# dcm2niix

[![Build](https://github.com/rordenlab/dcm2niix/actions/workflows/build.yml/badge.svg)](https://github.com/rordenlab/dcm2niix/actions/workflows/build.yml)

## About

dcm2niix is 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](https://www.nitrc.org/projects/mricrogl/). A full manual for this software is available in the form of a [NITRC wiki](http://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage).

The DICOM format is the standard image format generated by modern medical imaging devices. However, DICOM is very [complicated](https://github.com/jonclayden/divest) and has been interpreted differently by different vendors. The NIfTI format is popular with scientists, it is very simple and explicit. However, this simplicity also imposes limitations (e.g. it demands equidistant slices). dcm2niix is also able to generate a [BIDS JSON format](https://bids-specification.readthedocs.io/en/stable/) `sidecar` which includes relevant information for brain scientists in a vendor agnostic and human readable form. 
The [Neuroimaging DICOM and NIfTI Primer](https://github.com/DataCurationNetwork/data-primers/blob/master/Neuroimaging%20DICOM%20and%20NIfTI%20Data%20Curation%20Primer/neuroimaging-dicom-and-nifti-data-curation-primer.md) provides details.

## JavaScript/WebAssembly

To view the WASM specific README, please click [here](./js/README.md). The rest of this README is for the `dcm2niix` CLI program.

## License

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.

## Dependencies

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](https://github.com/madler/pigz) installed. You can get a version of both dcm2niix and pigz compiled for your operating system by downloading [MRIcroGL](https://www.nitrc.org/projects/mricrogl/).

## Image Conversion and Compression

DICOM provides many ways to store/compress image data, known as [transfer syntaxes](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#DICOM_Transfer_Syntaxes_and_Compressed_Images). The [COMPILE.md file describes details](./COMPILE.md) 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](https://keyj.emphy.de/nanojpeg/). This support is modular: you can compile for [libjpeg-turbo](https://github.com/chris-allan/libjpeg-turbo) or disable it altogether.
 - JPEG-LS lossless support is optional, and can be provided by using [CharLS](https://github.com/team-charls/charls).
  - JPEG2000 lossy and lossless support is optional, and can be provided using [OpenJPEG](https://github.com/uclouvain/openjpeg) or [Jasper](https://www.ece.uvic.ca/~frodo/jasper/).
 - GZ compression (e.g. creating .nii.gz images) is optional, and can be provided using either the included [miniz](https://github.com/richgel999/miniz) or the popular zlib. Of particular note, the [Cloudflare zlib](https://github.com/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](https://github.com/madler/pigz) for parallel compression.
 - [Zstandard](https://github.com/facebook/zstd) compression (creating .nii.zst images via `-z s`) is optional, and can be enabled with `-DUSE_ZSTD=ON` (CMake) or `ZSTD=1 make` (Makefile). Zstandard offers a favorable compression/speed tradeoff compared to gzip.
 - Siemens physio recordings stored as DICOM (XA30/XA60 [PhysioLogging](https://www.magnetomworld.siemens-healthineers.com/clinical-corner/application-tips/physiologging) Raw Data Storage SOPs carrying a gzipped XML payload, and legacy CMRR Multi-Band VE11C PMU DICOMs carrying a raw binary blob in the Siemens CSA Non-Image Storage SOP) are extracted to BIDS-compliant `_recording-<label>_physio.tsv.gz` files with companion `.json` sidecars (cardiac/respiratory/ecg/external_trigger streams), rather than skipped as non-image. For CMRR streams that carry firmware-detected R-wave / respiratory peaks, the TSV is 3 columns — `[<signal>, trigger, <signal>_trigger]` — where column 2 is BIDS-canonical scanner volume triggers (byte-compatible with `bidsphysio`) and column 3 carries the per-stream physiological events. The EXT stream is the one exception: its peak column is named `external_trigger_peak` rather than `external_trigger_trigger`. The sibling regression set [dcm_qa_physio](https://github.com/neurolabusc/dcm_qa_physio) documents the column layout, the `VALUE=2048` trigger-sentinel encoding, and the deliberate divergence from [bidsphysio](https://github.com/cbinyu/bidsphysio) (which remains the canonical post-processor for richer cases).
 - MR spectroscopy (Siemens VB/VE/XA SVS + single-DICOM Enhanced CSI, Philips classic + Enhanced SVS, UIH SVS + single-DICOM 2D/3D MRSI) is converted to BIDS-MRS compliant complex-float NIfTI + `.json` sidecars (including `_mrsi` suffix and a 4×4 `VOI` matrix emitted when sufficient source VOI metadata is present). Multi-DICOM classic Siemens CSI/MRSI is not supported. Non-square UIH MRSI grids produce correct dim/payload alignment matching spec2nii (dcm2niix deliberately mirrors spec2nii's transposed affine/data-shape coupling for byte parity). Philips classic 2× payloads emit a paired `<stem>_mrsref.nii.gz` water-reference companion; standalone water-reference acquisitions (`wrsoff` / `no_Water_Suppression` series-naming) are relabeled `_mrsref` with `WaterSuppressed: false`. Vendor-sequence-state-specific reshapes / splits (CMRR sLASER DKD reference-scan ordering, Philips MEGA-PRESS edit ON/OFF interpretation) are intentionally NOT done in C — dcm2niix ships raw bundled NIfTIs. The sibling Python tool [`tools/mrs_post.py`](tools/mrs_post.py) (pydicom + numpy) consumes those outputs to produce spec2nii-equivalent split / reshaped files when desired. Ported from [spec2nii](https://github.com/wtclarke/spec2nii) (BSD-3-Clause, William Clarke, U. Oxford). MRS regression / validation corpus lives in the sibling [dcm_qa_mrs](https://github.com/neurolabusc/dcm_qa_mrs) repo — current parity: 23/40 PASS bare, 30/40 PASS via `--with-mrs-post`, 10 SKIP bounded by spec2nii reference availability.

