def dicom_to_nifti(dicom_input, output_file):
"""
This function will convert an anatomical dicom series to a nifti
Examples: See unit test
:param output_file: filepath to the output nifti
:param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms
"""
if len(dicom_input) <= 0:
raise ConversionError('NO_DICOM_FILES_FOUND')
# remove duplicate slices based on position and data
dicom_input = _remove_duplicate_slices(dicom_input)
# remove localizers based on image type
dicom_input = _remove_localizers_by_imagetype(dicom_input)
if settings.validate_slicecount:
# remove_localizers based on image orientation (only valid if slicecount is validated)
dicom_input = _remove_localizers_by_orientation(dicom_input)
# validate all the dicom files for correct orientations
common.validate_slicecount(dicom_input)
if settings.validate_orientation:
# validate that all slices have the same orientation
common.validate_orientation(dicom_input)
if settings.validate_orthogonal:
# validate that we have an orthogonal image (to detect gantry tilting etc)
common.validate_orthogonal(dicom_input)
# sort the dicoms
dicom_input = common.sort_dicoms(dicom_input)
if settings.validate_sliceincrement:
# validate that all slices have a consistent slice increment
common.validate_sliceincrement(dicom_input)
# Get data; originally z,y,x, transposed to x,y,z
data = common.get_volume_pixeldata(dicom_input)
affine = common.create_affine(dicom_input)
# Convert to nifti
nii_image = nibabel.Nifti1Image(data, affine)
# Set TR and TE if available
if Tag(0x0018, 0x0081) in dicom_input[0] and Tag(0x0018,
0x0081) in dicom_input[0]:
common.set_tr_te(nii_image, float(dicom_input[0].RepetitionTime),
float(dicom_input[0].EchoTime))
# Save to disk
if output_file is not None:
logger.info('Saving nifti to disk %s' % output_file)
nii_image.to_filename(output_file)
return {'NII_FILE': output_file, 'NII': nii_image}
def _convert_slice_incement_inconsistencies(dicom_input):
"""
If there is slice increment inconsistency detected, for the moment CT images, then split the volumes into subvolumes based on the slice increment and process each volume separately using a space constructed based on the highest resolution increment
"""
# Estimate the "first" slice increment based on the 2 first slices
increment = numpy.array(dicom_input[0].ImagePositionPatient) - numpy.array(
dicom_input[1].ImagePositionPatient)
# Create as many volumes as many changes in slice increment. NB Increments might be repeated in different volumes
max_slice_increment = 0
slice_incement_groups = []
current_group = [dicom_input[0], dicom_input[1]]
previous_image_position = numpy.array(dicom_input[1].ImagePositionPatient)
for dicom in dicom_input[2:]:
current_image_position = numpy.array(dicom.ImagePositionPatient)
current_increment = previous_image_position - current_image_position
max_slice_increment = max(max_slice_increment,
numpy.linalg.norm(current_increment))
if numpy.allclose(increment, current_increment, rtol=0.05, atol=0.1):
current_group.append(dicom)
if not numpy.allclose(
increment, current_increment, rtol=0.05, atol=0.1):
slice_incement_groups.append(current_group)
current_group = [current_group[-1], dicom]
increment = current_increment
previous_image_position = current_image_position
slice_incement_groups.append(current_group)
# Create nibabel objects for each volume based on the corresponding headers
slice_incement_niftis = []
slice_increments = []
voxel_sizes = {}
for dicom_slices in slice_incement_groups:
data = common.get_volume_pixeldata(dicom_slices)
affine, _ = common.create_affine(dicom_slices)
current_volume = nibabel.Nifti1Image(data.squeeze(), affine)
slice_increment = numpy.linalg.norm(current_volume.header.get_zooms())
voxel_sizes['%.5f' %
slice_increment] = current_volume.header.get_zooms()
slice_increments.extend([slice_increment] * (len(dicom_slices) - 1))
slice_incement_niftis.append(current_volume)
tenth_percentile_incement = numpy.percentile(slice_increments, 15)
most_used_increment = min(slice_increments,
key=lambda x: abs(x - tenth_percentile_incement))
voxel_size = voxel_sizes['%.5f' % most_used_increment]
