def parse_one(self):
"""
Composer function, parses all metadata that can be parsed from a single dicom.
Called by NIMSDicom init, if dicom manufacturer is Siemens.
"""
mr.parse_standard_mr_tags(self)
self.psd_name = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.tSequenceFileName', str,
'').lower().replace('%', '', 1)
self.psd_iname = self.getelem(self._hdr, 'SeriesDescription')
self.fov_x = self.getelem(
self._hdr,
'CsaSeries.MrPhoenixProtocol.sSliceArray.asSlice[0].dPhaseFOV',
float) # XXX fov-x = PhaseFOV?
self.fov_y = self.getelem(
self._hdr,
'CsaSeries.MrPhoenixProtocol.sSliceArray.asSlice[0].dReadoutFOV',
float) # XXX fov-y = ReadoutFOV?
self.receive_coil_name = self.getelem(self._hdr, 'CsaImage.ImaCoilString')
slice_duration = self.getelem(self._hdr,
'CsaImage.SliceMeasurementDuration', float,
0.)
self.slice_duration = slice_duration / 1e6 if slice_duration else None
self.prescribed_duration = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.lScanTimeSec') # FIXME
self.duration = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.lTotalScanTimeSec'
) # FIXME: not guaranteed
self.acq_no = None # siemens acq # indicates the brain volume instance. varies within one scan.
# this is not an ideal solution, as it assumes that EPI timeserise are always stored in mosaic
# for mosaics, lReps = prescribed timepoints. non-mosaics and non-timeseries will not have this tag
lRep = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.lRepetitions',
int)
self.num_timepoints = (lRep + 1) if lRep else None
self.dwi_dirs = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.sDiffusion.lDiffDirections',
int, None)
if (self.dwi_dirs or 0) > 1:
self.is_dwi = True
self.num_timepoints = 1
# some siemens MR dicoms are not reconstructable
if 'CSAPARALLEL' in self.image_type:
self.is_non_image = True
if 'POSDISP' in self.image_type:
self.is_non_image = True
if self.image_type == SIEMENS_TYPE_DIS2D:
# had 2 image orientations, and other metdata that varied between dicoms, and is not a localizer. AFNI cannot reconstsruct.
# this is a specific hack fix. A more general fix is to check each dicom to see if the orientation matches the first dicom
# but checking EVERY dicom isn't very ideal solution (some datasets might have an ENORMOUS number of dicoms)
self.is_non_image = True
infer_psd_type(self)
mr.adjust_fov_acqmat(self)
mr.infer_scan_type(self)
def parse_all(self):
"""
Composer function, parses all metadata that requires all dicoms.
Called by NIMSDicom load_data, if dicom manufacturer is Siemens.
"""
if 'MOSAIC' not in self.image_type:
log.debug('SIEMENS SINGLE SLICE DICOM')
self.total_num_slices = len(self._dcm_list)
# num slices from CSA
self.num_slices = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.lSize', int,
self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sGroupArray.asGroup[0].nSize', int, None)
)
self.num_timepoints = self.total_num_slices / self.num_slices if self.num_slices else None
elif 'MOSAIC' in self.image_type: # explicit for readability
log.debug('SIEMENS MOSAIC')
self.num_slices = self.getelem(self._hdr, 'CsaImage.NumberOfImagesInMosaic', int, self.getelem(self._hdr, 'NumberOfImagesInMosaic', int))
self.num_timepoints = len(self._dcm_list)
mosaic_dim = int(self.num_slices ** 0.5)
if mosaic_dim ** 2 < self.num_slices:
mosaic_dim += 1
self.size = [x / mosaic_dim for x in self.size]
self.fov = [x / mosaic_dim for x in self.fov]
self.total_num_slices = self.num_slices * self.num_timepoints
log.debug('num slices / vol: %s' % str(self.num_slices)) # stringify to be able to log NoneType
self.duration = self.num_timepoints * (self.num_averages or 1) * self.tr if self.num_timepoints and self.tr else None
