
t1 = int(t_frms[ifrm][1])
# --------------
# check if there is enough prompt data to do a reconstruction
# --------------
log.info('dynamic frame times t0, t1:%r, %r' % (t0, t1))
if not histo:
hst = mmrhist(datain, scanner_params, t0=t0, t1=t1)
else:
hst = histo
log.info('using provided histogram')
if np.sum(hst['dhc']) > 0.99 * np.sum(hst['phc']):
log.warning(
'the amount of random events is the greatest part of prompt events => omitting reconstruction'
)
ifrmP = ifrm + 1
continue
# --------------------
# transform the mu-map if given the affine transformation for each frame
if tAffine:
# create the folder for aligned (registered for motion compensation) mu-maps
nimpa.create_dir(fmureg)
# the converted nii image resample to the reference size
fmu = os.path.join(
fmureg, 'mumap_dyn_frm' + str(ifrm) + fcomment + '.nii.gz')
# command for resampling
if os.path.isfile(Cnt['RESPATH']):
cmd = [
Cnt['RESPATH'], '-ref', fmuref, '-flo', muod['fim'],
'-trans', faff_frms[ifrm], '-res', fmu, '-pad', '0'
]
if log.getEffectiveLevel() > log.DEBUG:
cmd.append('-voff')
call(cmd)
else:
log.error(
'path to the executable for resampling is incorrect!')
raise IOError('Incorrect NiftyReg (resampling) executable.')
# get the new mu-map from the just resampled file
muodct = nimpa.getnii(fmu, output='all')
muo = muodct['im']
A = muodct['affine']
muo[muo < 0] = 0
output['fmureg'].append(fmu)
else:
muo = muod['im']
#---------------------
# output image file name
if nfrm > 1:
frmno = '_frm' + str(ifrm)
else:
frmno = ''
# run OSEM reconstruction of a single time frame
recimg = mmrrec.osemone(datain, [muhd['im'], muo],
hst,
scanner_params,
recmod=recmod,
itr=itr,
fwhm=fwhm,
outpath=petimg,
frmno=frmno,
fcomment=fcomment + '_i',
store_img=store_img_intrmd,
store_itr=store_itr,
ret_sinos=ret_sinos)
# form dynamic numpy array
dynim[ifrm, :, :, :] = recimg.im
if ret_sinos and itr > 1 and recmod > 2:
dynpsn[ifrm, :, :, :] = hst['psino']
dynssn[ifrm, :, :, :] = recimg.ssn
dynrsn[ifrm, :, :, :] = recimg.rsn
dynmsk[ifrm, :, :, :] = recimg.amsk
if store_img_intrmd: output['fpeti'].append(recimg.fpet)
if nfrm == 1: output['tuple'] = recimg
output['im'] = np.squeeze(dynim)
if ret_sinos and itr > 1 and recmod > 2:
output['sinos'] = {
'psino': dynpsn,
'ssino': dynssn,
'rsino': dynrsn,
'amask': dynmsk
}
# ----------------------------------------------------------------------
# trim the PET image
# images have to be stored for PVC
if pvcroi: store_img_intrmd = True
if trim:
# create file name
if 'lm_dcm' in datain:
fnm = os.path.basename(datain['lm_dcm'])[:20]
elif 'lm_ima' in datain:
fnm = os.path.basename(datain['lm_ima'])[:20]
# trim PET and upsample
petu = nimpa.trimim(dynim,
affine=image_affine(datain, Cnt),
scale=trim_scale,
int_order=trim_interp,
outpath=petimg,
fname=fnm,
fcomment=fcomment,
store_img_intrmd=store_img_intrmd,
memlim=trim_memlim,
verbose=log.getEffectiveLevel() < logging.INFO)
output.update({
'trimmed': {
'im': petu['im'],
'fpet': petu['fimi'],
'affine': petu['affine']
}
})
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
#run PVC if requested and required input given
if pvcroi:
if not os.path.isfile(datain['T1lbl']):
log.error(
'no label image from T1 parcellations and/or ROI definitions!')
raise StandardError('No ROIs')
else:
# get the PSF kernel for PVC
if not psfkernel:
psfkernel = nimpa.psf_measured(scanner='mmr', scale=trim_scale)
else:
if isinstance(
psfkernel,
(np.ndarray, np.generic)) and psfkernel.shape != (3, 17):
log.error(
'the PSF kernel has to be an numpy array with the shape of (3, 17)!'
)
raise IndexError('PSF: wrong shape or not a matrix')
#> file names for NIfTI images of PVC ROIs and PVC corrected PET
froi = []
fpvc = []
#> perform PVC for each time frame
dynpvc = np.zeros(petu['im'].shape, dtype=np.float32)
for i in range(ifrmP, nfrm):
# transform the parcellations (ROIs) if given the affine transformation for each frame
if not tAffine:
log.warning(
'affine transformation are not provided: will generate for the time frame.'
)
faffpvc = ''
#raise StandardError('No affine transformation')
else:
faffpvc = faff_frms[i]
# chose file name of individual PVC images
if nfrm > 1:
fcomment_pvc = '_frm' + str(i) + fcomment
else:
fcomment_pvc = fcomment
#============================
# perform PVC
petpvc_dic = nimpa.pvc_iyang(petu['fimi'][i],
datain,
Cnt,
pvcroi,
psfkernel,
tool=pvcreg_tool,
itr=pvcitr,
faff=faffpvc,
fcomment=fcomment_pvc,
outpath=pvcdir,
store_rois=store_rois,
store_img=store_img_intrmd)
#============================
if nfrm > 1:
dynpvc[i, :, :, :] = petpvc_dic['im']
else:
dynpvc = petpvc_dic['im']
fpvc.append(petpvc_dic['fpet'])
if store_rois: froi.append(petpvc_dic['froi'])
#> update output dictionary
output.update({'impvc': dynpvc})
if store_img_intrmd: output.update({'fpvc': fpvc})
if store_rois: output.update({'froi': froi})
# ----------------------------------------------------------------------
if store_img:
# description for saving NIFTI image
# attenuation number: if only bed present then it is 0.5
attnum = (1 * muhd['exists'] + 1 * muod['exists']) / 2.
descrip = 'alg=osem' \
+';att='+str(attnum*(recmod>0)) \
+';sct='+str(1*(recmod>1)) \
+';spn='+str(Cnt['SPN']) \
+';sub=14' \
+';itr='+str(itr) \
+';fwhm='+str(fwhm) \
+';nfrm='+str(nfrm)
# squeeze the not needed dimensions
dynim = np.squeeze(dynim)
# NIfTI file name for the full PET image (single or multiple frame)
# save the image to NIfTI file
if nfrm == 1:
t0 = hst['t0']
t1 = hst['t1']
if t1 == t0:
t0 = 0
t1 = hst['dur']
fpet = os.path.join(
petimg,
os.path.basename(recimg.fpet)[:8] \
+'_t-'+str(t0)+'-'+str(t1)+'sec' \
+'_itr-'+str(itr) )
fpeto = fpet + fcomment + '.nii.gz'
nimpa.prc.array2nii(dynim[::-1, ::-1, :],
recimg.affine,
fpeto,
descrip=descrip)
else:
fpet = os.path.join(
petimg,
os.path.basename(recimg.fpet)[:8]\
+'_nfrm-'+str(nfrm)+'_itr-'+str(itr)
)
fpeto = fpet + fcomment + '.nii.gz'
nimpa.prc.array2nii(dynim[:, ::-1, ::-1, :],
recimg.affine,
fpeto,
descrip=descrip)
# get output file names for trimmed/PVC images
if trim:
# folder for trimmed and dynamic
pettrim = os.path.join(petimg, 'trimmed')
# make folder
nimpa.create_dir(pettrim)
# trimming scale added to NIfTI descritoption
descrip_trim = descrip + ';trim_scale=' + str(trim_scale)
# file name for saving the trimmed image
fpetu = os.path.join(
pettrim,
os.path.basename(fpet) + '_trimmed-upsampled-scale-' +
str(trim_scale))
# in case of PVC
if pvcroi:
# itertive Yang (iY) added to NIfTI descritoption
descrip_pvc = descrip_trim + ';pvc=iY'
# file name for saving the PVC NIfTI image
fpvc = fpetu + '_PVC' + fcomment + '.nii.gz'
output['trimmed']['fpvc'] = fpvc
# update the trimmed image file name
fpetu += fcomment + '.nii.gz'
# store the file name in the output dictionary
output['trimmed']['fpet'] = fpetu
output['fpet'] = fpeto
# save images
if nfrm == 1:
if trim:
nimpa.prc.array2nii(petu['im'][::-1, ::-1, :],
petu['affine'],
fpetu,
descrip=descrip_trim)
if pvcroi:
nimpa.prc.array2nii(dynpvc[::-1, ::-1, :],
petu['affine'],
fpvc,
descrip=descrip_pvc)
elif nfrm > 1:
if trim:
nimpa.prc.array2nii(petu['im'][:, ::-1, ::-1, :],
petu['affine'],
fpetu,
descrip=descrip_trim)
if pvcroi:
nimpa.prc.array2nii(dynpvc[:, ::-1, ::-1, :],
petu['affine'],
fpvc,
descrip=descrip_pvc)
if del_img_intrmd:
if pvcroi:
for fi in fpvc:
os.remove(fi)
if trim:
for fi in petu['fimi']:
os.remove(fi)
return output
def mmrchain(
datain, # all input data in a dictionary
scanner_params, # all scanner parameters in one dictionary
# containing constants, transaxial and axial
# LUTs.
outpath=None, # output path for results
fout=None, # full file name (any folders and extensions are disregarded)
frames=None, # definition of time frames, default: ['fluid', [0, 0]]
mu_h=None, # hardware mu-map.
