nii2dcm 0.1.0

nii2dcm: NIfTI to DICOM creation with Python

nii2dcm

NIfTI to DICOM file creation with Python
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Overview

DICOM is the international standard used to store and display medical images in clinical institutions. It is a vast and complicated standard. The NIfTI file format is widely used within medical imaging research because it is a comparatively simple data format, generally stripped of identifiable patient data and with far fewer metadata fields.

Researchers often convert DICOM files to NIfTI files using tools such as dcm2niix. However, the reverse process is much harder.

nii2dcm is designed to convert a NIfTI file (.nii/.nii.gz) into a single-frame DICOM Series in one line, e.g.:

nii2dcm nifti-file.nii.gz dicom-output-directory

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Getting Started

To install and run nii2dcm locally follow these steps.

Prerequisites

• Python (tested with v3.9)

Installation

1. Setup a Python virtual environment

   python -m venv nii2dcm-venv
   source nii2dcm-venv/bin/activate
   

2. Clone this repo

   git clone https://github.com/tomaroberts/nii2dcm.git
   

3. Install the Python requirements

   pip install -r requirements.txt
   

4. Install nii2dcm

   cd nii2dcm
   python setup.py install
   

5. Verify installation by displaying nii2dcm help information

   nii2dcm -h
   

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Usage

nii2dcm is designed to be pointed at a single .nii or .nii.gz and generate a single-frame DICOM dataset:

nii2dcm nifti-file.nii.gz dicom-output-directory

Currently, nii2dcm provides three Python classes corresponding to different DICOM types. These are:

• Dicom – generic DICOM class
• DicomMRI – MRI DICOM class
• DicomMRISVR – 3D SVR MRI DICOM class

The created DICOM type can be specified with the -d or --dicom-type flag. For example, the following will output a 3D MRI SVR DICOM dataset

nii2dcm SVR-output.nii.gz path/to/output/dir/ -d SVR

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Roadmap

This project is in its infancy! Expect :bug::ant::beetle:

There are many things I would like to test and implement. Raise an Issue if you have ideas or suggestions.

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Acknowledgments

• SVRTK @ KCL - the original motivation for this project
• Chris Rorden for dcm2niix - inspiration for the reverse process
• NHS Topol Digital Fellowships scheme - for the protected time to work on this

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Disclaimer

The Software has been developed for research purposes only and is not a clinical tool.

Licence

BSD 3-Clause License

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Copyright (c) 2021-2022 Tom Roberts. All rights reserved.

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