# CsaSeries.MrPhoenixProtocol.sSliceArray.ucMode indicates siemens slice order:
# Siemens; 1 Ascending, 2 Descending, and 4 Interleaved Ascending.
# NIFTI; 1 Ascending, 2 Descending, and 4 Interleaving Descending
# Siemens slice order 4 could be nifti slice order 3 or 5 depending on num_slices
# - nifti slice order 3: interleave_asc, odd first, odd num slices, interleave INC
# - nifti slice order 5: interleave_asc, even first, even num slices, interleave INC 2
self.slice_order = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.ucMode', None, 0)
if self.slice_order == 4: # don't try to guess if num_slices can't be determined
if self.num_slices % 2 != 0:
self.slice_order = 3 # interleaved ascending, odd first
else:
self.slice_order = 5 # interleaved ascending, even first
self.num_receivers = len([self._hdr[key] for key in self._hdr if key.endswith('sCoilElementID.tCoilID')])
if self.total_num_slices < MAX_LOC_DCMS:
slice_norms = [np.cross(np.matrix(d.get('ImageOrientationPatient')[0:3]), np.matrix(d.get('ImageOrientationPatient')[3:6]))[0] for d in self._dcm_list]
norm_diff = [np.abs(np.dot(slice_norms[0], n)).round(2) for n in slice_norms]
self.is_localizer = bool(len(set(norm_diff)) > 1)
if self.is_dwi:
self.bvals = np.array([MetaExtractor(d).get(TAG_BVALUE, 0.) for d in self._dcm_list[0:self.num_slices]])
self.bvecs = np.array([MetaExtractor(d).get(TAG_BVEC, [0., 0., 0.]) for d in self._dcm_list[0:self.num_slices]]).transpose()
mr.infer_scan_type(self) # infer scan type again after determining all info
def parse_one(self):
"""
Composer function, parses all metadata that can be parsed from a single dicom.
Called by NIMSDicom init, if dicom manufacturer is Siemens.
"""
mr.parse_standard_mr_tags(self)
self.psd_name = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.tSequenceFileName', str, '').lower().replace('%', '', 1)
self.psd_iname = self.getelem(self._hdr, 'SeriesDescription')
self.fov_x = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.asSlice[0].dPhaseFOV', float) # XXX fov-x = PhaseFOV?
self.fov_y = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.asSlice[0].dReadoutFOV', float) # XXX fov-y = ReadoutFOV?
self.receive_coil_name = self.getelem(self._hdr, 'CsaImage.ImaCoilString')
slice_duration = self.getelem(self._hdr, 'CsaImage.SliceMeasurementDuration', float, 0.)
self.slice_duration = slice_duration / 1e6 if slice_duration else None
self.prescribed_duration = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.lScanTimeSec') # FIXME
self.duration = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.lTotalScanTimeSec') # FIXME: not guaranteed
self.acq_no = None # siemens acq # indicates the brain volume instance. varies within one scan.
# this is not an ideal solution, as it assumes that EPI timeserise are always stored in mosaic
# for mosaics, lReps = prescribed timepoints. non-mosaics and non-timeseries will not have this tag
lRep = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.lRepetitions', int)
self.num_timepoints = (lRep + 1) if lRep else None
self.dwi_dirs = self.getelem(self._hdr, 'CsaSeries.MrPhoenixProtocol.sDiffusion.lDiffDirections', int, None)
if (self.dwi_dirs or 0) > 1:
self.is_dwi = True
self.num_timepoints = 1
# some siemens MR dicoms are not reconstructable
if 'CSAPARALLEL' in self.image_type:
self.is_non_image = True
if 'POSDISP' in self.image_type:
self.is_non_image = True
if self.image_type == SIEMENS_TYPE_DIS2D:
# had 2 image orientations, and other metdata that varied between dicoms, and is not a localizer. AFNI cannot reconstsruct.
# this is a specific hack fix. A more general fix is to check each dicom to see if the orientation matches the first dicom
# but checking EVERY dicom isn't very ideal solution (some datasets might have an ENORMOUS number of dicoms)
self.is_non_image = True
infer_psd_type(self)
mr.adjust_fov_acqmat(self)
mr.infer_scan_type(self)
def parse_one(self):
"""
Composer function, parses all metadata that can be parsed from a single dicom.
Called by NIMSData init, if dicom manufacturer is GE Medical Sytems.
"""
mr.parse_standard_mr_tags(self)
self.psd_name = self.getelem(self._hdr, 'PulseSequenceName', str,
'').lower()
self.psd_iname = self.getelem(self._hdr, 'InternalPulseSequenceName')
self.fov_x, self.fov_y = 2 * [
self.getelem(self._hdr, 'ReconstructionDiameter', float)
]
self.receive_coil_name = self.getelem(self._hdr, 'ReceiveCoilName')
self.mt_offset_hz = self.getelem(self._hdr, 'OffsetFrequency', float)
effective_echo_spacing = self.getelem(self._hdr, 'EffectiveEchoSpacing',
float)
self.effective_echo_spacing = effective_echo_spacing / 1e6 if effective_echo_spacing else None
asset_r = self.getelem(self._hdr, 'AssetRFactors', None, [None, None])
if isinstance(
asset_r, unicode
) and '\\' in asset_r: # GE Signa HDxt stores asset as string '1\1'
asset_r = map(
int, asset_r.split('\\')) # reformat to [1, 1] for consistency
elif isinstance(asset_r, float): # asset_r can be single item float
asset_r = [None, None]