mu_o=None, # object mu-map.
tAffine=None, # affine transformations for the mu-map for
# each time frame separately.
itr=4, # number of OSEM iterations
fwhm=0., # Gaussian Post-Smoothing FWHM
psf=None, # Resolution Modelling
recmod=-1, # reconstruction mode: -1: undefined, chosen
# automatically. 3: attenuation and scatter
# correction, 1: attenuation correction
# only, 0: no correction (randoms only).
histo=None, # input histogram (from list-mode data);
# if not given, it will be performed.
decay_ref_time=None, # decay corrects relative to the reference
# time provided; otherwise corrects to the scan
# start time.
trim=False,
trim_scale=2,
trim_interp=0, # interpolation for upsampling used in PVC
trim_memlim=True, # reduced use of memory for machines
# with limited memory (slow though)
pvcroi=None, # ROI used for PVC. If undefined no PVC
# is performed.
pvcreg_tool='niftyreg', # the registration tool used in PVC
store_rois=False, # stores the image of PVC ROIs
# as defined in pvcroi.
pvcpsf=None,
pvcitr=5,
fcomment='', # text comment used in the file name of
# generated image files
ret_sinos=False, # return prompt, scatter and randoms
# sinograms for each reconstruction
ret_histo=False, # return histogram (LM processing output) for
# each image frame
store_img=True,
store_img_intrmd=False,
store_itr=None, # store any reconstruction iteration in
# the list. ignored if the list is empty.
del_img_intrmd=False,
):
if frames is None:
frames = ['fluid', [0, 0]]
if mu_h is None:
mu_h = []
if mu_o is None:
mu_o = []
if pvcroi is None:
pvcroi = []
if pvcpsf is None:
pvcpsf = []
if store_itr is None:
store_itr = []
# decompose all the scanner parameters and constants
Cnt = scanner_params['Cnt']
# -------------------------------------------------------------------------
# HISOTGRAM PRECEEDS FRAMES
if histo is not None and 'psino' in histo:
frames = ['fluid', [histo['t0'], histo['t1']]]
else:
histo = None
log.warning(
'the given histogram does not contain a prompt sinogram--will generate a histogram.')
# FRAMES
# check for the provided dynamic frames
if isinstance(frames, list):
# Can be given in three ways:
# * a 1D list (duration of each frame is listed)
# * a more concise 2D list--repetition and duration lists in
# each entry. Must start with the 'def' entry.
# * a 2D list with fluid timings: must start with the string
# 'fluid' or 'timings'. a 2D list with consecutive lists
# describing start and end of the time frame, [t0, t1];
# The number of time frames for this option is unlimited,
# provided the t0 and t1 are within the acquisition times.
# 2D starting with entry 'fluid' or 'timings'
if (isinstance(frames[0], str) and frames[0] in ('fluid', 'timings')
and all(isinstance(t, list) and len(t) == 2 for t in frames[1:])):
t_frms = frames[1:]
# if 2D definitions, starting with entry 'def':
elif (isinstance(frames[0], str) and frames[0] == 'def'
and all(isinstance(t, list) and len(t) == 2 for t in frames[1:])):
# get total time and list of all time frames
dfrms = dynamic_timings(frames)
t_frms = dfrms[1:]
# if 1D:
elif all(isinstance(t, Integral) for t in frames):
# get total time and list of all time frames
dfrms = dynamic_timings(frames)
t_frms = dfrms[1:]
else:
log.error('osemdyn: frames definitions are not given\
in the correct list format: 1D [15,15,30,30,...]\
or 2D list [[2,15], [2,30], ...]')
else:
log.error(
'provided dynamic frames definitions are incorrect (should be a list of definitions).')
raise TypeError('Wrong data type for dynamic frames')
# number of dynamic time frames
nfrm = len(t_frms)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# create folders for results
if outpath is None:
petdir = os.path.join(datain['corepath'], 'reconstructed')
fmudir = os.path.join(datain['corepath'], 'mumap-obj')
pvcdir = os.path.join(datain['corepath'], 'PRCL')
else:
petdir = os.path.join(outpath, 'PET')
fmudir = os.path.join(outpath, 'mumap-obj')
pvcdir = os.path.join(outpath, 'PRCL')
if fout is not None:
# > get rid of folders
fout = os.path.basename(fout)
# > get rid of extension
fout = fout.split('.')[0]
# folder for co-registered mu-maps (for motion compensation)
fmureg = os.path.join(fmudir, 'registered')
# folder for affine transformation MR/CT->PET
petaff = os.path.join(petdir, 'faffine')
# folder for reconstructed images (dynamic or static depending on number of frames).
if nfrm > 1:
petimg = os.path.join(petdir, 'multiple-frames')
pvcdir = os.path.join(pvcdir, 'multiple-frames')
elif nfrm == 1:
petimg = os.path.join(petdir, 'single-frame')
pvcdir = os.path.join(pvcdir, 'single-frame')
else:
raise TypeError('Unrecognised/confusing time frames!')
# create now the folder
nimpa.create_dir(petimg)
# create folder
nimpa.create_dir(petdir)
# -------------------------------------------------------------------------
# -------------------------------------------------------------------------
# MU-MAPS
# get the mu-maps, if given; otherwise will use blank mu-maps.
if tAffine is not None:
muod = obtain_image(mu_o, imtype='object mu-map')
else:
muod = obtain_image(mu_o, Cnt=Cnt, imtype='object mu-map')
# hardware mu-map
muhd = obtain_image(mu_h, Cnt, imtype='hardware mu-map')
# choose the mode of reconstruction based on the provided (or not) mu-maps
if muod['exists'] and muhd['exists'] and recmod == -1:
recmod = 3
elif (muod['exists'] or muhd['exists']) and recmod == -1:
recmod = 1
log.warning('partial mu-map: scatter correction is switched off.')
else:
if recmod == -1:
recmod = 0
log.warning(
'no mu-map provided: scatter and attenuation corrections are switched off.')
# -------------------------------------------------------------------------
# import pdb; pdb.set_trace()
# output dictionary
output = {}
output['recmod'] = recmod
output['frames'] = t_frms
output['#frames'] = nfrm
# if affine transformation is given
# the baseline mu-map in NIfTI file or dictionary has to be given
if tAffine is None:
log.info('using the provided mu-map the same way for all frames.')
else:
if len(tAffine) != nfrm:
raise ValueError("the number of affine transformations in the list"
" has to be the same as the number of dynamic frames")
elif not isinstance(tAffine, list):
raise ValueError("tAffine has to be a list of either 4x4 numpy arrays"
" of affine transformations or a list of file path strings")
elif 'fim' not in muod:
raise NameError("when tAffine is given, the object mu-map has to be"
" provided either as a dictionary or NIfTI file")
# check if all are file path strings to the existing files
if all(isinstance(t, str) for t in tAffine):
if all(os.path.isfile(t) for t in tAffine):
# the internal list of affine transformations
faff_frms = tAffine
log.info('using provided paths to affine transformations for each dynamic frame.')
else:
raise IOError('not all provided paths are valid!')
# check if all are numpy arrays
elif all(isinstance(t, (np.ndarray, np.generic)) for t in tAffine):
# create the folder for dynamic affine transformations
nimpa.create_dir(petaff)
faff_frms = []
for i in range(nfrm):
fout_ = os.path.join(petaff, 'affine_frame(' + str(i) + ').txt')
np.savetxt(fout_, tAffine[i], fmt='%3.9f')
faff_frms.append(fout_)
log.info('using provided numpy arrays affine transformations for each dynamic frame.')
else:
raise ValueError(
'Affine transformations for each dynamic frame could not be established.')