nifti_volume = resample.resample_nifti_images(slice_incement_niftis,
voxel_size=voxel_size)
return nifti_volume, max_slice_increment
def _convert_slice_incement_inconsistencies(dicom_input):
"""
If there is slice increment inconsistency detected, for the moment CT images, then split the volumes into subvolumes based on the slice increment and process each volume separately using a space constructed based on the highest resolution increment
"""
# Estimate the "first" slice increment based on the 2 first slices
increment = numpy.array(dicom_input[0].ImagePositionPatient) - numpy.array(dicom_input[1].ImagePositionPatient)
# Create as many volumes as many changes in slice increment. NB Increments might be repeated in different volumes
max_slice_increment = 0
slice_incement_groups = []
current_group = [dicom_input[0], dicom_input[1]]
previous_image_position = numpy.array(dicom_input[1].ImagePositionPatient)
for dicom in dicom_input[2:]:
current_image_position = numpy.array(dicom.ImagePositionPatient)
current_increment = previous_image_position - current_image_position
max_slice_increment = max(max_slice_increment, numpy.linalg.norm(current_increment))
if numpy.allclose(increment, current_increment, rtol=0.05, atol=0.1):
current_group.append(dicom)
if not numpy.allclose(increment, current_increment, rtol=0.05, atol=0.1):
slice_incement_groups.append(current_group)
current_group = [current_group[-1], dicom]
increment = current_increment
previous_image_position = current_image_position
slice_incement_groups.append(current_group)
# Create nibabel objects for each volume based on the corresponding headers
slice_incement_niftis = []
for dicom_slices in slice_incement_groups:
data = common.get_volume_pixeldata(dicom_slices)
affine, _ = common.create_affine(dicom_slices)
slice_incement_niftis.append(nibabel.Nifti1Image(data, affine))
nifti_volume = resample.resample_nifti_images(slice_incement_niftis)
return nifti_volume, max_slice_increment
def _timepoint_to_block(timepoint_dicoms):
"""
Convert slices to a block of data by reading the headers and appending
"""
# similar way of getting the block to anatomical however here we are creating the dicom series our selves
return common.get_volume_pixeldata(timepoint_dicoms)
def _stack_to_block(timepoint_dicoms):
"""
Convert a mosaic slice to a block of data by reading the headers, splitting the mosaic and appending
"""
return common.get_volume_pixeldata(timepoint_dicoms)
def _timepoint_to_block(timepoint_dicoms):
"""
Convert slices to a block of data by reading the headers and appending
"""
# similar way of getting the block to anatomical however here we are creating the dicom series our selves
return common.get_volume_pixeldata(timepoint_dicoms)
def _stack_to_block(timepoint_dicoms):
"""
Convert a mosaic slice to a block of data by reading the headers, splitting the mosaic and appending
"""
return common.get_volume_pixeldata(timepoint_dicoms)
def dicom_to_nifti(dicom_input, output_file):
"""
This function will convert an anatomical dicom series to a nifti
Examples: See unit test
:param output_file: filepath to the output nifti
:param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms
"""
if len(dicom_input) <= 0:
raise ConversionError('NO_DICOM_FILES_FOUND')
# remove duplicate slices based on position and data
dicom_input = remove_duplicate_slices(dicom_input)
# remove localizers based on image type
dicom_input = remove_localizers_by_imagetype(dicom_input)
# if no dicoms remain we should raise exception
if len(dicom_input) < 1:
raise ConversionValidationError('TOO_FEW_SLICES/LOCALIZER')
if settings.validate_slicecount:
common.validate_slicecount(dicom_input)
# remove_localizers based on image orientation (only valid if slicecount is validated)
dicom_input = remove_localizers_by_orientation(dicom_input)
# validate all the dicom files for correct orientations
common.validate_slicecount(dicom_input)
if settings.validate_orientation:
# validate that all slices have the same orientation
common.validate_orientation(dicom_input)
if settings.validate_orthogonal:
# validate that we have an orthogonal image (to detect gantry tilting etc)
common.validate_orthogonal(dicom_input)