## ReproIn one-pass naming (`-f %H`)

dcm2niix can emit ReproIn/heudiconv-style BIDS filenames in a single pass via the `-f %H` format specifier, with the optional `-bi` (subject), `-bv` (session), and `-br` (project subdirectory) flags. A companion script `tools/reproinx.py` handles cross-series concerns (fmap pairing, `_scans.tsv`, `B0FieldIdentifier`/`B0FieldSource`, session backfill, root scaffolding, plus cross-series reclassification such as short reverse-PE EPIs to `fmap/_epi` and rescue of session-first/suffix-last protocols that the one-pass parser cannot name) and accepts `--anonymize`, `--strict`, `--keep-derivatives`, `--no-convert`, `--min-volumes`/`-N`, and `--shift-dates` (BIDS-recommended date de-identification: per-subject shift to 1925 preserving relative timing) flags. The anonymisation flag `-ba` has three modes: `y` (default; strip dates and patient PII), `n` (keep both), `o` (omit patient PII only — keep acquisition timestamps so reproinx's `_scans.tsv` and fmap closest-time pairing still work). See [REPROIN.md](./REPROIN.md) for the full grammar, defaults, privacy considerations, and known limitations.

## Versions

[See releases](https://github.com/rordenlab/dcm2niix/releases) for recent release notes. [See the VERSIONS.md file for details on earlier releases](./VERSIONS.md).

## Contribute

dcm2niix is developed by the community for the community and everybody can become a part of the [community](./CONTRIBUTE.md). Pull requests should target the `development` branch — `master` is reserved for tagged stable releases.

## Running

Command line usage is described in the [NITRC wiki](https://www.nitrc.org/plugins/mwiki/index.php/dcm2nii:MainPage#General_Usage). 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/output /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](./BATCH.md).

