ihnorton / dcm2niix Goto Github PK

dcm2niix is a designed to convert neuroimaging data from the DICOM format to the NIfTI format. This web page hosts the developmental source code - a compiled version for Linux, MacOS, and Windows of the most recent stable release is included with MRIcroGL. A full manual for this software is available in the form of a NITRC wiki.

This software is open source. The bulk of the code is covered by the BSD license. Some units are either public domain (nifti*.*, miniz.c) or use the MIT license (ujpeg.cpp). See the license.txt file for more details.

This software should run on macOS, Linux and Windows typically without requiring any other software. However, if you use dcm2niix to create gz-compressed images it will be faster if you have pigz installed. You can get a version of both dcm2niix and pigz compiled for your operating system by downloading MRIcroGL.

DICOM provides many ways to store/compress image data, known as transfer syntaxes. The COMPILE.md file describes details on how to enable different options to provide support for more formats.

• The base code includes support for raw, run-length encoded, and classic JPEG lossless decoding.
• Lossy JPEG is handled by the included NanoJPEG. This support is modular: you can compile for libjpeg-turbo or disable it altogether.
• JPEG-LS lossless support is optional, and can be provided by using CharLS.
• JPEG2000 lossy and lossless support is optional, and can be provided using OpenJPEG or Jasper.
• GZ compression (e.g. creating .nii.gz images) is optional, and can be provided using either the included miniz or the popular zlib. Of particular note, the Cloudflare zlib exploits modern hardware (available since 2008) for very rapid compression. Alternatively, you can compile dcm2niix without a gzip compressor. Regardless of how you compile dcm2niix, it can use the external program pigz for parallel compression.

See the VERSIONS.md file for details on releases.

Command line usage is described in the NITRC wiki. The minimal command line call would be dcm2niix /path/to/dicom/folder. However, you may want to invoke additional options, for example the call dcm2niix -z y -f %p_%t_%s -o /path/ouput /path/to/dicom/folder will save data as gzip compressed, with the filename based on the protocol name (%p) acquisition time (%t) and DICOM series number (%s), with all files saved to the folder "output". For more help see help: dcm2niix -h.

See the BATCH.md file for instructions on using the batch processing version.

cmake and pkg-config (optional) can be installed as follows:

Ubuntu: sudo apt-get install cmake pkg-config

MacOS: brew install cmake pkg-config

Basic build:

mkdir build && cd build
cmake ..
make

dcm2niix will be created in the bin subfolder. To install on the system run make install instead of make - this will copy the executable to your path so you do not have to provide the full path to the executable.

In rare case if cmake fails with the message like "Generator: execution of make failed", it could be fixed by sudo ln -s `which make` /usr/bin/gmake.

Advanced build:

As noted in the Image Conversion and Compression Support section, the software provides many optional modules with enhanced features. A common choice might be to include support for JPEG2000, JPEG-LS (this option requires a c++14 compiler), as well as using the high performance Cloudflare zlib library (this option requires a CPU built after 2008). To build with these options simply request them when configuring cmake:

mkdir build && cd build
cmake -DZLIB_IMPLEMENTATION=Cloudflare -DUSE_JPEGLS=ON -DUSE_OPENJPEG=ON ..
make

optional batch processing version:

The batch processing binary dcm2niibatch is optional. To build dcm2niibatch as well change the cmake command to cmake -DBATCH_VERSION=ON ... This requires a compiler that supports c++11.

If you have any problems with the cmake build script described above or want to customize the software see the COMPILE.md file for details on manual compilation.

• Valerio Luccio's dinifti is focused on conversion of Siemens data.
• dcm2nii is the predecessor of dcm2niix. It is deprecated for modern images, but does handle image formats that predate DICOM (proprietary Elscint, GE and Siemens formats).
• DWIConvert converts DICOM images to NRRD and NIfTI formats.
• Xiangrui Li's dicm2nii is written in Matlab. The Matlab language makes this very scriptable.
• dicom2nifti uses the scriptable Python wrapper utilizes the high performance GDCMCONV executables.
• MRtrix mrconvert is a useful general purpose image converter and handles DTI data well. It is an outstanding tool for modern Philips enhanced images.
• Jolinda Smith's mcverter has great support for various vendors.
• mri_convert is part of the popular FreeSurfer package. In my limited experience this tool works well for GE and Siemens data, but fails with Philips 4D datasets.
• SPM12 is one of the most popular tools in the field. It includes DICOM to NIfTI conversion. Being based on Matlab it is easy to script.
• R2A_GUI is a Matlab script that converts Philips PAR/REC images to NIfTI.

• bidsify is a Python project that uses dcm2niix to convert DICOM and Philips PAR/REC images to the BIDS standard.
• bidskit uses dcm2niix to create BIDS datasets.
• boutiques-dcm2niix is a dockerfile for installing and validating dcm2niix.
• DAC2BIDS uses dcm2niibatch to create BIDS datasets.
• Dcm2Bids uses dcm2niix to create BIDS datasets.
• dcm2niir R wrapper for dcm2niix/dcm2nii.
• dcm2niix_afni is a version of dcm2niix included with the AFNI distribution.
• dcm2niiXL is a shell script and tuned compilation of dcm2niix designed for accelerated conversion of extra large datasets.
• dicom2nifti_batch is a Matlab script for automating dcm2niix.
• divest R interface to dcm2niix.
• fsleyes is a powerful Python-based image viewer. It uses dcm2niix to handle DICOM files through its fslpy libraries.
• heudiconv can use dcm2niix to create BIDS datasets.
• MRIcroGL is available for MacOS, Linux and Windows and provides a graphical interface for dcm2niix. You can get compiled copies from the MRIcroGL NITRC web site.
• neuro_docker includes dcm2niix as part of a single, static Dockerfile.
• NeuroDebian provides up-to-date version of dcm2niix for Debian-based systems.
• neurodocker generates custom Dockerfiles given specific versions of neuroimaging software.
• nipype can use dcm2niix to convert images.
• pydcm2niix is a Python module for working with dcm2niix.
• sci-tran dcm2niix Flywheel Gear (docker).