Dicom Series From Array

Overview

This example illustrates how to write a DICOM series from a numeric array and create appropriate meta-data so it can be read my DICOM viewers.

Generating an array is done using a simple random number generator for this case but can come from other sources.

Writing the 3D image as a DICOM series is done by configuring the meta-data dictionary for each of the slices and then writing it in DICOM format. In our case we generate all of the meta-data to indicate that this series is derived. Note that we write the intensity values as is and thus do not set the rescale slope (0028|1053), rescale intercept (0028|1052) meta-data dictionary values.

See also Read Image Meta-Data Dictionary and Print, Dicom Series Reader.

Code

Python

from __future__ import print_function

import SimpleITK as sitk

import sys, time, os
import numpy as np

if len( sys.argv ) < 2:
    print( "Usage: python " + __file__ + "<output_directory>" )
    sys.exit ( 1 )


# Create a new series from a numpy array
new_arr = np.random.uniform(-10, 10, size = (3,4,5)).astype(np.int16)
new_img = sitk.GetImageFromArray(new_arr)
new_img.SetSpacing([2.5,3.5,4.5])


# Write the 3D image as a series
# IMPORTANT: There are many DICOM tags that need to be updated when you modify an
#            original image. This is a delicate opration and requires knowlege of
#            the DICOM standard. This example only modifies some. For a more complete
#            list of tags that need to be modified see:
#                           http://gdcm.sourceforge.net/wiki/index.php/Writing_DICOM
#            If it is critical for your work to generate valid DICOM files,
#            It is recommended to use David Clunie's Dicom3tools to validate the files 
#                           (http://www.dclunie.com/dicom3tools.html).

writer = sitk.ImageFileWriter()
# Use the study/series/frame of reference information given in the meta-data
# dictionary and not the automatically generated information from the file IO
writer.KeepOriginalImageUIDOn()



modification_time = time.strftime("%H%M%S")
modification_date = time.strftime("%Y%m%d")

# Copy some of the tags and add the relevant tags indicating the change.
# For the series instance UID (0020|000e), each of the components is a number, cannot start
# with zero, and separated by a '.' We create a unique series ID using the date and time.
# tags of interest:
direction = new_img.GetDirection()
series_tag_values = [("0008|0031",modification_time), # Series Time
                  ("0008|0021",modification_date), # Series Date
                  ("0008|0008","DERIVED\\SECONDARY"), # Image Type
                  ("0020|000e", "1.2.826.0.1.3680043.2.1125."+modification_date+".1"+modification_time), # Series Instance UID
                  ("0020|0037", '\\'.join(map(str, (direction[0], direction[3], direction[6],# Image Orientation (Patient)
                                                    direction[1],direction[4],direction[7])))),
                  ("0008|103e", "Created-SimpleITK")] # Series Description

for i in range(new_img.GetDepth()):
    image_slice = new_img[:,:,i]
    # Tags shared by the series.
    for tag, value in series_tag_values:
        image_slice.SetMetaData(tag, value)
    # Slice specific tags.
    image_slice.SetMetaData("0008|0012", time.strftime("%Y%m%d")) # Instance Creation Date
    image_slice.SetMetaData("0008|0013", time.strftime("%H%M%S")) # Instance Creation Time
    # Setting the type to CT preserves the slice location.
    image_slice.SetMetaData("0008|0060", "CT")  # set the type to CT so the thickness is carried over
    
    # (0020, 0032) image position patient determines the 3D spacing between slices.
    image_slice.SetMetaData("0020|0032", '\\'.join(map(str,new_img.TransformIndexToPhysicalPoint((0,0,i))))) # Image Position (Patient)
    image_slice.SetMetaData("0020,0013", str(i)) # Instance Number

    # Write to the output directory and add the extension dcm, to force writing in DICOM format.
    writer.SetFileName(os.path.join(sys.argv[1],str(i)+'.dcm'))
    writer.Execute(image_slice)
    
# Re-read the series
# Read the original series. First obtain the series file names using the
# image series reader.
data_directory = sys.argv[1]
series_IDs = sitk.ImageSeriesReader.GetGDCMSeriesIDs(data_directory)
if not series_IDs:
    print("ERROR: given directory \""+data_directory+"\" does not contain a DICOM series.")
    sys.exit(1)
series_file_names = sitk.ImageSeriesReader.GetGDCMSeriesFileNames(data_directory, series_IDs[0])

series_reader = sitk.ImageSeriesReader()
series_reader.SetFileNames(series_file_names)

# Configure the reader to load all of the DICOM tags (public+private):
# By default tags are not loaded (saves time).
# By default if tags are loaded, the private tags are not loaded.
# We explicitly configure the reader to load tags, including the
# private ones.
series_reader.LoadPrivateTagsOn()
image3D = series_reader.Execute()
print(image3D.GetSpacing(),'vs',new_img.GetSpacing())
sys.exit( 0 )