self.phase_encode_undersample, self.slice_encode_undersample = asset_r
# some very old Ge systems will output dicoms that don't define Locations in Acquition, or define it in a way
# that is weird. It may incorrectly label the value type as OB, but not be able to translate the value, resulting
# in the MetaExtractor excluding it from the it's output metadata.
self.num_slices = self.getelem(self._hdr, 'LocationsInAcquisition', int)
self.total_num_slices = self.getelem(self._hdr, 'ImagesInAcquisition', int)
self.num_timepoints = self.getelem(self._hdr, 'NumberOfTemporalPositions',
int)
# slice check could end up wrong, if both total_num_slices and num_slices are None
# could force num_slices and total_num_slices into different ORs, to prevent matching if both are None
# thus only when they are both defined, AND not equal, can this test pass
if (self.total_num_slices or 1) == (self.num_slices or 0):
self.total_num_slices = (self.num_slices or 1) * (self.num_timepoints
or 1)
log.debug('adjusted total_num_slices from %3d to %3d' %
(self.num_slices,
self.total_num_slices)) # num_slices == 'old' total_num
# some localizer don't have header field to indicate the number of slices
# per acquisition. If the total_number of slices is set, and the num_timepoints is 1
# then the number of slices should be equal to total number of slices
if not self.num_slices and (self.num_timepoints or 1) == 1:
self.num_slices = self.total_num_slices
prescribed_duration = (self.tr or 0) * (self.num_timepoints or 0) * (
self.num_averages or 1) # FIXME: only works for fMRI, not anatomical
if prescribed_duration != 0:
self.prescribed_duration = prescribed_duration
self.duration = prescribed_duration
else:
self.prescribed_duration = None
self.duration = None
dwi_dirs = self.getelem(self._hdr,
'UserData24{#DTIDiffusionDir.,Release10.0&Above}',
float)
self.dwi_dirs = int(dwi_dirs) if dwi_dirs else None
if self.image_type == GEMS_TYPE_ORIG and (self.dwi_dirs or 0) >= 6:
self.is_dwi = True
self.num_timepoints = 1
if self.image_type == GEMS_TYPE_DERIVED_RFMT:
self.is_non_image = True
infer_psd_type(self)
mr.adjust_fov_acqmat(self)
mr.infer_scan_type(self)
def parse_all(self):
"""
Composer function, parses all metadata that requires all dicoms.
Called by NIMSDicom load_data, if dicom manufacturer is Siemens.
"""
if 'MOSAIC' not in self.image_type:
log.debug('SIEMENS SINGLE SLICE DICOM')
self.total_num_slices = len(self._dcm_list)
# num slices from CSA
self.num_slices = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.lSize', int,
self.getelem(
self._hdr,
'CsaSeries.MrPhoenixProtocol.sGroupArray.asGroup[0].nSize',
int, None))
self.num_timepoints = self.total_num_slices / self.num_slices if self.num_slices else None
elif 'MOSAIC' in self.image_type: # explicit for readability
log.debug('SIEMENS MOSAIC')
self.num_slices = self.getelem(
self._hdr, 'CsaImage.NumberOfImagesInMosaic', int,
self.getelem(self._hdr, 'NumberOfImagesInMosaic', int))
self.num_timepoints = len(self._dcm_list)
mosaic_dim = int(self.num_slices**0.5)
if mosaic_dim**2 < self.num_slices:
mosaic_dim += 1
self.size = [x / mosaic_dim for x in self.size]
self.fov = [x / mosaic_dim for x in self.fov]
