Source code for dicom2nifti.convert_generic

# -*- coding: utf-8 -*-

@author: abrys

from __future__ import print_function
import dicom2nifti.patch_pydicom_encodings


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:'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 ( 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