dicom2nifti
package for converting dicom files to nifti
Decision gist · record as of 2026-08-14
Yes. The package is stable, permissive-licensed, and widely used. Install friction is low and dependencies are mature. The 417-day gap since last release is notable but not alarming given the narrow, well-defined scope; the repository is not archived and remains responsive. Choose it if you need robust DICOM-to-NIfTI conversion for medical imaging; be aware that some vendor formats and transfer syntaxes require workarounds or remain unsupported.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Compressed DICOM formats (JPEG, JPEG2000) require GDCM to be installed separately via your system package manager or conda.
- Low install friction with a pure-Python wheel; five runtime dependencies including nibabel, numpy, scipy, and pydicom are all stable and widely used.
- Last release was 417 days ago and the repository remains active, though maintenance pace has slowed.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions.
last release 2025-06-23 (417 days) · last repo commit 2025-06-23 · 403 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 237,818 downloads/mo, #8,953 on PyPI
Alternatives
Verify before relying
pip install dicom2nifti
import dicom2nifti
dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True)- Whether the package handles all Philips Enhanced DICOM variants reliably in production workflows.
- Current state of support for Hitachi MR DTI/DKI data—documentation notes it is not supported but invites contributions.
- Actual download volume and user base size to assess real-world adoption beyond ranking position.
What it is and what it does
dicom2nifti is a Python library that converts DICOM medical imaging files into NIfTI format, the standard for neuroimaging research. It handles anatomical CT and MR scans from multiple vendors (GE, Siemens, Philips, Hitachi) and includes vendor-specific support for 4D imaging like fMRI and DTI/DKI sequences. The library provides both a command-line interface and a Python API, with options to reorient images, resample to orthogonal geometry, and handle edge cases like gantry-tilted CT or inconsistent slice increments.
The package depends on nibabel for NIfTI I/O, numpy and scipy for array operations and interpolation, and pydicom plus python-gdcm for DICOM parsing and decompression. It is marked Production/Stable and targets healthcare and research audiences. Most classical anatomical DICOM files are supported; non-anatomical sequences may require explicit configuration, and some vendor-specific formats or transfer syntaxes remain unsupported.
Use it for
- Convert a batch of DICOM scans from a hospital PACS into NIfTI for neuroimaging analysis pipelines.
- Prepare fMRI or DTI data from Siemens or GE scanners for preprocessing workflows.
- Handle gantry-tilted CT scans by resampling to orthogonal space while preserving geometry via affine transformation.
- Process Philips Enhanced DICOM files with vendor-specific metadata preservation for research studies.
- Automate single-series DICOM-to-NIfTI conversion in a clinical data workflow with command-line scripting.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is stable, permissive-licensed, and widely used. Install friction is low and dependencies are mature. The 417-day gap since last release is notable but not alarming given the narrow, well-defined scope; the repository is not archived and remains responsive. Choose it if you need robust DICOM-to-NIfTI conversion for medical imaging; be aware that some vendor formats and transfer syntaxes require workarounds or remain unsupported.
Install
dicom2nifti on PyPI
Before you install
Low install friction with a pure-Python wheel; five runtime dependencies including nibabel, numpy, scipy, and pydicom are all stable and widely used. Last release was 417 days ago and the repository remains active, though maintenance pace has slowed.
Compressed DICOM formats (JPEG, JPEG2000) require GDCM to be installed separately via your system package manager or conda.
License in practice
MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions.
Quickstart
pip install dicom2nifti
import dicom2nifti
dicom2nifti.convert_directory(dicom_directory, output_folder, compression=True, reorient=True)Verify before relying
- Whether the package handles all Philips Enhanced DICOM variants reliably in production workflows.
- Current state of support for Hitachi MR DTI/DKI data—documentation notes it is not supported but invites contributions.
- Actual download volume and user base size to assess real-world adoption beyond ranking position.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnibabelnumpyscipypydicompython-gdcm |
| Maintenance | Aging 417 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 237,818 / month, #8,953 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/Engineering :: Medical Science Apps. |
Evidence: dicom2nifti-2.6.2-py3-none-any.whl
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See also python-gdcm · highdicom · pylibjpeg · dicomweb-client · TotalSegmentator · nibabel · monai · itk-io · pynrrd