self.total_num_slices = self.num_slices * self.num_timepoints
log.debug('num slices / vol: %s' %
str(self.num_slices)) # stringify to be able to log NoneType
self.duration = self.num_timepoints * (
self.num_averages
or 1) * self.tr if self.num_timepoints and self.tr else None
# CsaSeries.MrPhoenixProtocol.sSliceArray.ucMode indicates siemens slice order:
# Siemens; 1 Ascending, 2 Descending, and 4 Interleaved Ascending.
# NIFTI; 1 Ascending, 2 Descending, and 4 Interleaving Descending
# Siemens slice order 4 could be nifti slice order 3 or 5 depending on num_slices
# - nifti slice order 3: interleave_asc, odd first, odd num slices, interleave INC
# - nifti slice order 5: interleave_asc, even first, even num slices, interleave INC 2
self.slice_order = self.getelem(
self._hdr, 'CsaSeries.MrPhoenixProtocol.sSliceArray.ucMode', None, 0)
if self.slice_order == 4: # don't try to guess if num_slices can't be determined
if self.num_slices % 2 != 0:
self.slice_order = 3 # interleaved ascending, odd first
else:
self.slice_order = 5 # interleaved ascending, even first
self.num_receivers = len([
self._hdr[key] for key in self._hdr
if key.endswith('sCoilElementID.tCoilID')
])
if self.total_num_slices < MAX_LOC_DCMS:
slice_norms = [
np.cross(np.matrix(d.get('ImageOrientationPatient')[0:3]),
np.matrix(d.get('ImageOrientationPatient')[3:6]))[0]
for d in self._dcm_list
]
norm_diff = [
np.abs(np.dot(slice_norms[0], n)).round(2) for n in slice_norms
]
self.is_localizer = bool(len(set(norm_diff)) > 1)
if self.is_dwi:
self.bvals = np.array(
[MetaExtractor(d).get(TAG_BVALUE, 0.) for d in self._dcm_list[:]])
self.bvecs = np.array([
MetaExtractor(d).get(TAG_BVEC, [0., 0., 0.])
for d in self._dcm_list[:]
]).transpose()
mr.infer_scan_type(
self) # infer scan type again after determining all info
def parse_one(self):
"""
Composer function, parses all metadata that can be parsed from a single dicom.
Called by NIMSData init, if dicom manufacturer is GE Medical Sytems.
"""
mr.parse_standard_mr_tags(self)
self.psd_name = self.getelem(self._hdr, 'PulseSequenceName', str, '').lower()
self.psd_iname = self.getelem(self._hdr, 'InternalPulseSequenceName')
self.fov_x, self.fov_y = 2 * [self.getelem(self._hdr, 'ReconstructionDiameter', float)]
self.receive_coil_name = self.getelem(self._hdr, 'ReceiveCoilName')
self.mt_offset_hz = self.getelem(self._hdr, 'OffsetFrequency', float)
effective_echo_spacing = self.getelem(self._hdr, 'EffectiveEchoSpacing', float)
self.effective_echo_spacing = effective_echo_spacing / 1e6 if effective_echo_spacing else None
asset_r = self.getelem(self._hdr, 'AssetRFactors', None, [None, None])
if isinstance(asset_r, unicode) and '\\' in asset_r: # GE Signa HDxt stores asset as string '1\1'
asset_r = map(int, asset_r.split('\\')) # reformat to [1, 1] for consistency
elif isinstance(asset_r, float): # asset_r can be single item float
asset_r = [None, None]
self.phase_encode_undersample, self.slice_encode_undersample = asset_r
# some very old Ge systems will output dicoms that don't define Locations in Acquition, or define it in a way
# that is weird. It may incorrectly label the value type as OB, but not be able to translate the value, resulting
# in the MetaExtractor excluding it from the it's output metadata.
self.num_slices = self.getelem(self._hdr, 'LocationsInAcquisition', int)
self.total_num_slices = self.getelem(self._hdr, 'ImagesInAcquisition', int)
self.num_timepoints = self.getelem(self._hdr, 'NumberOfTemporalPositions', int)
# slice check could end up wrong, if both total_num_slices and num_slices are None
# could force num_slices and total_num_slices into different ORs, to prevent matching if both are None
# thus only when they are both defined, AND not equal, can this test pass
if (self.total_num_slices or 1) == (self.num_slices or 0):
self.total_num_slices = (self.num_slices or 1) * (self.num_timepoints or 1)
log.debug('adjusted total_num_slices from %3d to %3d' % (self.num_slices, self.total_num_slices)) # num_slices == 'old' total_num
# some localizer don't have header field to indicate the number of slices
# per acquisition. If the total_number of slices is set, and the num_timepoints is 1
# then the number of slices should be equal to total number of slices
if not self.num_slices and (self.num_timepoints or 1) == 1:
self.num_slices = self.total_num_slices
prescribed_duration = (self.tr or 0) * (self.num_timepoints or 0) * (self.num_averages or 1) # FIXME: only works for fMRI, not anatomical
if prescribed_duration != 0:
self.prescribed_duration = prescribed_duration
self.duration = prescribed_duration
else:
self.prescribed_duration = None
self.duration = None
dwi_dirs = self.getelem(self._hdr, 'UserData24{#DTIDiffusionDir.,Release10.0&Above}', float)
self.dwi_dirs = int(dwi_dirs) if dwi_dirs else None
if self.image_type == GEMS_TYPE_ORIG and (self.dwi_dirs or 0) >= 6:
self.is_dwi = True
self.num_timepoints = 1
if self.image_type == GEMS_TYPE_DERIVED_RFMT:
self.is_non_image = True
infer_psd_type(self)
mr.adjust_fov_acqmat(self)
mr.infer_scan_type(self)
