# -*- coding: utf-8 -*-
"""
dicom2nifti
@author: abrys
"""
from __future__ import print_function
import dicom2nifti.patch_pydicom_encodings
dicom2nifti.patch_pydicom_encodings.apply()
import logging
import nibabel
import numpy
from pydicom.tag import Tag
import six
import dicom2nifti.common as common
import dicom2nifti.settings as settings
from dicom2nifti.exceptions import ConversionError
logger = logging.getLogger(__name__)
[docs]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 _remove_duplicate_slices(dicoms):
"""
Search dicoms for localizers and delete them
"""
# Loop overall files and build dict
dicoms_dict = {}
filtered_dicoms = []
for dicom_ in dicoms:
if tuple(dicom_.ImagePositionPatient) not in dicoms_dict:
dicoms_dict[tuple(dicom_.ImagePositionPatient)] = dicom_
filtered_dicoms.append(dicom_)
else:
if numpy.array_equal(dicom_.pixel_array,
dicoms_dict[tuple(dicom_.ImagePositionPatient)].pixel_array):
logger.warning('Removing duplicate slice from series')
else:
filtered_dicoms.append(dicom_)
return filtered_dicoms
def _remove_localizers_by_imagetype(dicoms):
"""
Search dicoms for localizers and delete them
"""
# Loop overall files and build dict
filtered_dicoms = []
for dicom_ in dicoms:
if 'ImageType' in dicom_ and 'LOCALIZER' in dicom_.ImageType:
continue
# 'Projection Image' are Localizers for CT only see MSMET-234
if 'CT' in dicom_.Modality and 'ImageType' in dicom_ and 'PROJECTION IMAGE' in dicom_.ImageType:
continue
filtered_dicoms.append(dicom_)
return filtered_dicoms
def _remove_localizers_by_orientation(dicoms):
"""
Removing localizers based on the orientation.
This is needed as in some cases with ct data there are some localizer/projection type images that cannot
be distiguished by the dicom headers. This is why we kick out all orientations that do not have more than 4 files
4 is the limit anyway for converting to nifti on our case
"""
orientations = []
sorted_dicoms = {}
# Loop overall files and build dict
for dicom_header in dicoms:
# Create affine matrix (http://nipy.sourceforge.net/nibabel/dicom/dicom_orientation.html#dicom-slice-affine)
image_orient1 = numpy.array(dicom_header.ImageOrientationPatient)[0:3]
image_orient2 = numpy.array(dicom_header.ImageOrientationPatient)[3:6]
image_orient_combined = (image_orient1.tolist(), image_orient2.tolist())
found_orientation = False
for orientation in orientations:
if numpy.allclose(image_orient_combined[0], numpy.array(orientation[0]), rtol=0.001, atol=0.001) \
and numpy.allclose(image_orient_combined[1], numpy.array(orientation[1]), rtol=0.001,
atol=0.001):
sorted_dicoms[str(orientation)].append(dicom_header)
found_orientation = True
break
if not found_orientation:
orientations.append(image_orient_combined)
sorted_dicoms[str(image_orient_combined)] = [dicom_header]
# if there are multiple possible orientations delete orientations where there are less than 4 files
# we don't convert anything less that that anyway
if len(sorted_dicoms) > 1:
filtered_dicoms = []
for orientation in sorted_dicoms.keys():
if len(sorted_dicoms[orientation]) >= 4:
filtered_dicoms.extend(sorted_dicoms[orientation])
return filtered_dicoms
else:
return six.next(six.itervalues(sorted_dicoms))