# -------------------------------------------------------------------------------------
# get ref image for mu-map resampling
# -------------------------------------------------------------------------------------
if 'fmuref' in muod:
fmuref = muod['fmuref']
log.info('reusing the reference mu-map from the object mu-map dictionary.')
else:
# create folder if doesn't exists
nimpa.create_dir(fmudir)
# ref file name
fmuref = os.path.join(fmudir, 'muref.nii.gz')
# ref affine
B = image_affine(datain, Cnt, gantry_offset=False)
# ref image (blank)
im = np.zeros((Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']), dtype=np.float32)
# store ref image
nimpa.array2nii(im, B, fmuref)
log.info('generated a reference mu-map in:\n{}'.format(fmuref))
# -------------------------------------------------------------------------------------
output['fmuref'] = fmuref
output['faffine'] = faff_frms
# output list of intermediate file names for mu-maps and PET images
# (useful for dynamic imaging)
if tAffine is not None: output['fmureg'] = []
if store_img_intrmd:
output['fpeti'] = []
if fwhm > 0:
output['fsmoi'] = []
# > number of3D sinograms
if Cnt['SPN'] == 1:
snno = Cnt['NSN1']
elif Cnt['SPN'] == 11:
snno = Cnt['NSN11']
else:
raise ValueError('unrecognised span: {}'.format(Cnt['SPN']))
# dynamic images in one numpy array
dynim = np.zeros((nfrm, Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMY']), dtype=np.float32)
# if asked, output only scatter+randoms sinogram for each frame
if ret_sinos and itr > 1 and recmod > 2:
dynmsk = np.zeros((nfrm, Cnt['NSEG0'], Cnt['NSANGLES'], Cnt['NSBINS']), dtype=np.float32)
dynrsn = np.zeros((nfrm, snno, Cnt['NSANGLES'], Cnt['NSBINS']), dtype=np.float32)
dynssn = np.zeros((nfrm, snno, Cnt['NSANGLES'], Cnt['NSBINS']), dtype=np.float32)
dynpsn = np.zeros((nfrm, snno, Cnt['NSANGLES'], Cnt['NSBINS']), dtype=np.float32)
# > returning dictionary of histograms if requested
if ret_histo:
hsts = {}
# import pdb; pdb.set_trace()
# starting frame index with reasonable prompt data
ifrmP = 0
# iterate over frame index
for ifrm in range(nfrm):
# start time of a current (ifrm-th) dynamic frame
t0 = int(t_frms[ifrm][0])
# end time of a current (ifrm-th) dynamic frame
t1 = int(t_frms[ifrm][1])
# --------------
# check if there is enough prompt data to do a reconstruction
# --------------
log.info('dynamic frame times t0={}, t1={}:'.format(t0, t1))
if histo is None:
hst = mmrhist(datain, scanner_params, t0=t0, t1=t1)
else:
hst = histo
log.info(
dedent('''\
------------------------------------------------------
using provided histogram
------------------------------------------------------'''))
if ret_histo:
hsts[str(t0) + '-' + str(t1)] = hst
if np.sum(hst['dhc']) > 0.99 * np.sum(hst['phc']):
log.warning(
dedent('''\
===========================================================================
amount of randoms is the greater part of prompts => omitting reconstruction
==========================================================================='''))
ifrmP = ifrm + 1
continue
# --------------------
# transform the mu-map if given the affine transformation for each frame
if tAffine is not None:
# create the folder for aligned (registered for motion compensation) mu-maps
nimpa.create_dir(fmureg)
# the converted nii image resample to the reference size
fmu = os.path.join(fmureg, 'mumap_dyn_frm' + str(ifrm) + fcomment + '.nii.gz')
# command for resampling
if os.path.isfile(Cnt['RESPATH']):
cmd = [
Cnt['RESPATH'], '-ref', fmuref, '-flo', muod['fim'], '-trans', faff_frms[ifrm],
'-res', fmu, '-pad', '0']
if log.getEffectiveLevel() > log.INFO:
cmd.append('-voff')
call(cmd)
else:
raise IOError('Incorrect path to NiftyReg (resampling) executable.')
# get the new mu-map from the just resampled file
muodct = nimpa.getnii(fmu, output='all')
muo = muodct['im']
muo[muo < 0] = 0
output['fmureg'].append(fmu)
else:
muo = muod['im']
# ---------------------
# output image file name
if nfrm > 1:
frmno = '_frm' + str(ifrm)
else:
frmno = ''
# run OSEM reconstruction of a single time frame
recimg = mmrrec.osemone(datain, [muhd['im'], muo], hst, scanner_params,
decay_ref_time=decay_ref_time, recmod=recmod, itr=itr, fwhm=fwhm,
psf=psf, outpath=petimg, frmno=frmno, fcomment=fcomment + '_i',
store_img=store_img_intrmd, store_itr=store_itr, fout=fout,
ret_sinos=ret_sinos)
# form dynamic Numpy array
if fwhm > 0:
dynim[ifrm, :, :, :] = recimg.imsmo
else:
dynim[ifrm, :, :, :] = recimg.im
if ret_sinos and itr > 1 and recmod > 2:
dynpsn[ifrm, :, :, :] = np.squeeze(hst['psino'])
dynssn[ifrm, :, :, :] = np.squeeze(recimg.ssn)
dynrsn[ifrm, :, :, :] = np.squeeze(recimg.rsn)
dynmsk[ifrm, :, :, :] = np.squeeze(recimg.amsk)
if store_img_intrmd:
output['fpeti'].append(recimg.fpet)
if fwhm > 0:
output['fsmoi'].append(recimg.fsmo)
if nfrm == 1: output['tuple'] = recimg
output['im'] = np.squeeze(dynim)
if ret_sinos and itr > 1 and recmod > 2:
output['sinos'] = {
'psino': np.squeeze(dynpsn), 'ssino': np.squeeze(dynssn), 'rsino': np.squeeze(dynrsn),
'amask': np.squeeze(dynmsk)}
if ret_histo:
output['hst'] = hsts
# ----------------------------------------------------------------------
# trim the PET image
# images have to be stored for PVC
if pvcroi: store_img_intrmd = True
if trim:
# create file name
if 'lm_dcm' in datain:
fnm = os.path.basename(datain['lm_dcm'])[:20]
elif 'lm_ima' in datain:
fnm = os.path.basename(datain['lm_ima'])[:20]
# trim PET and upsample
petu = nimpa.imtrimup(dynim, affine=image_affine(datain, Cnt), scale=trim_scale,
int_order=trim_interp, outpath=petimg, fname=fnm, fcomment=fcomment,
store_img_intrmd=store_img_intrmd, memlim=trim_memlim,
verbose=log.getEffectiveLevel())
output.update({
'trimmed': {'im': petu['im'], 'fpet': petu['fimi'], 'affine': petu['affine']}})
# ----------------------------------------------------------------------
# ----------------------------------------------------------------------
# run PVC if requested and required input given
if pvcroi:
if not os.path.isfile(datain['T1lbl']):
raise Exception('No labels and/or ROIs image definitions found!')
else:
# get the PSF kernel for PVC
if not pvcpsf:
pvcpsf = nimpa.psf_measured(scanner='mmr', scale=trim_scale)
else:
if (
isinstance(pvcpsf, (np.ndarray, np.generic)) and
pvcpsf.shape != (3, 2 * Cnt['RSZ_PSF_KRNL'] + 1)
): # yapf: disable
raise ValueError(
'the PSF kernel has to be an numpy array with the shape of ({},{})'.format(
3, 2 * Cnt['RSZ_PSF_KRNL'] + 1))
# > file names for NIfTI images of PVC ROIs and PVC corrected PET
froi = []
fpvc = []
# > perform PVC for each time frame
dynpvc = np.zeros(petu['im'].shape, dtype=np.float32)
for i in range(ifrmP, nfrm):
# transform the parcellations (ROIs) if given the affine transformation for each frame
if tAffine is None:
log.warning(
'affine transformation are not provided: will generate for the time frame.')
faffpvc = None
# raise StandardError('No affine transformation')
else:
faffpvc = faff_frms[i]
# chose file name of individual PVC images
if nfrm > 1:
fcomment_pvc = '_frm' + str(i) + fcomment
else:
fcomment_pvc = fcomment
# ===========================
# perform PVC
petpvc_dic = nimpa.pvc_iyang(petu['fimi'][i], datain, Cnt, pvcroi, pvcpsf,
tool=pvcreg_tool, itr=pvcitr, faff=faffpvc,
fcomment=fcomment_pvc, outpath=pvcdir,
store_rois=store_rois, store_img=store_img_intrmd)
# ===========================
if nfrm > 1:
dynpvc[i, :, :, :] = petpvc_dic['im']
else:
dynpvc = petpvc_dic['im']
fpvc.append(petpvc_dic['fpet'])
if store_rois: froi.append(petpvc_dic['froi'])
# > update output dictionary
output.update({'impvc': dynpvc})
output['fprc'] = petpvc_dic['fprc']
output['imprc'] = petpvc_dic['imprc']
if store_img_intrmd: output.update({'fpvc': fpvc})
if store_rois: output.update({'froi': froi})
# ----------------------------------------------------------------------
if store_img:
# description for saving NIFTI image
# attenuation number: if only bed present then it is 0.5
attnum = (1 * muhd['exists'] + 1 * muod['exists']) / 2.
