{"categories":[{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"}],"enrichment":{"capability":"Converts DICOM medical imaging files to NIfTI format, supporting anatomical CT/MR scans and vendor-specific 4D imaging (fMRI, DTI) with optional resampling and reorientation.","skillfed_tags":["medical-imaging","dicom-nifti","neuroimaging"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"dicom2nifti","links":{"html":"https://skillfed.io/packages/dicom2nifti","md":"https://skillfed.io/packages/dicom2nifti.md","pypi":"https://pypi.org/project/dicom2nifti/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-06-23","license_spdx":null,"license_treatment":"permissive","name":"dicom2nifti","python_support":"unspecified","summary":"package for converting dicom files to nifti"},"popularity":{"monthly_downloads":237818,"position":8953,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.6.2"}