## Install

There are a couple ways to install dcm2niix
 - [Github Releases](https://github.com/rordenlab/dcm2niix/releases) provides the latest compiled executables. This is an excellent option for MacOS and Windows users. However, the provided Linux executable requires a recent version of Linux (e.g. Ubuntu 14.04 or later), so the provided Unix executable is not suitable for very old distributions. Specifically, it requires Glibc 2.19 (from 2014) or later. Users of older systems can compile their own copy of dcm2niix or download the compiled version included with MRIcroGL Glibc 2.12 (from 2011, see below).
 - Run the following command to get the latest release version for Linux, Macintosh or Windows:
   * `curl -fLO https://github.com/rordenlab/dcm2niix/releases/latest/download/dcm2niix_lnx.zip`
   * `curl -fLO https://github.com/rordenlab/dcm2niix/releases/latest/download/macos_dcm2niix.pkg`
   * `curl -fLO https://github.com/rordenlab/dcm2niix/releases/latest/download/dcm2niix_win.zip`
 - Latest development builds are available as artifacts from [GitHub Actions](https://github.com/rordenlab/dcm2niix/actions/workflows/build.yml) for Linux, Macintosh, and Windows.
 - [MRIcroGL (NITRC)](https://www.nitrc.org/projects/mricrogl) or [MRIcroGL (GitHub)](https://github.com/rordenlab/MRIcroGL12/releases) includes dcm2niix that can be run from the command line or from the graphical user interface (select the Import menu item). The Linux version of dcm2niix is compiled on a [holy build box](https://github.com/phusion/holy-build-box), so it should run on any Linux distribution.
 - If you have a MacOS computer with Homebrew or MacPorts you can run `brew install dcm2niix` or `sudo port install dcm2niix`, respectively.
 - If you have Conda, [`conda install -c conda-forge dcm2niix`](https://anaconda.org/conda-forge/dcm2niix) on Linux, MacOS or Windows.
 - If you have pip, `python -m pip install dcm2niix` on Linux, MacOS or Windows.
 - On Debian Linux computers you can run `sudo apt-get install dcm2niix`.


## Build from source

It is often easier to download and install a precompiled version. However, you can also build from source.

### Build command line version with cmake (Linux, MacOS, Windows)

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

Ubuntu: `sudo apt-get install cmake pkg-config`

MacOS: `brew install cmake pkg-config` or `sudo port install cmake pkgconfig`

Once these tools are available, you can compile with cmake:

```bash
git clone https://github.com/rordenlab/dcm2niix.git
cd dcm2niix
mkdir build && cd build
cmake -DZLIB_IMPLEMENTATION=zlib-ng -DUSE_JPEGLS=ON -DUSE_OPENJPEG=GitHub ..
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``.

### Building the command line version without cmake

This is the simplest way to compile dcm2niix on a Linux or MacOS computer. Be warned that this minimal version will not be able to extract DICOM images compressed with the (rarely used) JPEG2000 or JPEG-LS formats.

