def get_dcm_slice_timing(dicom_files):
# From Satrajit Ghosh's code at: https://github.com/satra/nipype/blob/enh/restingwf/examples/rsfmri_preprocessing.py#L47
from dcmstack.extract import default_extractor
from dicom import read_file
from nipype.utils.filemanip import filename_to_list
meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
return (meta['RepetitionTime'] / 1000., meta['CsaImage.MosaicRefAcqTimes'],
meta['SpacingBetweenSlices'])
def get_info(dicom_files): """Given a Siemens dicom file return metadata Returns ------- Slice Acquisition Times """ meta = default_extractor(read_file(filename_to_list(dicom_files)[0], stop_before_pixels=True, force=True)) # returning STA in seconds... return meta['CsaImage.MosaicRefAcqTimes']
def get_info(dicom_files):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
meta = default_extractor(read_file(filename_to_list(dicom_files)[0], stop_before_pixels=True, force=True))
return (meta["RepetitionTime"] / 1000.0, meta["CsaImage.MosaicRefAcqTimes"], meta["SpacingBetweenSlices"])
def get_info(dicom_files):
"""Given a Siemens dicom file return metadata
Returns
-------
Slice Acquisition Times
"""
meta = default_extractor(
read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
# returning STA in seconds...
return meta['CsaImage.MosaicRefAcqTimes']
def get_info(dicom_files):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
return (meta['RepetitionTime'] / 1000., meta['CsaImage.MosaicRefAcqTimes'],
meta['SpacingBetweenSlices'])
def get_info(dicom_files):
from dcmstack.extract import default_extractor
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
return (meta['RepetitionTime']/1000., meta['CsaImage.MosaicRefAcqTimes'],
meta['SpacingBetweenSlices'])
def get_info(dicom_files):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
try:
from dcmstack.extract import default_extractor
except:
raise ImportError("To use this function install dcmstack: pip install "
"git+https://github.com/moloney/dcmstack.git@c12d27d2c802d75a33ad70110124500a83e851ee#egg=dcmstack")
meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
stop_before_pixels=True,
force=True))
return (meta['RepetitionTime'] / 1000., meta['CsaImage.MosaicRefAcqTimes'],
meta['SpacingBetweenSlices'])
def fast_retrieve_dicom(self, path, meta=None):
# add_dcm takes a lot of time if we have to reexamine metadata everytime
# so we copy metadata if we're in the same volume of a time series
dcm = self.client.retrieve_dicom(path)
try:
#get the volume number
tpid = dcm[(0x0020, 0x0100)].value
except KeyError:
# then were not looking at a time series
return (dcm, None)
#otherwise
if tpid != self.tpid:
# then we're in a new volume
meta = default_extractor(dcm)
else:
#we're in the same volume, so use old meta:
meta = self.volumizer._get_meta(dcm, meta)
return (dcm, meta)
def get_info(dicom_file):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
from dcmstack.extract import default_extractor
import numpy as np
from dicom import read_file
from nipype.utils.filemanip import filename_to_list
meta = default_extractor(read_file(filename_to_list(dicom_file)[0], stop_before_pixels=True, force=True))
TR = meta["RepetitionTime"] / 1000.0
slice_times_pre = meta["CsaImage.MosaicRefAcqTimes"]
slice_times = (np.array(slice_times_pre) / 1000.0).tolist()
slice_thickness = meta["SpacingBetweenSlices"]
return TR, slice_times, slice_thickness
def fast_retrieve_dicom(self, path, meta=None):
# add_dcm takes a lot of time if we have to reexamine metadata everytime
# so we copy metadata if we're in the same volume of a time series
dcm = self.client.retrieve_dicom(path)
try:
#get the volume number
tpid = dcm[(0x0020, 0x0100)].value
except KeyError:
# then were not looking at a time series
return(dcm, None)
#otherwise
if tpid != self.tpid:
# then we're in a new volume
meta = default_extractor(dcm)
else:
#we're in the same volume, so use old meta:
meta = self.volumizer._get_meta(dcm, meta)
return(dcm, meta)
def get_info(dicom_file):
"""Given a Siemens dicom file return metadata
Returns
-------
RepetitionTime
Slice Acquisition Times
Spacing between slices
"""
from dcmstack.extract import default_extractor
import numpy as np
from dicom import read_file
from nipype.utils.filemanip import filename_to_list
meta = default_extractor(read_file(filename_to_list(dicom_file)[0],
stop_before_pixels=True,
force=True))
TR=meta['RepetitionTime']/1000.
slice_times_pre=meta['CsaImage.MosaicRefAcqTimes']
slice_times = (np.array(slice_times_pre)/1000.).tolist()
slice_thickness = meta['SpacingBetweenSlices']
return TR, slice_times, slice_thickness
def assemble_volume(self, slices):
"""Put each dicom slice together into a nibabel nifti image object."""
# Build a DicomStack from each of the slices
tic = time.time()
meta = default_extractor(slices[0])
stack = DicomStack()
for f in slices:
# without new_meta, this takes 99% of time in this function by
# wasting cycles on looking up redundant data.
new_meta = self._get_meta(f, meta)
stack.add_dcm(f, new_meta)
# Convert into a Nibabel Nifti object
nii_img = stack.to_nifti(voxel_order="")
# Build the volume dictionary we will put in the dicom queue
dcm = slices[0]
exam, series, acquisition = self.dicom_esa(dcm)
volume = dict(
exam=exam,
series=series,
acquisition=acquisition,
patient_id=dcm.PatientID,
series_description=dcm.SeriesDescription,
tr=float(dcm.RepetitionTime) / 1000,
ntp=float(dcm.NumberOfTemporalPositions),
image=nii_img,
)
time_it(tic, "Assembled a volume", level='info')
if self.keep_vols:
self.assembled_volumes.append(volume)
self.last_assembled_time = time.time()
return volume
def assemble_volume(self, slices):
"""Put each dicom slice together into a nibabel nifti image object."""
# Build a DicomStack from each of the slices
tic = time.time()
meta = default_extractor(slices[0])
stack = DicomStack()
for f in slices:
# without new_meta, this takes 99% of time in this function by
# wasting cycles on looking up redundant data.
new_meta = self._get_meta(f, meta)
stack.add_dcm(f, new_meta)
# Convert into a Nibabel Nifti object
nii_img = stack.to_nifti(voxel_order="")
# Build the volume dictionary we will put in the dicom queue
dcm = slices[0]
exam, series, acquisition = self.dicom_esa(dcm)
volume = dict(
exam=exam,
series=series,
acquisition=acquisition,
patient_id=dcm.PatientID,
series_description=dcm.SeriesDescription,
tr=float(dcm.RepetitionTime) / 1000,
ntp=float(dcm.NumberOfTemporalPositions),
image=nii_img,
)
time_it(tic, "Assembled a volume", level='info')
if self.keep_vols:
self.assembled_volumes.append(volume)
self.last_assembled_time = time.time()
return volume
from dcmstack.extract import default_extractor
import pydicom
import os
data_path = '/media/charesti-start/data/irsa-fmri/dicom/CBU101295/20100930_101706/Series_003_CBU_EPI_BOLD_216/'
dicom_file = os.path.join(
data_path, '1.3.12.2.1107.5.2.32.35119.2010093010310825996437574.dcm')
ds = pydicom.dcmread(dicom_file)
meta = default_extractor(
ds)
st_times = np.asarray(meta['CsaImage.MosaicRefAcqTimes'])/1000
