VoxelKit
Inspect, preview, and QA-check multidimensional imaging data from one unified Python and CLI workflow.
⚡ Get started in seconds
pip install voxelkit
Features
Everything you need for day-to-day imaging dataset triage
File Inspection
Extract shape, dtype, and metadata from any supported format instantly — no format-specific boilerplate required.
PNG Slice Previews
Generate crisp 2D previews from 3D/4D volumes with configurable plane, slice index, and colormap in a single command.
Per-File QA Reports
Compute statistics and auto-detect problems — NaNs, Infs, constant arrays, near-zero volumes — for any single file.
Batch QA Reporting
Aggregate QA across an entire directory with report-batch. Emit JSON for pipelines or a self-contained HTML report with thumbnails for sharing.
DICOM with PHI Stripping
Inspect single .dcm files or whole series directories. Patient identifiers are stripped by default; --phi opts in with a stderr warning.
Batch Anonymisation
Scrub PHI from every .dcm under a directory tree with voxelkit anonymise. Preserves pixel data, modality, and series grouping.
DICOM → NIfTI Conversion
Convert a single slice or full series directory to NIfTI with voxelkit convert. Builds the affine from DICOM headers with the LPS → RAS flip applied.
Embedding Analysis
Specialized QA for 2D embedding matrices — detect dead dimensions, outlier samples, and per-dimension norm statistics.
Unified Python API
Four clean functions — inspect_file, preview_file, report_file, report_batch — work identically across all formats.
REST API
FastAPI-powered HTTP endpoints for remote inspection, preview generation, and QA reporting — drop into any stack.
Optional Local GUI
Streamlit-based offline interface for point-and-click workflows. Launch in seconds with voxelkit gui.
Supported Formats
One toolkit, five imaging ecosystems
Who Can Benefit
VoxelKit fits wherever multidimensional data needs triage
Neuroscience Researchers
Quickly inspect and QA fMRI, DTI, and structural MRI datasets in NIfTI format — no format-specific boilerplate.
Medical Imaging Teams
Run batch QA across large clinical datasets to catch constant arrays, NaN-contaminated volumes, or zero-dominated scans before analysis.
ML / AI Practitioners
Validate imaging tensors and embedding matrices before training. Catch dead dimensions and outliers early to prevent silent failures.
Data Scientists
Rapidly explore unfamiliar multidimensional datasets from collaborators without reading format-specific documentation first.
Data Engineers
Integrate VoxelKit's REST API or Python interface into automated pipelines to enforce data quality gates at ingestion time.
Geospatial Engineers
Inspect and QA multi-band raster stacks, satellite imagery tiles, and elevation models stored as NumPy, HDF5, or TIFF arrays.
Educators & Students
Use the GUI and CLI to interactively explore neuroimaging and scientific datasets in teaching and research settings.
Ready to triage your data?
The full documentation covers installation, CLI reference, Python API, QA warning types, and real-world examples.