descrip = (f"alg=osem"
f";att={attnum*(recmod>0)}"
f";sct={1*(recmod>1)}"
f";spn={Cnt['SPN']}"
f";sub=14"
f";itr={itr}"
f";fwhm={fwhm}"
f";psf={psf}"
f";nfrm={nfrm}")
# squeeze the not needed dimensions
dynim = np.squeeze(dynim)
# NIfTI file name for the full PET image (single or multiple frame)
# save the image to NIfTI file
if nfrm == 1:
t0 = hst['t0']
t1 = hst['t1']
if t1 == t0:
t0 = 0
t1 = hst['dur']
# > --- file naming and saving ---
if fout is None:
fpet = os.path.join(
petimg,
os.path.basename(recimg.fpet)[:8] + f'_t-{t0}-{t1}sec_itr-{itr}')
fpeto = f"{fpet}{fcomment}.nii.gz"
else:
fpeto = os.path.join(petimg, os.path.basename(fout) + '.nii.gz')
nimpa.prc.array2nii(dynim[::-1, ::-1, :], recimg.affine, fpeto, descrip=descrip)
# > --- ---
else:
if fout is None:
fpet = os.path.join(petimg,
os.path.basename(recimg.fpet)[:8] + f'_nfrm-{nfrm}_itr-{itr}')
fpeto = f"{fpet}{fcomment}.nii.gz"
else:
fpeto = os.path.join(petimg, os.path.basename(fout) + f'_nfrm-{nfrm}.nii.gz')
nimpa.prc.array2nii(dynim[:, ::-1, ::-1, :], recimg.affine, fpeto, descrip=descrip)
output['fpet'] = fpeto
# get output file names for trimmed/PVC images
if trim:
# folder for trimmed and dynamic
pettrim = os.path.join(petimg, 'trimmed')
# make folder
nimpa.create_dir(pettrim)
# trimming scale added to NIfTI descritoption
descrip_trim = f'{descrip};trim_scale={trim_scale}'
# file name for saving the trimmed image
if fout is None:
fpetu = os.path.join(
pettrim,
os.path.basename(fpet) + f'_trimmed-upsampled-scale-{trim_scale}')
else:
fpetu = os.path.join(
pettrim,
os.path.basename(fout) + f'_trimmed-upsampled-scale-{trim_scale}')
# in case of PVC
if pvcroi:
# itertive Yang (iY) added to NIfTI descritoption
descrip_pvc = f'{descrip_trim};pvc=iY'
# file name for saving the PVC NIfTI image
fpvc = f"{fpetu}_PVC{fcomment}.nii.gz"
output['trimmed']['fpvc'] = fpvc
# update the trimmed image file name
fpetu += f'{fcomment}.nii.gz'
# store the file name in the output dictionary
output['trimmed']['fpet'] = fpetu
# save images
if nfrm == 1:
if trim:
nimpa.prc.array2nii(petu['im'][::-1, ::-1, :], petu['affine'], fpetu,
descrip=descrip_trim)
if pvcroi:
nimpa.prc.array2nii(dynpvc[::-1, ::-1, :], petu['affine'], fpvc,
descrip=descrip_pvc)
elif nfrm > 1:
if trim:
nimpa.prc.array2nii(petu['im'][:, ::-1, ::-1, :], petu['affine'], fpetu,
descrip=descrip_trim)
if pvcroi:
nimpa.prc.array2nii(dynpvc[:, ::-1, ::-1, :], petu['affine'], fpvc,
descrip=descrip_pvc)
if del_img_intrmd:
if pvcroi:
for fi in fpvc:
os.remove(fi)
if trim:
for fi in petu['fimi']:
os.remove(fi)
return output
def osemone(datain,
mumaps,
hst,
scanner_params,
recmod=3,
itr=4,
fwhm=0.,
psf=None,
mask_radius=29.,
decay_ref_time=None,
attnsino=None,
sctsino=None,
randsino=None,
normcomp=None,
emmskS=False,
frmno='',
fcomment='',
outpath=None,
fout=None,
store_img=False,
store_itr=None,
ret_sinos=False):
'''
OSEM image reconstruction with several modes
(with/without scatter and/or attenuation correction)
Args:
psf: Reconstruction with PSF, passed to `psf_config`
'''
# > Get particular scanner parameters: Constants, transaxial and axial LUTs
Cnt = scanner_params['Cnt']
txLUT = scanner_params['txLUT']
axLUT = scanner_params['axLUT']
# ---------- sort out OUTPUT ------------
# -output file name for the reconstructed image
if outpath is None:
opth = os.path.join(datain['corepath'], 'reconstructed')
else:
opth = outpath
# > file output name (the path is ignored if given)
if fout is not None:
# > get rid of folders
fout = os.path.basename(fout)
# > get rid of extension
fout = fout.split('.')[0]
if store_img is True or store_itr is not None:
mmraux.create_dir(opth)
return_ssrb, return_mask = ret_sinos, ret_sinos
# ----------
log.info('reconstruction in mode: %d', recmod)
# get object and hardware mu-maps
muh, muo = mumaps
# get the GPU version of the image dims
mus = mmrimg.convert2dev(muo + muh, Cnt)
# remove gaps from the prompt sino
psng = mmraux.remgaps(hst['psino'], txLUT, Cnt)
# ========================================================================
# GET NORM
# -------------------------------------------------------------------------
if normcomp is None:
ncmp, _ = mmrnorm.get_components(datain, Cnt)
else:
ncmp = normcomp
log.warning('using user-defined normalisation components')
nsng = mmrnorm.get_norm_sino(datain,
scanner_params,
hst,
normcomp=ncmp,
gpu_dim=True)
# ========================================================================
# ========================================================================
# ATTENUATION FACTORS FOR COMBINED OBJECT AND BED MU-MAP
# -------------------------------------------------------------------------
# > combine attenuation and norm together depending on reconstruction mode
if recmod == 0:
asng = np.ones(psng.shape, dtype=np.float32)
else:
# > check if the attenuation sino is given as an array
if isinstance(attnsino, np.ndarray) \
and attnsino.shape==(Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']):
asng = mmraux.remgaps(attnsino, txLUT, Cnt)
log.info('using provided attenuation factor sinogram')
elif isinstance(attnsino, np.ndarray) \
and attnsino.shape==(Cnt['Naw'], Cnt['NSN11']):
asng = attnsino
log.info('using provided attenuation factor sinogram')
else:
asng = cu.zeros(psng.shape, dtype=np.float32)
petprj.fprj(asng.cuvec,
cu.asarray(mus).cuvec, txLUT, axLUT,
np.array([-1], dtype=np.int32), Cnt, 1)
# > combine attenuation and normalisation
ansng = asng * nsng
# ========================================================================
# ========================================================================
# Randoms
# -------------------------------------------------------------------------
if isinstance(randsino, np.ndarray) \
and randsino.shape==(Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']):
rsino = randsino
rsng = mmraux.remgaps(randsino, txLUT, Cnt)
else:
rsino, snglmap = randoms(hst, scanner_params)
rsng = mmraux.remgaps(rsino, txLUT, Cnt)
# ========================================================================
# ========================================================================
# SCAT
# -------------------------------------------------------------------------
if recmod == 2:
if sctsino is not None:
ssng = mmraux.remgaps(sctsino, txLUT, Cnt)
elif sctsino is None and os.path.isfile(datain['em_crr']):
emd = nimpa.getnii(datain['em_crr'])
ssn = vsm(
datain,
mumaps,
emd['im'],
scanner_params,
histo=hst,
rsino=rsino,
prcnt_scl=0.1,
emmsk=False,
)
ssng = mmraux.remgaps(ssn, txLUT, Cnt)
else:
raise ValueError(
"No emission image available for scatter estimation! " +
" Check if it's present or the path is correct.")