# sort the dicoms
dicom_input = common.sort_dicoms(dicom_input)
# validate slice increment inconsistent
slice_increment_inconsistent = False
if settings.validate_slice_increment:
# validate that all slices have a consistent slice increment
common.validate_slice_increment(dicom_input)
elif common.is_slice_increment_inconsistent(dicom_input):
slice_increment_inconsistent = True
if settings.validate_instance_number:
# validate that all slices have a consistent instance_number
common.validate_instance_number(dicom_input)
# if inconsistent increment and we allow resampling then do the resampling based conversion to maintain the correct geometric shape
if slice_increment_inconsistent and settings.resample:
nii_image, max_slice_increment = _convert_slice_incement_inconsistencies(dicom_input)
# do the normal conversion
else:
# Get data; originally z,y,x, transposed to x,y,z
data = common.get_volume_pixeldata(dicom_input)
affine, max_slice_increment = common.create_affine(dicom_input)
# Convert to nifti
nii_image = nibabel.Nifti1Image(data.squeeze(), affine)
# Set TR and TE if available
if Tag(0x0018, 0x0080) in dicom_input[0] and Tag(0x0018, 0x0081) in dicom_input[0]:
common.set_tr_te(nii_image, float(dicom_input[0].RepetitionTime), float(dicom_input[0].EchoTime))
# Save to disk
if output_file is not None:
logger.info('Saving nifti to disk %s' % output_file)
nii_image.header.set_slope_inter(1, 0)
nii_image.header.set_xyzt_units(2) # set units for xyz (leave t as unknown)
nii_image.to_filename(output_file)
return {'NII_FILE': output_file,
'NII': nii_image,
'MAX_SLICE_INCREMENT': max_slice_increment}
def dicom_to_nifti(dicom_input, output_file):
"""
This function will convert an anatomical dicom series to a nifti
Examples: See unit test
:param output_file: filepath to the output nifti
:param dicom_input: directory with the dicom files for a single scan, or list of read in dicoms
"""
if len(dicom_input) <= 0:
raise ConversionError('NO_DICOM_FILES_FOUND')
# remove duplicate slices based on position and data
dicom_input = _remove_duplicate_slices(dicom_input)
# remove localizers based on image type
dicom_input = _remove_localizers_by_imagetype(dicom_input)
if settings.validate_slicecount:
# remove_localizers based on image orientation (only valid if slicecount is validated)
dicom_input = _remove_localizers_by_orientation(dicom_input)
# validate all the dicom files for correct orientations
common.validate_slicecount(dicom_input)
if settings.validate_orientation:
# validate that all slices have the same orientation
common.validate_orientation(dicom_input)
if settings.validate_orthogonal:
# validate that we have an orthogonal image (to detect gantry tilting etc)
common.validate_orthogonal(dicom_input)
# sort the dicoms
dicom_input = common.sort_dicoms(dicom_input)
# validate slice increment inconsistent
slice_increment_inconsistent = False
if settings.validate_slice_increment:
# validate that all slices have a consistent slice increment
common.validate_slice_increment(dicom_input)
elif common.is_slice_increment_inconsistent(dicom_input):
slice_increment_inconsistent = True
# if inconsistent increment and we allow resampling then do the resampling based conversion to maintain the correct geometric shape
if slice_increment_inconsistent and settings.resample:
nii_image, max_slice_increment = _convert_slice_incement_inconsistencies(dicom_input)
# do the normal conversion
else:
# Get data; originally z,y,x, transposed to x,y,z
data = common.get_volume_pixeldata(dicom_input)
affine, max_slice_increment = common.create_affine(dicom_input)
# Convert to nifti
nii_image = nibabel.Nifti1Image(data, affine)
# Set TR and TE if available
if Tag(0x0018, 0x0081) in dicom_input[0] and Tag(0x0018, 0x0081) in dicom_input[0]:
common.set_tr_te(nii_image, float(dicom_input[0].RepetitionTime), float(dicom_input[0].EchoTime))
# Save to disk
if output_file is not None:
logger.info('Saving nifti to disk %s' % output_file)
nii_image.to_filename(output_file)
return {'NII_FILE': output_file,
'NII': nii_image,
'MAX_SLICE_INCREMENT': max_slice_increment}
