https://github.com/lamyj/dicomifier

A medical image converter
https://github.com/lamyj/dicomifier

bruker conversion dicom medical-imaging mri nifti

Last synced: 3 months ago
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A medical image converter

• Host: GitHub
• URL: https://github.com/lamyj/dicomifier
• Owner: lamyj
• License: other
• Created: 2015-03-19T16:28:40.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2024-05-12T13:21:20.000Z (6 months ago)
• Last Synced: 2024-06-24T01:02:58.337Z (4 months ago)
• Topics: bruker, conversion, dicom, medical-imaging, mri, nifti
• Language: Python
• Homepage: https://dicomifier.readthedocs.io/
• Size: 35.4 MB
• Stars: 29
• Watchers: 9
• Forks: 7
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-dicom - Dicomifier - A set of tools to convert Bruker data to DICOM files, and DICOM files to NIfTI. (Libraries / Python)

README

        
# Dicomifier

[![Conda Version](https://img.shields.io/conda/v/conda-forge/dicomifier.svg)](https://anaconda.org/conda-forge/dicomifier) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4459178.svg)](https://doi.org/10.5281/zenodo.4459178)

Dicomifier is a set of tools to convert Bruker data to DICOM files, and DICOM files to NIfTI. It retains meta-data (e.g. MR parameters such as echo time or subject parameters such as weight or height) throughout the conversion process, and aligns the meta-data from Bruker on the DICOM dictionary for unified processing pipelines.

Getting started? Have a look at the [installation instructions and basic usage](https://dicomifier.readthedocs.io/en/latest/getting_started.html). If you're in a hurry and have [Miniconda](https://conda.io/en/master/miniconda.html) or [Anaconda](https://anaconda.org) installed, `conda install -c conda-forge dicomifier` should get you going.

Need to know more about your options and arguments? The [commands reference](https://dicomifier.readthedocs.io/en/latest/commands/index.html) is for you.

Want to find out how it works? The [API reference](https://dicomifier.readthedocs.io/en/latest/api_reference/index.html) is a good place to start, followed by the [source code](https://github.com/lamyj/dicomifier).

Interested in how the vendors store diffusion MRI meta-data? There is a [section](https://dicomifier.readthedocs.io/en/latest/diffusion/index.html) just for you.