else:
ssng = np.zeros(rsng.shape, dtype=rsng.dtype)
# ========================================================================
log.info('------ OSEM (%d) -------', itr)
# ------------------------------------
Sn = 14 # number of subsets
# -get one subset to get number of projection bins in a subset
Sprj, s = get_subsets14(0, scanner_params)
Nprj = len(Sprj)
# -init subset array and sensitivity image for a given subset
sinoTIdx = np.zeros((Sn, Nprj + 1), dtype=np.int32)
# -init sensitivity images for each subset
imgsens = np.zeros((Sn, Cnt['SZ_IMY'], Cnt['SZ_IMX'], Cnt['SZ_IMZ']),
dtype=np.float32)
tmpsens = cu.zeros((Cnt['SZ_IMY'], Cnt['SZ_IMX'], Cnt['SZ_IMZ']),
dtype=np.float32)
for n in range(Sn):
# first number of projection for the given subset
sinoTIdx[n, 0] = Nprj
sinoTIdx[n, 1:], s = get_subsets14(n, scanner_params)
# sensitivity image
petprj.bprj(tmpsens.cuvec,
cu.asarray(ansng[sinoTIdx[n, 1:], :]).cuvec, txLUT, axLUT,
sinoTIdx[n, 1:], Cnt)
imgsens[n] = tmpsens
del tmpsens
# -------------------------------------
# -mask for reconstructed image. anything outside it is set to zero
msk = mmrimg.get_cylinder(
Cnt, rad=mask_radius, xo=0, yo=0, unival=1, gpu_dim=True) > 0.9
# -init image
img = np.ones((Cnt['SZ_IMY'], Cnt['SZ_IMX'], Cnt['SZ_IMZ']),
dtype=np.float32)
# -decay correction
lmbd = np.log(2) / resources.riLUT[Cnt['ISOTOPE']]['thalf']
if Cnt['DCYCRR'] and 't0' in hst and 'dur' in hst:
# > decay correct to the reference time (e.g., injection time) if provided
# > otherwise correct in reference to the scan start time (using the time
# > past from the start to the start time frame)
if decay_ref_time is not None:
tref = decay_ref_time
else:
tref = hst['t0']
dcycrr = np.exp(
lmbd * tref) * lmbd * hst['dur'] / (1 - np.exp(-lmbd * hst['dur']))
# apply quantitative correction to the image
qf = ncmp['qf'] / resources.riLUT[Cnt['ISOTOPE']]['BF'] / float(
hst['dur'])
qf_loc = ncmp['qf_loc']
elif not Cnt['DCYCRR'] and 't0' in hst and 'dur' in hst:
dcycrr = 1.
# apply quantitative correction to the image
qf = ncmp['qf'] / resources.riLUT[Cnt['ISOTOPE']]['BF'] / float(
hst['dur'])
qf_loc = ncmp['qf_loc']
else:
dcycrr = 1.
qf = 1.
qf_loc = 1.
# -affine matrix for the reconstructed images
B = mmrimg.image_affine(datain, Cnt)
# resolution modelling
psfkernel = psf_config(psf, Cnt)
# -time it
stime = time.time()
# import pdb; pdb.set_trace()
# ========================================================================
# OSEM RECONSTRUCTION
# -------------------------------------------------------------------------
with trange(itr,
desc="OSEM",
disable=log.getEffectiveLevel() > logging.INFO,
leave=log.getEffectiveLevel() <= logging.INFO) as pbar:
for k in pbar:
petprj.osem(img, psng, rsng, ssng, nsng, asng, sinoTIdx, imgsens,
msk, psfkernel, txLUT, axLUT, Cnt)
if np.nansum(img) < 0.1:
log.warning(
'it seems there is not enough true data to render reasonable image'
)
# img[:]=0
itr = k
break
if recmod >= 3 and k < itr - 1 and itr > 1:
sct_time = time.time()
sct = vsm(datain,
mumaps,
mmrimg.convert2e7(img, Cnt),
scanner_params,
histo=hst,
rsino=rsino,
emmsk=emmskS,
return_ssrb=return_ssrb,
return_mask=return_mask)
if isinstance(sct, dict):
ssn = sct['sino']
else:
ssn = sct
ssng = mmraux.remgaps(ssn, txLUT, Cnt)
pbar.set_postfix(scatter="%.3gs" % (time.time() - sct_time))
# save images during reconstruction if requested
if store_itr and (k + 1) in store_itr:
im = mmrimg.convert2e7(img * (dcycrr * qf * qf_loc), Cnt)
if fout is None:
fpet = os.path.join(opth, (
os.path.basename(datain['lm_bf'])[:16].replace(
'.', '-') +
f"{frmno}_t{hst['t0']}-{hst['t1']}sec_itr{k+1}{fcomment}_inrecon.nii.gz"
))
else:
fpet = os.path.join(
opth, fout + f'_itr{k+1}{fcomment}_inrecon.nii.gz')
nimpa.array2nii(im[::-1, ::-1, :], B, fpet)
log.info('recon time: %.3g', time.time() - stime)
# ========================================================================
log.info('applying decay correction of: %r', dcycrr)
log.info('applying quantification factor: %r to the whole image', qf)
log.info('for the frame duration of: %r', hst['dur'])
# additional factor for making it quantitative in absolute terms (derived from measurements)
img *= dcycrr * qf * qf_loc
# ---- save images -----
# -first convert to standard mMR image size
im = mmrimg.convert2e7(img, Cnt)
# -description text to NIfTI
# -attenuation number: if only bed present then it is 0.5
attnum = (1 * (np.sum(muh) > 0.5) + 1 * (np.sum(muo) > 0.5)) / 2.
descrip = (f"alg=osem"
f";sub=14"
f";att={attnum*(recmod>0)}"
f";sct={1*(recmod>1)}"
f";spn={Cnt['SPN']}"
f";itr={itr}"
f";fwhm=0"
f";t0={hst['t0']}"
f";t1={hst['t1']}"
f";dur={hst['dur']}"
f";qf={qf}")
# > file name of the output reconstructed image
# > (maybe used later even if not stored now)
if fout is None:
fpet = os.path.join(
opth,
(os.path.basename(datain['lm_bf']).split('.')[0] +
f"{frmno}_t{hst['t0']}-{hst['t1']}sec_itr{itr}{fcomment}.nii.gz"))
else:
fpet = os.path.join(opth, fout + f'_itr{itr}{fcomment}.nii.gz')
if store_img:
log.info('saving image to: %s', fpet)
nimpa.array2nii(im[::-1, ::-1, :], B, fpet, descrip=descrip)
im_smo = None
fsmo = None
if fwhm > 0:
im_smo = ndi.filters.gaussian_filter(im,
fwhm2sig(fwhm,
voxsize=Cnt['SZ_VOXY'] *
10),
mode='mirror')
if store_img:
fsmo = fpet.split('.nii.gz')[0] + '_smo-' + str(fwhm).replace(
'.', '-') + 'mm.nii.gz'
log.info('saving smoothed image to: ' + fsmo)
descrip.replace(';fwhm=0', ';fwhm=str(fwhm)')
nimpa.array2nii(im_smo[::-1, ::-1, :], B, fsmo, descrip=descrip)
# returning:
# (0) E7 image [can be smoothed];
# (1) file name of saved E7 image
# (2) [optional] scatter sino
# (3) [optional] single slice rebinned scatter
# (4) [optional] mask for scatter scaling based on attenuation data
# (5) [optional] random sino
# if ret_sinos and recmod>=3:
# recout = namedtuple('recout', 'im, fpet, ssn, sssr, amsk, rsn')
# recout.im = im
# recout.fpet = fout
# recout.ssn = ssn
# recout.sssr = sssr
# recout.amsk = amsk
# recout.rsn = rsino
# else:
# recout = namedtuple('recout', 'im, fpet')
# recout.im = im
# recout.fpet = fout
if ret_sinos and recmod >= 3 and itr > 1:
RecOut = namedtuple(
'RecOut', 'im, fpet, imsmo, fsmo, affine, ssn, sssr, amsk, rsn')
recout = RecOut(im, fpet, im_smo, fsmo, B, ssn, sct['ssrb'],
sct['mask'], rsino)
else:
RecOut = namedtuple('RecOut', 'im, fpet, imsmo, fsmo, affine')
recout = RecOut(im, fpet, im_smo, fsmo, B)
return recout
def osemone(datain,
mumaps,
hst,
scanner_params,
recmod=3,
itr=4,
fwhm=0.,
mask_radius=29.,
sctsino=np.array([]),
outpath='',
store_img=False,
frmno='',
fcomment='',
store_itr=[],
emmskS=False,
ret_sinos=False,
attnsino=None,
randsino=None,
normcomp=None):
#---------- sort out OUTPUT ------------
#-output file name for the reconstructed image, initially assume n/a
fout = 'n/a'
if store_img or store_itr:
if outpath == '':
opth = os.path.join(datain['corepath'], 'reconstructed')
else:
opth = outpath
mmraux.create_dir(opth)
if ret_sinos:
return_ssrb = True
return_mask = True
else:
return_ssrb = False
return_mask = False
#----------
# Get particular scanner parameters: Constants, transaxial and axial LUTs
Cnt = scanner_params['Cnt']
txLUT = scanner_params['txLUT']
axLUT = scanner_params['axLUT']
import time
from niftypet import nipet
# from niftypet.nipet.sct import mmrsct
# from niftypet.nipet.prj import mmrhist
if Cnt['VERBOSE']: print 'i> reconstruction in mode', recmod
# get object and hardware mu-maps
muh, muo = mumaps
# get the GPU version of the image dims
mus = mmrimg.convert2dev(muo + muh, Cnt)
if Cnt['SPN'] == 1:
snno = Cnt['NSN1']
elif Cnt['SPN'] == 11:
snno = Cnt['NSN11']
# remove gaps from the prompt sino
psng = mmraux.remgaps(hst['psino'], txLUT, Cnt)
#=========================================================================
# GET NORM
#-------------------------------------------------------------------------
if normcomp == None:
ncmp, _ = mmrnorm.get_components(datain, Cnt)
else:
ncmp = normcomp
print 'w> using user-defined normalisation components'
nsng = mmrnorm.get_sinog(datain, hst, axLUT, txLUT, Cnt, normcomp=ncmp)
#=========================================================================
#=========================================================================
# ATTENUATION FACTORS FOR COMBINED OBJECT AND BED MU-MAP
#-------------------------------------------------------------------------
#> combine attenuation and norm together depending on reconstruction mode
if recmod == 0:
asng = np.ones(psng.shape, dtype=np.float32)
else:
#> check if the attenuation sino is given as an array
if isinstance(attnsino, np.ndarray) \
and attnsino.shape==(Cnt['NSN11'], Cnt['NSANGLES'], Cnt['NSBINS']):
asng = mmraux.remgaps(attnsino, txLUT, Cnt)
print 'i> using provided attenuation factor sinogram'
elif isinstance(attnsino, np.ndarray) \
and attnsino.shape==(Cnt['Naw'], Cnt['NSN11']):
asng = attnsino
print 'i> using provided attenuation factor sinogram'
else:
asng = np.zeros(psng.shape, dtype=np.float32)
petprj.fprj(asng, mus, txLUT, axLUT,
np.array([-1], dtype=np.int32), Cnt, 1)
#> combine attenuation and normalisation
ansng = asng * nsng
#=========================================================================
#=========================================================================
# Randoms
#-------------------------------------------------------------------------
if isinstance(randsino, np.ndarray):
rsino = randsino
rsng = mmraux.remgaps(randsino, txLUT, Cnt)
else:
rsino, snglmap = nipet.randoms(hst, scanner_params)
rsng = mmraux.remgaps(rsino, txLUT, Cnt)
#=========================================================================
#=========================================================================
# SCAT
#-------------------------------------------------------------------------
if recmod == 2:
if sctsino.size > 0:
ssng = mmraux.remgaps(sctsino, txLUT, Cnt)
elif sctsino.size == 0 and os.path.isfile(datain['em_crr']):
emd = nimpa.getnii(datain['em_crr'])
ssn = nipet.vsm(datain,
mumaps,
emd['im'],
hst,
rsino,
scanner_params,
prcnt_scl=0.1,
emmsk=False)
ssng = mmraux.remgaps(ssn, txLUT, Cnt)
else:
print 'e> no emission image available for scatter estimation! check if it' 's present or the path is correct.'
