def _is_valid_imaging_dicom(dicom_header):
"""
Function will do some basic checks to see if this is a valid imaging dicom
"""
# if it is philips and multiframe dicom then we assume it is ok
try:
if common.is_philips([dicom_header]):
if common.is_multiframe_dicom([dicom_header]):
return True
if "SeriesInstanceUID" not in dicom_header:
return False
if "InstanceNumber" not in dicom_header:
return False
if "ImageOrientationPatient" not in dicom_header or len(dicom_header.ImageOrientationPatient) < 6:
return False
if "ImagePositionPatient" not in dicom_header or len(dicom_header.ImagePositionPatient) < 3:
return False
# for all others if there is image position patient we assume it is ok
if Tag(0x0020, 0x0037) not in dicom_header:
return False
return True
except (KeyError, AttributeError):
return False
def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for philips images.
As input philips images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the output nifti
:param dicom_input: directory with dicom files for 1 scan
"""
assert common.is_philips(dicom_input)
if common.is_multiframe_dicom(dicom_input):
_assert_explicit_vr(dicom_input)
logger.info('Found multiframe dicom')
if _is_multiframe_4d(dicom_input):
logger.info('Found sequence type: MULTIFRAME 4D')
return _multiframe_to_nifti(dicom_input, output_file)
if _is_multiframe_anatomical(dicom_input):
logger.info('Found sequence type: MULTIFRAME ANATOMICAL')
return _multiframe_to_nifti(dicom_input, output_file)
else:
logger.info('Found singleframe dicom')
grouped_dicoms = _get_grouped_dicoms(dicom_input)
if _is_singleframe_4d(dicom_input):
logger.info('Found sequence type: SINGLEFRAME 4D')
return _singleframe_to_nifti(grouped_dicoms, output_file)
logger.info('Assuming anatomical data')
return convert_generic.dicom_to_nifti(dicom_input, output_file)
def _is_valid_imaging_dicom(dicom_header):
"""
Function will do some basic checks to see if this is a valid imaging dicom
"""
# if it is philips and multiframe dicom then we assume it is ok
try:
if common.is_philips([dicom_header]):
if common.is_multiframe_dicom([dicom_header]):
return True
if "SeriesInstanceUID" not in dicom_header:
return False
if "InstanceNumber" not in dicom_header:
return False
if "ImageOrientationPatient" not in dicom_header or len(dicom_header.ImageOrientationPatient) < 6:
return False
if "ImagePositionPatient" not in dicom_header or len(dicom_header.ImagePositionPatient) < 3:
return False
# for all others if there is image position patient we assume it is ok
if Tag(0x0020, 0x0037) not in dicom_header:
return False
return True
except (KeyError, AttributeError):
return False
def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for philips images.
As input philips images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the output nifti
:param dicom_input: directory with dicom files for 1 scan
"""
assert common.is_philips(dicom_input)
if common.is_multiframe_dicom(dicom_input):
_assert_explicit_vr(dicom_input)
logger.info('Found multiframe dicom')
if _is_multiframe_4d(dicom_input):
logger.info('Found sequence type: MULTIFRAME 4D')
return _multiframe_to_nifti(dicom_input, output_file)
if _is_multiframe_anatomical(dicom_input):
logger.info('Found sequence type: MULTIFRAME ANATOMICAL')
return _multiframe_to_nifti(dicom_input, output_file)
else:
logger.info('Found singleframe dicom')
grouped_dicoms = _get_grouped_dicoms(dicom_input)
if _is_singleframe_4d(dicom_input):
logger.info('Found sequence type: SINGLEFRAME 4D')
return _singleframe_to_nifti(grouped_dicoms, output_file)
logger.info('Assuming anatomical data')
return convert_generic.dicom_to_nifti(dicom_input, output_file)
def test_is_multiframe_dicom(self):
assert common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_ENHANCED_DTI))
assert not common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_DTI))
assert common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_ENHANCED_ANATOMICAL))
assert not common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_ANATOMICAL))
assert common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_ENHANCED_FMRI))
assert not common.is_multiframe_dicom(read_dicom_directory(test_data.PHILIPS_FMRI))
def are_imaging_dicoms(dicom_input):
"""
This function will check the dicom headers to see which type of series it is
Possibilities are fMRI, DTI, Anatomical (if no clear type is found anatomical is used)
:param dicom_input: directory with dicom files or a list of dicom objects
"""
# if it is philips and multiframe dicom then we assume it is ok
if common.is_philips(dicom_input):
if common.is_multiframe_dicom(dicom_input):
return True
# for all others if there is image position patient we assume it is ok
header = dicom_input[0]