```bash
git clone https://github.com/rordenlab/dcm2niix.git
cd dcm2niix/console
make
./dcm2niix
```

## Referencing

 - Li X, Morgan PS, Ashburner J, Smith J, Rorden C (2016) The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 264:47-56. doi: 10.1016/j.jneumeth.2016.03.001. [PMID: 26945974](https://www.ncbi.nlm.nih.gov/pubmed/26945974) 

## Alternatives

 - [BIDS-converter](https://github.com/openneuropet/BIDS-converter) hosts Matlab and Python scripts for PET images, supporting DICOM and ECAT (ecat2nii) formats.
 - [dcm2nii](https://people.cas.sc.edu/rorden/mricron/dcm2nii.html) 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).
 - Python [dcmstack](https://github.com/moloney/dcmstack) DICOM to Nifti conversion with meta data preservation.
 - [dicm2nii](http://www.mathworks.com/matlabcentral/fileexchange/42997-dicom-to-nifti-converter) is written in Matlab. The Matlab language makes this very scriptable.
 - [dicom2nifti](https://github.com/icometrix/dicom2nifti) uses the scriptable Python wrapper utilizes the [high performance  GDCMCONV](http://gdcm.sourceforge.net/wiki/index.php/Gdcmconv) executables.
 - [dicomtonifti](https://github.com/dgobbi/vtk-dicom/wiki/dicomtonifti) leverages [VTK](https://www.vtk.org/).
 - [dimon](https://afni.nimh.nih.gov/pub/dist/doc/program_help/Dimon.html) and [to3d](https://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html) are included with AFNI.
 - [dinifti](https://as.nyu.edu/cbi/resources/Software/DINIfTI.html) is focused on conversion of classic Siemens DICOMs.
 - [DWIConvert](https://github.com/BRAINSia/BRAINSTools/tree/master/DWIConvert) converts DICOM images to NRRD and NIfTI formats.
 - [mcverter](http://lcni.uoregon.edu/%7Ejolinda/MRIConvert/) a great tool for classic DICOMs.
 - [mrd2nii](https://github.com/shimming-toolbox/mrd2nii) converts ISMRMRD data into NIfTI images.
 - [mri_convert](https://surfer.nmr.mgh.harvard.edu/pub/docs/html/mri_convert.help.xml.html) is part of the popular FreeSurfer package.
 - [MRtrix mrconvert](http://mrtrix.readthedocs.io/en/latest/reference/commands/mrconvert.html) is a useful general purpose image converter and handles DTI data well. It is an outstanding tool for modern Philips enhanced images.
 - [nanconvert](https://github.com/spinicist/nanconvert) uses the ITK library to convert DICOM from GE and proprietary Bruker to standard formats like DICOM.
 - [Plastimatch](https://plastimatch.org/) is a Swiss Army knife - it computes registration, image processing, 
 statistics and it has a basic image format converter that can convert some DICOM images to NIfTI or NRRD.
 - [Simple Dicom Reader 2 (Sdr2)](http://ogles.sourceforge.net/sdr2-doc/index.html) uses [dcmtk](https://dicom.offis.de/dcmtk.php.en) to read DICOM images and convert them to the NIfTI format.
 - [SlicerHeart extension](https://github.com/SlicerHeart/SlicerHeart) is specifically designed to help 3D Slicer support ultra sound (US) images stored as DICOM.
 - [spec2nii](https://github.com/wtclarke/spec2nii) converts MR spectroscopy to NIFTI.
 - [SPM12](http://www.fil.ion.ucl.ac.uk/spm/software/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.

## Links

dcm2niix is a core dependency of BIDS converters:

 - [Table of DICOM to BIDS converters](https://bids.neuroimaging.io/benefits#mri-and-pet-converterss)

The following tools exploit dcm2niix

  - [@niivue/dcm2niix](https://www.npmjs.com/package/@niivue/dcm2niix) is a WebAssembly (WASM) package of dcm2niix, allowing it to be embedded into web pages, as seen in this [live demo](https://niivue.github.io/niivue-dcm2niix/).
  - [abcd-dicom2bids](https://github.com/DCAN-Labs/abcd-dicom2bids) selectively downloads high quality ABCD datasets.