sys.exit()
else:
ssng = np.zeros(rsng.shape, dtype=rsng.dtype)
#=========================================================================
if Cnt['VERBOSE']:
print '\n>------ OSEM (', itr, ') -------\n'
#------------------------------------
Sn = 14 # number of subsets
#-get one subset to get number of projection bins in a subset
Sprj, s = get_subsets14(0, scanner_params)
Nprj = len(Sprj)
#-init subset array and sensitivity image for a given subset
sinoTIdx = np.zeros((Sn, Nprj + 1), dtype=np.int32)
#-init sensitivity images for each subset
imgsens = np.zeros((Sn, Cnt['SZ_IMY'], Cnt['SZ_IMX'], Cnt['SZ_IMZ']),
dtype=np.float32)
for n in range(Sn):
sinoTIdx[n, 0] = Nprj #first number of projection for the given subset
sinoTIdx[n, 1:], s = get_subsets14(n, scanner_params)
# sensitivity image
petprj.bprj(imgsens[n, :, :, :], ansng[sinoTIdx[n, 1:], :], txLUT,
axLUT, sinoTIdx[n, 1:], Cnt)
#-------------------------------------
#-mask for reconstructed image. anything outside it is set to zero
msk = mmrimg.get_cylinder(
Cnt, rad=mask_radius, xo=0, yo=0, unival=1, gpu_dim=True) > 0.9
#-init image
img = np.ones((Cnt['SZ_IMY'], Cnt['SZ_IMX'], Cnt['SZ_IMZ']),
dtype=np.float32)
#-decay correction
lmbd = np.log(2) / resources.riLUT[Cnt['ISOTOPE']]['thalf']
if Cnt['DCYCRR'] and 't0' in hst and 'dur' in hst:
dcycrr = np.exp(lmbd * hst['t0']) * lmbd * hst['dur'] / (
1 - np.exp(-lmbd * hst['dur']))
# apply quantitative correction to the image
qf = ncmp['qf'] / resources.riLUT[Cnt['ISOTOPE']]['BF'] / float(
hst['dur'])
qf_loc = ncmp['qf_loc']
elif not Cnt['DCYCRR'] and 't0' in hst and 'dur' in hst:
dcycrr = 1.
# apply quantitative correction to the image
qf = ncmp['qf'] / resources.riLUT[Cnt['ISOTOPE']]['BF'] / float(
hst['dur'])
qf_loc = ncmp['qf_loc']
else:
dcycrr = 1.
qf = 1.
qf_loc = 1.
#-affine matrix for the reconstructed images
B = mmrimg.image_affine(datain, Cnt)
#-time it
stime = time.time()
# import pdb; pdb.set_trace()
#=========================================================================
# OSEM RECONSTRUCTION
#-------------------------------------------------------------------------
for k in trange(itr, disable=not Cnt['VERBOSE'], desc="OSEM"):
petprj.osem(img, msk, psng, rsng, ssng, nsng, asng, imgsens, txLUT,
axLUT, sinoTIdx, Cnt)
if np.nansum(img) < 0.1:
print '---------------------------------------------------------------------'
print 'w> it seems there is not enough true data to render reasonable image.'
print '---------------------------------------------------------------------'
#img[:]=0
itr = k
break
if recmod >= 3 and (((k < itr - 1) and (itr > 1))): # or (itr==1)
sct_time = time.time()
sct = nipet.vsm(datain,
mumaps,
mmrimg.convert2e7(img, Cnt),
hst,
rsino,
scanner_params,
emmsk=emmskS,
return_ssrb=return_ssrb,
return_mask=return_mask)
if isinstance(sct, dict):
ssn = sct['sino']
else:
ssn = sct
ssng = mmraux.remgaps(ssn, txLUT, Cnt)
if Cnt['VERBOSE']:
print 'i> scatter time:', (time.time() - sct_time)
# save images during reconstruction if requested
if store_itr and k in store_itr:
im = mmrimg.convert2e7(img * (dcycrr * qf * qf_loc), Cnt)
fout = os.path.join(opth, os.path.basename(datain['lm_bf'])[:8] \
+ frmno +'_t'+str(hst['t0'])+'-'+str(hst['t1'])+'sec' \
+'_itr'+str(k)+fcomment+'_inrecon.nii.gz')
nimpa.array2nii(im[::-1, ::-1, :], B, fout)
if Cnt['VERBOSE']: print 'i> recon time:', (time.time() - stime)
#=========================================================================
if Cnt['VERBOSE']:
print 'i> applying decay correction of', dcycrr
print 'i> applying quantification factor', qf, 'to the whole image for the frame duration of :', hst[
'dur']
img *= dcycrr * qf * qf_loc #additional factor for making it quantitative in absolute terms (derived from measurements)
#---- save images -----
#-first convert to standard mMR image size
im = mmrimg.convert2e7(img, Cnt)
#-description text to NIfTI
#-attenuation number: if only bed present then it is 0.5
attnum = (1 * (np.sum(muh) > 0.5) + 1 * (np.sum(muo) > 0.5)) / 2.
descrip = 'alg=osem'+ \
';sub=14'+ \
';att='+str(attnum*(recmod>0))+ \
';sct='+str(1*(recmod>1))+ \
';spn='+str(Cnt['SPN'])+ \
';itr='+str(itr) +\
';fwhm='+str(fwhm) +\
';t0='+str(hst['t0']) +\
';t1='+str(hst['t1']) +\
';dur='+str(hst['dur']) +\
';qf='+str(qf)
if fwhm > 0:
im = ndi.filters.gaussian_filter(im,
fwhm2sig(fwhm, Cnt),
mode='mirror')
if store_img:
fout = os.path.join(opth, os.path.basename(datain['lm_bf'])[:8] \
+ frmno +'_t'+str(hst['t0'])+'-'+str(hst['t1'])+'sec' \
+'_itr'+str(itr)+fcomment+'.nii.gz')
if Cnt['VERBOSE']: print 'i> saving image to: ', fout
nimpa.array2nii(im[::-1, ::-1, :], B, fout, descrip=descrip)
# returning:
# (0) E7 image [can be smoothed];
# (1) file name of saved E7 image
# (2) [optional] scatter sino
# (3) [optional] single slice rebinned scatter
# (4) [optional] mask for scatter scaling based on attenuation data
# (5) [optional] random sino
# if ret_sinos and recmod>=3:
# recout = namedtuple('recout', 'im, fpet, ssn, sssr, amsk, rsn')
# recout.im = im
# recout.fpet = fout
# recout.ssn = ssn
# recout.sssr = sssr
# recout.amsk = amsk
# recout.rsn = rsino
# else:
# recout = namedtuple('recout', 'im, fpet')
# recout.im = im
# recout.fpet = fout
if ret_sinos and recmod >= 3 and itr > 1:
RecOut = namedtuple('RecOut', 'im, fpet, affine, ssn, sssr, amsk, rsn')
recout = RecOut(im, fout, B, ssn, sct['ssrb'], sct['mask'], rsino)
else:
RecOut = namedtuple('RecOut', 'im, fpet, affine')
recout = RecOut(im, fout, B)
return recout
def align_mumap(
datain,
scanner_params=None,
outpath='',
reg_tool='niftyreg',
use_stored=False,
hst=None,
t0=0,
t1=0,
itr=2,
faff='',
fpet='',
fcomment='',
store=False,
store_npy=False,
petopt='ac',
musrc='ute', # another option is pct for mu-map source
ute_name='UTE2',
del_auxilary=True,
verbose=False,
):
'''
Align the a pCT or MR-derived mu-map to a PET image reconstructed to chosen
specifications (e.g., with/without attenuation and scatter corrections)
use_sotred only works if hst or t0/t1 given but not when faff.