return Tag(0x0020, 0x0037) in header
def are_imaging_dicoms(dicom_input):
"""
This function will check the dicom headers to see which type of series it is
Possibilities are fMRI, DTI, Anatomical (if no clear type is found anatomical is used)
:param dicom_input: directory with dicom files or a list of dicom objects
"""
# if it is philips and multiframe dicom then we assume it is ok
if common.is_philips(dicom_input):
if common.is_multiframe_dicom(dicom_input):
return True
# for all others if there is image position patient we assume it is ok
header = dicom_input[0]
return Tag(0x0020, 0x0037) in header
def _is_multiframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe 4D dataset
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
number_of_stack_slices = common.get_ss_value(header[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(header.NumberOfFrames) / number_of_stack_slices)
if number_of_stacks <= 1:
return False
return True
def _is_multiframe_4d(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe 4D dataset
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
number_of_stack_slices = common.get_ss_value(header[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(header.NumberOfFrames) / number_of_stack_slices)
if number_of_stacks <= 1:
return False
return True
def dicom_to_nifti(dicom_input, output_file=None):
"""
This is the main dicom to nifti conversion fuction for philips images.
As input philips images are required. It will then determine the type of images and do the correct conversion
Examples: See unit test
:param output_file: file path to the output nifti
:param dicom_input: directory with dicom files for 1 scan
"""
assert common.is_philips(dicom_input)
# remove duplicate slices based on position and data
dicom_input = convert_generic.remove_duplicate_slices(dicom_input)
# remove localizers based on image type
dicom_input = convert_generic.remove_localizers_by_imagetype(dicom_input)
# remove_localizers based on image orientation (only valid if slicecount is validated)
dicom_input = convert_generic.remove_localizers_by_orientation(dicom_input)
# if no dicoms remain raise exception
if not dicom_input:
raise ConversionValidationError('TOO_FEW_SLICES/LOCALIZER')
if common.is_multiframe_dicom(dicom_input):
_assert_explicit_vr(dicom_input)
logger.info('Found multiframe dicom')
if _is_multiframe_4d(dicom_input):
logger.info('Found sequence type: MULTIFRAME 4D')
return _multiframe_to_nifti(dicom_input, output_file)
if _is_multiframe_anatomical(dicom_input):
logger.info('Found sequence type: MULTIFRAME ANATOMICAL')
return _multiframe_to_nifti(dicom_input, output_file)
else:
logger.info('Found singleframe dicom')
grouped_dicoms = _get_grouped_dicoms(dicom_input)
if _is_singleframe_4d(dicom_input):
logger.info('Found sequence type: SINGLEFRAME 4D')
return _singleframe_to_nifti(grouped_dicoms, output_file)
logger.info('Assuming anatomical data')
return convert_generic.dicom_to_nifti(dicom_input, output_file)
def test_is_multiframe_dicom(self):
assert common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_ENHANCED_DTI))
assert not common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_DTI))
assert common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_ENHANCED_ANATOMICAL))
assert not common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_ANATOMICAL))
assert common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_ENHANCED_FMRI))
assert not common.is_multiframe_dicom(
read_dicom_directory(test_data.PHILIPS_FMRI))
def _is_multiframe_anatomical(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe anatomical dataset
NOTE: Only the first slice will be checked so you can only provide an already sorted dicom directory
(containing one series)
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
number_of_stack_slices = common.get_ss_value(header[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(header.NumberOfFrames) / number_of_stack_slices)
if number_of_stacks > 1:
return False
return True
def _is_multiframe_anatomical(dicom_input):
"""
Use this function to detect if a dicom series is a philips multiframe anatomical dataset
NOTE: Only the first slice will be checked so you can only provide an already sorted dicom directory
(containing one series)
"""
# check if it is multi frame dicom
if not common.is_multiframe_dicom(dicom_input):
return False
header = dicom_input[0]
# check if there are multiple stacks
number_of_stack_slices = common.get_ss_value(header[Tag(0x2001, 0x105f)][0][Tag(0x2001, 0x102d)])
number_of_stacks = int(int(header.NumberOfFrames) / number_of_stack_slices)
if number_of_stacks > 1:
return False
return True