  - [AFNI](https://github.com/afni/afni) bundles dcm2niix_afni.
  - [autobids](https://github.com/khanlab/autobids) automates dcm2bids which uses dcm2niix.
  - [autobidsify](https://github.com/NeuroJSON/autobidsify) helps convert raw neuroimaging datasets into BIDS-compatible structures.
  - [BiDirect_BIDS_Converter](https://github.com/wulms/BiDirect_BIDS_Converter) for conversion from DICOM to the BIDS standard.
  - [BIDS Toolbox](https://github.com/cardiff-brain-research-imaging-centre/bids-toolbox) is a web service for the creation and manipulation of BIDS datasets, using dcm2niix for importing DICOM data.
  - [BIDScoin](https://github.com/Donders-Institute/bidscoin) is a DICOM to BIDS converter with a GUI and thorough [documentation](https://bidscoin.readthedocs.io).
  - [bidsconvertr](https://github.com/wulms/bidsconvertr) uses R to converts DICOM data to NIfTI and finally to BIDS.
  - [bidsify](https://github.com/spinoza-rec/bidsify) is a Python project that uses dcm2niix to convert DICOM and Philips PAR/REC images to the BIDS standard.
  - [bidskit](https://github.com/jmtyszka/bidskit) uses dcm2niix to create [BIDS](http://bids.neuroimaging.io/) datasets.
  - [BIDS-Manager](https://github.com/ANCPLabOldenburg/BIDS-Manager) GUI and CLI tool for BIDS conversion, curation, metadata editing.
  - [BIDS_Manager](https://github.com/Dynamap/BIDS_Manager) organizes data in BIDS standard.
  - [bidsme](https://github.com/CyclotronResearchCentre/bidsme) flexible bidsificator for multimodal datasets.
  - [BIDSvue](https://bidsvue.org/) graphical BIDS anonymization, validation and curation.
  - [BioImage Suite Web Project](https://github.com/bioimagesuiteweb/bisweb) is a JavaScript project that uses dcm2niix for its DICOM conversion module.
  - [birc-bids](https://github.com/bircibrain/birc-bids) provides a Docker/Singularity container with various BIDS conversion utilities.
  - [BMAT](https://github.com/ColinVDB/BMAT) translates data from MRI scanners to the BIDS structure.
  - [BOLD5000_autoencoder](https://github.com/nmningmei/BOLD5000_autoencoder) uses dcm2niix to pipe imaging data into an unsupervised machine learning algorithm.
  - [boutiques-dcm2niix](https://github.com/lalet/boutiques-dcm2niix) is a dockerfile for installing and validating dcm2niix.
  - [Brain imAgiNg Analysis iN Arcana (Banana)](https://pypi.org/project/banana/) is a collection of brain imaging analysis workflows, it uses dcm2niix for format conversions.
  - [Brain Lesion Suite](https://github.com/BrainLesion) supports dcm2niix for DICOM to NifTI conversion.
  - [brainnetome DiffusionKit](http://diffusion.brainnetome.org/en/latest/) uses dcm2niix to convert images.
  - [BraTS-Preprocessor](https://neuronflow.github.io/BraTS-Preprocessor/) uses dcm2niix to import files for [Brain Tumor Segmentation](https://www.frontiersin.org/articles/10.3389/fnins.2020.00125/full).
  - [browserqc](https://browserqc.org/) web-based quality metrics for DICOM and NIfTI brain scans inspired by [mriqc](https://mriqc.readthedocs.io/en/latest/#).
  - [CALMaR](https://github.com/neurodesk/calmar-webapp) Co-designed Automated Lesion Mapping and Reporting.
  - [CardioNIfTI](https://github.com/UK-Digital-Heart-Project/CardioNIfTI) processes cardiac MR DICOM datasets and converts them to NIfTI.
  - [clinica](https://github.com/aramis-lab/clinica) is a software platform for clinical neuroimaging studies that uses dcm2niix to convert DICOM images.
  - [clpipe](https://github.com/cohenlabUNC/clpipe) uses dcm2bids for DICOM import.
