Understanding DICOM image attributes to get axial/coronal/sagittal cuts

Question

I have to write a program in c# able to parse DICOM and display axial, coronal and sagittal cuts.

Seems like it\'s a lot of work, but I have to do it so !

Big fi

Answer 1

This is a python implementation to find plane of an IMA file. Refer: http://dicomiseasy.blogspot.com/2013/06/getting-oriented-using-image-plane.html

import pydicom # for gettind information out of the IMA files
import numpy as np

"""
This function takes IOP of an image and returns its plane (Sagittal, Coronal, Transverse)
"""

def file_plane(IOP):
    IOP_round = [round(x) for x in IOP]
    plane = np.cross(IOP_round[0:3], IOP_round[3:6])
    plane = [abs(x) for x in plane]
    if plane[0] == 1:
        return "Sagittal"
    elif plane[1] == 1:
        return "Coronal"
    elif plane[2] == 1:
        return "Transverse"

a=pydicom.read_file("path\to\your\DICOM\file.IMA")
IOP = a.ImageOrientationPatient
plane = file_plane(IOP)

Answer 2

Sometimes you have to build the different views. For example if you have a CT image set which are axial to the subject, then you can make your own slices for coronal and sagittal views by sampling the image set appropriately. I would start by writing code to simply display the data you have as is as a starting point. By the time that's working you'll have a deeper understanding of the issues.

Answer 3

This question is already 3 y.o., but maybe my answer will be helpful for somebody.

@user6867490 has already referenced to the beautiful article by Roni Zaharia.

The only thing that I would like to add is: you can simply get access to the Image Orientation (Patient), Tag (0020,0037) by writing

f = dicom.read_file(dicom_file_name)
f.ImageOrientationPatient

If you'll obtain:

['1', '0', '0', '0', '0', '-1'] you are dealing with Coronal plane view

['0', '1', '0', '0', '0', '-1'] you are dealing with Sagittal plane view

['1', '0', '0', '0', '1', '0'] you are dealing with Axial plane view

In my case, there was a dataset containing a few images of Sagittal and lots of Axial views (which I needed actually). So, I simply iterated through the whole dataset and did a simple logical comparison to filter out all the saggitals.

Answer 4

You are right, this is quite a big task! And you are probably not going to find someone here who can provide a step-by-step tutorial for you, However some hints:

1. The DICOM standard is awkward and needs a steep learning curve at the beginning. The DICOM Cookbook helped me a lot to become familiar with the terms and the structure of the documentation.
2. There are plenty of other toolkits around, some of them are free and ship with a very liberal license, such as DCMTK. These take away the burden to learn how the information is encoded in the various binary formats which slightly differ and provide a uniform API to access the information quite easily. I would strongly advise against writing your own DICOM implementation from scratch.
3. The DICOM attributes which define the 3D position and orientation of a slice or frame are ImagePositionPatient (0020,0032) , ImageOrientationPatient (0020,0037) and PixelSpacing (0028,0030). These can be used to order the slices correctly and calculate interpolated intersection slices.
4. Make sure to look at the Enhanced CT/MR Information object definitions. In general there are two ways to store a stack of images in DICOM: Singleframe (one file per image) and Multiframe (one file for one or more stacks of images). They differ in the way that you can obtain the information mentioned above.

HTH and good luck!