pyvoxel 0.0.1a1

An AI-powered open-source medical image analysis toolbox

Voxel

Documentation | DOSMA Basics Tutorial

Voxel provides fast Pythonic data structures and tools for wrangling with medical images.

Installation

Voxel requires Python 3.7+. The core module depends on numpy, nibabel, pydicom, PyYAML, and tqdm.

To install Voxel, run:

pip install pyvoxel

If you would like to contribute to Voxel, we recommend you clone the repository and install Voxel with pip in editable mode.

git clone [e-mail]:voxelimaging/pyvoxel.git
cd pyvoxel
pip install -e '.[dev,docs]'
make dev

To run tests, build documentation and contribute, run

make autoformat test build-docs

Features

Simplified, Efficient I/O

Voxel provides efficient readers for DICOM and NIfTI formats built on nibabel and pydicom. Multi-slice DICOM data can be loaded in parallel with multiple workers and structured into the appropriate 3D volume(s). For example, multi-echo and dynamic contrast-enhanced (DCE) MRI scans have multiple volumes acquired at different echo times and trigger times, respectively. These can be loaded into multiple volumes with ease:

import voxel as vx

multi_echo_scan = dm.load("/path/to/multi-echo/scan", group_by="EchoNumbers", num_workers=8, verbose=True)
dce_scan = dm.load("/path/to/dce/scan", group_by="TriggerTime")

Data-Embedded Medical Images

Voxel's MedicalVolume data structure supports array-like operations (arithmetic, slicing, etc.) on medical images while preserving spatial attributes and accompanying metadata. This structure supports NumPy interoperability, intelligent reformatting, fast low-level computations, and native GPU support. For example, given MedicalVolumes mvA and mvB we can do the following:

# Reformat image into Superior->Inferior, Anterior->Posterior, Left->Right directions.
mvA = mvA.reformat(("SI", "AP", "LR"))

# Get and set metadata
study_description = mvA.get_metadata("StudyDescription")
mvA.set_metadata("StudyDescription", "A sample study")

# Perform NumPy operations like you would on image data.
rss = np.sqrt(mvA**2 + mvB**2)

# Move to GPU 0 for CuPy operations
mv_gpu = mvA.to(dosma.Device(0))

# Take slices. Metadata will be sliced appropriately.
mv_subvolume = mvA[10:20, 10:20, 4:6]

Citation

Voxel is a refactored version of the dosma package that focuses on medical image data structures and I/O. If you use Voxel in your research, please cite the following work:

@inproceedings{desai2019dosma,
  title={DOSMA: A deep-learning, open-source framework for musculoskeletal MRI analysis},
  author={Desai, Arjun D and Barbieri, Marco and Mazzoli, Valentina and Rubin, Elka and Black, Marianne S and Watkins, Lauren E and Gold, Garry E and Hargreaves, Brian A and Chaudhari, Akshay S},
  booktitle={Proc 27th Annual Meeting ISMRM, Montreal},
  pages={1135},
  year={2019}
}

In addition to DOSMA, please also consider citing the work that introduced the method used for analysis.