  - [conversion](https://github.com/pnlbwh/conversion) is a Python library that can convert dcm2niix created NIfTI files to the popular NRRD format (including DWI gradient tables). Note, recent versions of dcm2niix can directly convert DICOM images to NRRD.
  - [convert_source](https://github.com/AdebayoBraimah/convert_source) to convert DICOM to BIDS directory layout.
  - [CT-preprocess](https://github.com/GravO8/CT-preprocess) brain extract head CT scans.
  - [d2b-dcm2niix](https://github.com/d2b-dev/d2b-dcm2niix) data to BIDS wrapper.
  - [DAC2BIDS](https://github.com/dangom/dac2bids) uses dcm2niibatch to create [BIDS](http://bids.neuroimaging.io/) datasets.
  - [Data2Bids](https://github.com/SIMEXP/Data2Bids) converts non-DICOM images with associated JSON files to BIDS. While this tool does not require dcm2niix, it can leverage dcm2niix output similar to niix2bids.
  - [Dcm2Bids](https://github.com/cbedetti/Dcm2Bids) uses dcm2niix to create [BIDS](http://bids.neuroimaging.io/) datasets. Here is a [tutorial](https://andysbrainbook.readthedocs.io/en/latest/OpenScience/OS/BIDS_Overview.html) describing usage.
  - [dcm2niir](https://github.com/muschellij2/dcm2niir) R wrapper for dcm2niix/dcm2nii.
  - [dcm2niix_afni](https://afni.nimh.nih.gov/pub/dist/doc/program_help/dcm2niix_afni.html) is a version of dcm2niix included with the [AFNI](https://afni.nimh.nih.gov/) distribution.
  - [dcm2niiXL](https://github.com/neurolabusc/dcm2niiXL) is a shell script and tuned compilation of dcm2niix designed for accelerated conversion of extra large datasets.
  - [dcm2niixpy](https://github.com/Svdvoort/dcm2niixpy) Python package of dcm2niix.
  - [dcmwrangle](https://github.com/jbteves/dcmwrangle) a Python interactive and static tool for organizing dicoms.
  - [deface](https://niivue.github.io/deface/) browser-based anonymization of MR scans.
  - [DeepDicomSort](https://github.com/Svdvoort/DeepDicomSort) can recognize different scan types.
  - [DICOM-to-NIfTI-GUI](https://github.com/Zunairviqar/DICOM-to-NIfTI-GUI) is a Python script that provides a graphical wrapper for dcm2niix.
  - [dicom2bids](https://github.com/Jolinda/lcnimodules) includes python modules for converting dicom files to nifti in a bids-compatible file structure that use dcm2niix.
  - [DICOM2BIDS](https://github.com/klsea/DICOM2BIDS) is a Python 2 script for creating BIDS files.
  - [dicom2nifti_batch](https://github.com/scanUCLA/dicom2nifti_batch) is a Matlab script for automating dcm2niix.
  - [dicomConversionToNifti](https://github.com/bsmarine/dicomConversionToNifti) converts, de-identifies and assigns standardized naming convention to medical imaging.
  - [dicompare-web](https://github.com/astewartau/dicompare-web) for automated validation and comparison of MRI acquisition protocols using DICOM metadata.
  - [divest](https://github.com/jonclayden/divest) R interface to dcm2niix.
  - [DPABI](https://github.com/Chaogan-Yan/DPABI) [Data Processing & Analysis for Brain Imaging](https://rfmri.org/DPABI) includes dcm2niix.
  - [dwi2trx](https://tee-ar-ex.github.io/dwi2trx/) web app for processing diffusion data and generating custom gradient tables.
  - [edgereg](https://www.edgereg.org/) DICOM and NIfTI brain registration in the browser.
  - [Easy-MP2RAGE-T1-Map](https://github.com/thomshaw92/Easy-MP2RAGE-T1-Map) Quantitative T1 maps from MP2RAGE for desktop or [web](https://thomshaw92.github.io/Easy-MP2RAGE-T1-Map/)
  - [ExploreASL](https://sites.google.com/view/exploreasl/exploreasl) uses dcm2niix to import images.
  - [ExploreASL-GUI](https://github.com/MauricePasternak/ExploreASL-GUI) uses dcm2niix for image conversion.
  - [ezBIDS](https://github.com/brainlife/ezbids) is a [web service](https://brainlife.io/ezbids/) for converting directory full of DICOM images into BIDS without users having to learn python nor custom configuration file.