'''
if scanner_params is None:
scanner_params = {}
# > output folder
if outpath == '':
opth = os.path.join(datain['corepath'], 'mumap-obj')
else:
opth = os.path.join(outpath, 'mumap-obj')
# > create the folder, if not existent
nimpa.create_dir(opth)
# > get the timing of PET if affine not given
if faff == '' and hst is not None and isinstance(hst, dict) and 't0' in hst:
t0 = hst['t0']
t1 = hst['t1']
# > file name for the output mu-map
fnm = 'mumap-' + musrc.upper()
# > output dictionary
mu_dct = {}
# ---------------------------------------------------------------------------
# > used stored if requested
if use_stored:
fmu_stored = fnm + '-aligned-to_t'\
+ str(t0)+'-'+str(t1)+'_'+petopt.upper()\
+ fcomment
fmupath = os.path.join(opth, fmu_stored + '.nii.gz')
if os.path.isfile(fmupath):
mudct_stored = nimpa.getnii(fmupath, output='all')
# > create output dictionary
mu_dct['im'] = mudct_stored['im']
mu_dct['affine'] = mudct_stored['affine']
# pu_dct['faff'] = faff
return mu_dct
# ---------------------------------------------------------------------------
# > tmp folder for not aligned mu-maps
tmpdir = os.path.join(opth, 'tmp')
nimpa.create_dir(tmpdir)
# > three ways of passing scanner constants <Cnt> are here decoded
if 'Cnt' in scanner_params:
Cnt = scanner_params['Cnt']
elif 'SO_IMZ' in scanner_params:
Cnt = scanner_params
else:
Cnt = rs.get_mmr_constants()
# > if affine not provided histogram the LM data for recon and registration
if not os.path.isfile(faff):
from niftypet.nipet.prj import mmrrec
# -histogram the list data if needed
if hst is None:
from niftypet.nipet import mmrhist
if 'txLUT' in scanner_params:
hst = mmrhist(datain, scanner_params, t0=t0, t1=t1)
else:
raise ValueError('Full scanner are parameters not provided\
but are required for histogramming.')
# ========================================================
# -get hardware mu-map
if 'hmumap' in datain and os.path.isfile(datain['hmumap']):
muh = np.load(datain['hmumap'], allow_pickle=True)["hmu"]
(log.info if verbose else log.debug)('loaded hardware mu-map from file:\n{}'.format(
datain['hmumap']))
elif outpath != '':
hmupath = os.path.join(outpath, "mumap-hdw", "hmumap.npz")
if os.path.isfile(hmupath):
muh = np.load(hmupath, allow_pickle=True)["hmu"]
datain["hmumap"] = hmupath
else:
raise IOError('Invalid path to the hardware mu-map')
else:
log.error('the hardware mu-map is required first.')
raise IOError('Could not find the hardware mu-map!')
# ========================================================
# -check if T1w image is available
if not {'MRT1W#', 'T1nii', 'T1bc', 'T1N4'}.intersection(datain):
log.error('no MR T1w images required for co-registration!')
raise IOError('T1w image could not be obtained!')
# ========================================================
# -if the affine is not given,
# -it will be generated by reconstructing PET image, with some or no corrections
if not os.path.isfile(faff):
# first recon pet to get the T1 aligned to it
if petopt == 'qnt':
# ---------------------------------------------
# OPTION 1 (quantitative recon with all corrections using MR-based mu-map)
# get UTE object mu-map (may not be in register with the PET data)
mudic = obj_mumap(datain, Cnt, outpath=tmpdir, del_auxilary=del_auxilary)
muo = mudic['im']
# reconstruct PET image with UTE mu-map to which co-register T1w
recout = mmrrec.osemone(datain, [muh, muo], hst, scanner_params, recmod=3, itr=itr,
fwhm=0., fcomment=fcomment + '_QNT-UTE',
outpath=os.path.join(outpath, 'PET',
'positioning'), store_img=True)
elif petopt == 'nac':
# ---------------------------------------------
# OPTION 2 (recon without any corrections for scatter and attenuation)
# reconstruct PET image with UTE mu-map to which co-register T1w
muo = np.zeros(muh.shape, dtype=muh.dtype)
recout = mmrrec.osemone(datain, [muh, muo], hst, scanner_params, recmod=1, itr=itr,
fwhm=0., fcomment=fcomment + '_NAC',
outpath=os.path.join(outpath, 'PET',
'positioning'), store_img=True)
elif petopt == 'ac':
# ---------------------------------------------
# OPTION 3 (recon with attenuation correction only but no scatter)
# reconstruct PET image with UTE mu-map to which co-register T1w
mudic = obj_mumap(datain, Cnt, outpath=tmpdir, del_auxilary=del_auxilary)
muo = mudic['im']
recout = mmrrec.osemone(datain, [muh, muo], hst, scanner_params, recmod=1, itr=itr,
fwhm=0., fcomment=fcomment + '_AC-UTE',
outpath=os.path.join(outpath, 'PET',
'positioning'), store_img=True)
fpet = recout.fpet
mu_dct['fpet'] = fpet
# ------------------------------
if musrc == 'ute' and ute_name in datain and os.path.exists(datain[ute_name]):
# change to NIfTI if the UTE sequence is in DICOM files (folder)
if os.path.isdir(datain[ute_name]):
fnew = os.path.basename(datain[ute_name])
run([Cnt['DCM2NIIX'], '-f', fnew, datain[ute_name]])
fute = glob.glob(os.path.join(datain[ute_name], fnew + '*nii*'))[0]
elif os.path.isfile(datain[ute_name]):
fute = datain[ute_name]
# get the affine transformation
if reg_tool == 'spm':
regdct = nimpa.coreg_spm(fpet, fute,
outpath=os.path.join(outpath, 'PET', 'positioning'))
elif reg_tool == 'niftyreg':
regdct = nimpa.affine_niftyreg(
fpet,
fute,
outpath=os.path.join(outpath, 'PET', 'positioning'),
executable=Cnt['REGPATH'],
omp=multiprocessing.cpu_count() / 2, # pcomment=fcomment,
rigOnly=True,
affDirect=False,
maxit=5,
speed=True,
pi=50,
pv=50,
smof=0,
smor=0,
rmsk=True,
fmsk=True,
rfwhm=15., # pillilitres
rthrsh=0.05,
ffwhm=15., # pillilitres
fthrsh=0.05,
verbose=verbose)
else:
raise ValueError('unknown registration tool requested')
faff_mrpet = regdct['faff']
elif musrc == 'pct':
ft1w = nimpa.pick_t1w(datain)
if reg_tool == 'spm':
regdct = nimpa.coreg_spm(fpet, ft1w,
outpath=os.path.join(outpath, 'PET', 'positioning'))
elif reg_tool == 'niftyreg':
regdct = nimpa.affine_niftyreg(
fpet,
ft1w,
outpath=os.path.join(outpath, 'PET', 'positioning'),
executable=Cnt['REGPATH'],
omp=multiprocessing.cpu_count() / 2,
rigOnly=True,
affDirect=False,
maxit=5,
speed=True,
pi=50,
pv=50,
smof=0,
smor=0,
rmsk=True,
fmsk=True,
rfwhm=15., # pillilitres
rthrsh=0.05,
ffwhm=15., # pillilitres
fthrsh=0.05,
verbose=verbose)
else:
raise ValueError('unknown registration tool requested')
faff_mrpet = regdct['faff']
else:
raise IOError('Floating MR image not provided or is invalid.')
else:
faff_mrpet = faff
regdct = {}
if not os.path.isfile(fpet):
raise IOError('e> the reference PET should be supplied with the affine.')
# > output file name for the aligned mu-maps
if musrc == 'pct':
# > convert to mu-values before resampling to avoid artefacts with negative values
nii = nib.load(datain['pCT'])
img = nii.get_fdata(dtype=np.float32)
img_mu = hu2mu(img)
nii_mu = nib.Nifti1Image(img_mu, nii.affine)
fflo = os.path.join(tmpdir, 'pct2mu-not-aligned.nii.gz')
nib.save(nii_mu, fflo)
freg = os.path.join(opth, 'pct2mu-aligned-' + fcomment + '.nii.gz')
elif musrc == 'ute':
freg = os.path.join(opth, 'UTE-res-tmp' + fcomment + '.nii.gz')
if 'UTE' not in datain:
fnii = 'converted-from-DICOM_'
tstmp = nimpa.time_stamp(simple_ascii=True)
# convert the DICOM mu-map images to nii
if 'mumapDCM' not in datain:
raise IOError('DICOM with the UTE mu-map are not given.')
run([Cnt['DCM2NIIX'], '-f', fnii + tstmp, '-o', opth, datain['mumapDCM']])
# piles for the T1w, pick one:
fflo = glob.glob(os.path.join(opth, '*' + fnii + tstmp + '*.nii*'))[0]
else:
if os.path.isfile(datain['UTE']):
fflo = datain['UTE']
else:
raise IOError('The provided NIfTI UTE path is not valid.')
# > call the resampling routine to get the pCT/UTE in place
if reg_tool == "spm":
nimpa.resample_spm(fpet, fflo, faff_mrpet, fimout=freg, del_ref_uncmpr=True,
del_flo_uncmpr=True, del_out_uncmpr=True)
else:
nimpa.resample_niftyreg(fpet, fflo, faff_mrpet, fimout=freg, executable=Cnt['RESPATH'],
verbose=verbose)
# -get the NIfTI of registered image
nim = nib.load(freg)
A = nim.affine
imreg = nim.get_fdata(dtype=np.float32)
imreg = imreg[:, ::-1, ::-1]
imreg = np.transpose(imreg, (2, 1, 0))
# -convert to mu-values; sort out the file name too.
if musrc == 'pct':
mu = imreg
elif musrc == 'ute':
mu = np.float32(imreg) / 1e4
# -remove the converted file from DICOMs
os.remove(fflo)
else:
raise NameError('Confused o_O')
# > get rid of negatives and nans
mu[mu < 0] = 0
mu[np.isnan(mu)] = 0
# > return image dictionary with the image itself and other parameters
mu_dct['im'] = mu
mu_dct['affine'] = A
mu_dct['faff'] = faff_mrpet
if store or store_npy:
nimpa.create_dir(opth)
if faff == '':
fname = fnm + '-aligned-to_t'\
+ str(t0)+'-'+str(t1)+'_'+petopt.upper()\
+ fcomment
else:
fname = fnm + '-aligned-to-given-affine' + fcomment
if store_npy:
fnp = os.path.join(opth, fname + ".npz")
np.savez(fnp, mu=mu, A=A)
if store:
# > NIfTI
fmu = os.path.join(opth, fname + '.nii.gz')
nimpa.array2nii(mu[::-1, ::-1, :], A, fmu)
mu_dct['fim'] = fmu
if del_auxilary:
os.remove(freg)
if musrc == 'ute' and not os.path.isfile(faff):
os.remove(fute)
shutil.rmtree(tmpdir)
return mu_dct
def hdw_mumap(datain, hparts, params, outpath='', use_stored=False, del_interm=True):
'''Get hardware mu-map components, including bed, coils etc.'''
# two ways of passing Cnt are here decoded
if 'Cnt' in params:
Cnt = params['Cnt']
else:
Cnt = params
if outpath != '':
fmudir = os.path.join(outpath, 'mumap-hdw')
else:
fmudir = os.path.join(datain['corepath'], 'mumap-hdw')
nimpa.create_dir(fmudir)
# if requested to use the stored hardware mu_map get it from the path in datain
if use_stored and "hmumap" in datain and os.path.isfile(datain["hmumap"]):
if datain['hmumap'].endswith(('.nii', '.nii.gz')):
dct = nimpa.getnii(datain['hmumap'], output='all')
hmu = dct['im']
A = dct['affine']
fmu = datain['hmumap']
elif datain["hmumap"].endswith(".npz"):
arr = np.load(datain["hmumap"], allow_pickle=True)
hmu, A, fmu = arr["hmu"], arr["A"], arr["fmu"]
log.info('loaded hardware mu-map from file: {}'.format(datain['hmumap']))
fnp = datain['hmumap']
elif outpath and os.path.isfile(os.path.join(fmudir, "hmumap.npz")):
fnp = os.path.join(fmudir, "hmumap.npz")
arr = np.load(fnp, allow_pickle=True)
hmu, A, fmu = arr["hmu"], arr["A"], arr["fmu"]
datain['hmumap'] = fnp
# otherwise generate it from the parts through resampling the high resolution CT images
else:
hmupos = get_hmupos(datain, hparts, Cnt, outpath=outpath)
# just to get the dims, get the ref image
nimo = nib.load(hmupos[0]['niipath'])
A = nimo.affine
imo = nimo.get_fdata(dtype=np.float32)
imo[:] = 0
for i in hparts:
fin = os.path.join(
os.path.dirname(hmupos[0]['niipath']),
'r' + os.path.basename(hmupos[i]['niipath']).split('.')[0] + '.nii.gz')
nim = nib.load(fin)
mu = nim.get_fdata(dtype=np.float32)
mu[mu < 0] = 0
imo += mu
hdr = nimo.header
hdr['cal_max'] = np.max(imo)
hdr['cal_min'] = np.min(imo)
fmu = os.path.join(os.path.dirname(hmupos[0]['niipath']), 'hardware_umap.nii.gz')
hmu_nii = nib.Nifti1Image(imo, A)
nib.save(hmu_nii, fmu)
hmu = np.transpose(imo[:, ::-1, ::-1], (2, 1, 0))
# save the objects to numpy arrays
fnp = os.path.join(fmudir, "hmumap.npz")
np.savez(fnp, hmu=hmu, A=A, fmu=fmu)
# ppdate the datain dictionary (assuming it is mutable)
datain['hmumap'] = fnp
if del_interm:
for fname in glob.glob(os.path.join(fmudir, '_*.nii*')):
os.remove(fname)
for fname in glob.glob(os.path.join(fmudir, 'r_*.nii*')):
os.remove(fname)
# peturn image dictionary with the image itself and some other stats
hmu_dct = {'im': hmu, 'fim': fmu, 'affine': A}
if 'fnp' in locals():
hmu_dct['fnp'] = fnp
return hmu_dct
def obtain_image(img, Cnt=[], imtype=''):
'''
Obtain the image (hardware or object mu-map) from file,
numpy array, dictionary or empty list (assuming blank then).
The image has to have the dimensions of the PET image used as in Cnt['SO_IM[X-Z]'].
'''
from os import path
log = logging.getLogger(__name__)
# establishing what and if the image object has been provided
# all findings go to the output dictionary
output = {}
if isinstance(img, dict):
if Cnt and img['im'].shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'],
Cnt['SO_IMX']):
log.error(
'provided ' + imtype +
' via the dictionary has inconsistent dimensions compared to Cnt.'
)
raise ValueError('Wrong dimensions of the mu-map')
else:
output['im'] = img['im']
output['exists'] = True
output['fim'] = img['fim']
if 'faff' in img: output['faff'] = img['faff']
if 'fmuref' in img: output['fmuref'] = img['fmuref']
if 'affine' in img: output['affine'] = img['affine']
log.debug('using ' + imtype + ' from dictionary')
elif isinstance(img, (np.ndarray, np.generic)):
if Cnt and img.shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']):
log.error(
'provided ' + imtype +
' via the numpy array has inconsistent dimensions compared to Cnt.'
)
raise ValueError('Wrong dimensions of the mu-map')
else:
output['im'] = img
output['exists'] = True
output['fim'] = ''
log.debug('using hardware mu-map from numpy array.')
elif isinstance(img, basestring):
if path.isfile(img):
from niftypet import nimpa
imdct = nimpa.getnii(img, output='all')
output['im'] = imdct['im']
output['affine'] = imdct['affine']
if Cnt and output['im'].shape != (Cnt['SO_IMZ'], Cnt['SO_IMY'],
Cnt['SO_IMX']):
log.error(
'provided ' + imtype +
' via file has inconsistent dimensions compared to Cnt.')
raise ValueError('Wrong dimensions of the mu-map')
else:
output['exists'] = True
output['fim'] = img
log.debug('using ' + imtype + ' from NIfTI file.')
else:
log.error('provided ' + imtype + ' path is invalid.')
return None
elif isinstance(img, list):
output['im'] = np.zeros((Cnt['SO_IMZ'], Cnt['SO_IMY'], Cnt['SO_IMX']),
dtype=np.float32)
log.info(imtype + ' has not been provided -> using blank.')
output['fim'] = ''
output['exists'] = False
#------------------------------------------------------------------------
return output