  - [fmrif tools](https://github.com/nih-fmrif/fmrif_tools) uses dcm2niix for its [oxy2bids](https://fmrif-tools.readthedocs.io/en/latest/#) tool.
  - [fMRIprep.dcm2niix](https://github.com/BrettNordin/fMRIprep.dcm2niix) is designed to convert DICOM format to the NIfTI format.
  - [FreeSurfer](https://github.com/freesurfer/freesurfer) includes dcm2niix for image conversion.
  - [fsleyes](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes) is a powerful Python-based image viewer. It uses dcm2niix to handle DICOM files through its fslpy libraries.
  - [Functional Real-Time Interactive Endogenous Neuromodulation and Decoding (FRIEND) Engine](https://github.com/InstitutoDOr/FriendENGINE) uses dcm2niix.
  - [HABIT](https://github.com/lichao312214129/HABIT/tree/main) tumor analysis.
  - [HCF-bidser](https://github.com/TamerGezici/HCF-bidser) Jupyter notebook script for DICOM to BIDS format.
  - [heudiconv](https://github.com/nipy/heudiconv) can use dcm2niix to create [BIDS](http://bids.neuroimaging.io/) datasets. Data acquired using the [reproin](https://github.com/ReproNim/reproin) convention can be easily converted to BIDS.
  - [Horos (Osirix) Bids Output Extension](https://github.com/mslw/horos-bids-output) is a OsiriX / Horos plugin that uses dcm2niix for creating BIDS output.
  - [kipettools](https://github.com/mathesong/kipettools) uses dcm2niix to load PET data.
  - [LEAD-DBS](http://www.lead-dbs.org/) uses dcm2niix for [DICOM import](https://github.com/leaddbs/leaddbs/blob/master/ea_dicom_import.m).
  - [lin4neuro](http://www.lin4neuro.net/lin4neuro/18.04bionic/vm/) releases such as the English l4n-18.04.4-amd64-20200801-en.ova include MRIcroGL and dcm2niix pre-installed. This allows user with VirtualBox or VMWarePlayer to use these tools (and many other neuroimaging tools) in a graphical virtual machine.
  - [mercure-dcm2bids](https://github.com/mercure-imaging/mercure-dcm2bids) is a Mercure module to perform DICOM to BIDS conversion using dcm2bids and dcm2niix.
  - [micapipe](https://github.com/MICA-MNI/micapipe) generates modality based connectomes.
  - [mri_convert](https://surfer.nmr.mgh.harvard.edu/pub/docs/html/mri_convert.help.xml.html) is part of the popular FreeSurfer package and wraps dcm2niix to improve DICOM support.
  - [MRIcroGL](https://github.com/neurolabusc/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](https://www.nitrc.org/projects/mricrogl/).
  - [MrPyConvert](https://github.com/Jolinda/mrpyconvert) Python library dicom to bids conversion.
  - [musclemap-webapp](https://github.com/neurodesk/musclemap-webapp) whole-body muscle segmentation using a MONAI 2D UNet model.
  - [Nekton](https://github.com/deepc-health/nekton) is a python package for DICOM to NifTi and NifTi to DICOM-SEG and GSPS conversion.
  - [NeuroClaw](https://github.com/CUHK-AIM-Group/NeuroClaw) AI for reproducible neuroimaging includes dcm2niix skills.
  - [neuro_docker](https://github.com/Neurita/neuro_docker) includes dcm2niix as part of a single, static Dockerfile.
  - [NeuroDebian](http://neuro.debian.net/pkgs/dcm2niix.html) provides up-to-date version of dcm2niix for Debian-based systems.
  - [neurodocker](https://github.com/kaczmarj/neurodocker) includes dcm2niix as a lean, minimal install Dockerfile.
  - [NeuroElf](http://neuroelf.net) can use dcm2niix to convert DICOM images.
  - [Neuroinformatics Database (NiDB)](https://github.com/gbook/nidb) is designed to store, retrieve, analyze, and share neuroimaging data. It uses dcm2niix for image QA and handling some formats. 
  - [neurospin_to_bids](https://github.com/neurospin/neurospin_to_bids) for exporting imaging data from the NeuroSpin archive in BIDS format.
  - [NiftyPET](https://niftypet.readthedocs.io/en/latest/install.html) provides PET image reconstruction and analysis, and uses dcm2niix to handle DICOM images. 
  - [niix2bids](https://github.com/benoitberanger/niix2bids ) attempts to automatically convert Siemens MRI images converted by dcm2niix to BIDS.
  - [nipoppy](https://github.com/nipoppy/nipoppy) uses dcm2niix (via dcm2bids).
  - [nipype](https://github.com/nipy/nipype) can use dcm2niix to convert images.
  - [PET2BIDS](https://github.com/openneuropet/PET2BIDS) uses dcm2niix for DICOM images.
  - [pl-dcm2niix](https://github.com/FNNDSC/pl-dcm2niix) is a ChRIS wrapper for dcm2niix.
  - [py2bids](https://github.com/Jolinda/py2bids) dcm2niix dicom to bids conversion wrapper.
  - [pyBIDSconv provides a graphical format for converting DICOM images to the BIDS format](https://github.com/DrMichaelLindner/pyBIDSconv). It includes clever default heuristics for identifying Siemens scans.
  - [pydcm2niix is a Python module for working with dcm2niix](https://github.com/jstutters/pydcm2niix).
  - [pydra-dcm2bids](https://github.com/aramis-lab/pydra-dcm2bids) supports Pydra tasks for dcm2bids.
  - [pydra-dcm2niix](https://github.com/nipype/pydra-dcm2niix) is a contains Pydra task interface for dcm2niix.
  - [qsm](https://github.com/CAIsr/qsm) Quantitative Susceptibility Mapping software.
  - [qsmbly](https://github.com/astewartau/qsmbly) browser-based quantitative susceptibility mapping.
  - [QSMxT](https://github.com/QSMxT/QSMxT) is an end-to-end software toolbox for Quantitative Susceptibility Mapping.
  - [qunex](https://github.com/ULJ-Yale/qunex) Quantitative Neuroimaging Environment & ToolboX for data organization, preprocessing, and quality assurance neuroimaging modalities.
  - [reproin](https://github.com/ReproNim/reproin) is a setup for automatic generation of shareable, version-controlled BIDS datasets from MR scanners.
  - [Retina_OCT_dcm2nii](https://github.com/Choupan/Retina_OCT_dcm2nii) converts optical coherence tomography (OCT) data to NIfTI.
  - [sci-tran dcm2niix](https://github.com/scitran-apps/dcm2niix) Flywheel Gear (docker).
  - [seedseg](https://github.com/astewartau/seedseg) segmentation of gold fiducial markers in T1-weighted prostate MRI.
  - [SHAring iN vivO Imaging Resources (Shanoir)](https://project.inria.fr/shanoir/) includes dcm2niix.
  - [shimming-toolbox](https://github.com/shimming-toolbox/shimming-toolbox) enabled static and real-time shimming, using dcm2niix to import DICOM data.
  - [SlicerDcm2nii](https://github.com/SlicerDMRI/SlicerDcm2nii) is an extension to import DICOM data into 3D Slicer.
  - [spinalcordtoolbox-webapp](https://github.com/neurodesk/spinalcordtoolbox-webapp) MRI segmentation interface for Spinal Cord Toolbox.
  - [tar2bids](https://github.com/khanlab/tar2bids) converts DICOM tarball(s) to BIDS using heudiconv which invokes dcm2niix.
  - [TORTOISE](https://tortoise.nibib.nih.gov) is used for processing diffusion MRI data, and uses dcm2niix to import DICOM images.
  - [TractoR (Tracto­graphy with R) uses dcm2niix for image conversion](http://www.tractor-mri.org.uk/TractoR-and-DICOM).
  - [twice_exceptionality_repository](https://github.com/avery-water/twice_exceptionality_repository) converts DICOM to BIDS format, creates masks, and runs VBM.
  - [vesselboost-webapp](https://github.com/neurodesk/vesselboost-webapp) blood vessel segmentation.
  - [XA30_workaround](https://github.com/vanandrew/XA30_workaround) uses dcm2niix.
  - [XNAT2BIDS](https://github.com/kamillipi/2bids) is a simple xnat pipeline to convert DICOM scans to BIDS-compatible output.
