ultrasound-metrics

ultrasound-metrics#

ultrasound-metrics is an open-source Python library for ultrasound data and image quality analysis developed at Forest Neurotech. It provides a unified API supporting multiple array computation backends (NumPy, JAX, CuPy, PyTorch) through the Array API Standard.

Documentation on ultrasound-metrics can be found here, and examples can be viewed here. We are actively taking requests for additional metrics that may be helpful to ultrasound researchers.

Installation#

Build from source#

git clone https://github.com/Forest-Neurotech/ultrasound-metrics.git
cd ultrasound-metrics
make install

Build prerequisites:

  • uv >= 0.6.10

  • optional: make

Features#

We currently support the following ultrasound data and image quality metrics:

  • contrast

  • contrast-to-noise ratio (CNR)

  • generalized contrast-to-noise ratio (gCNR)

  • signal-to-noise ratio for raw radiofrequency signals (RF SNR)

  • signal-to-noise ratio for image ROIs (SNR)

  • temporal signal-to-noise ratio (tSNR)

  • sharpness (tenengrad)

  • coherence factor

and more!

To make a feature request, please submit a GitHub issue.

Acknowledgements#

ultrasound-metrics builds upon the excellent work of the open-source ultrasound community, including:

  • ultraspy - For educational examples and validation benchmarks

  • PICMUS - For public, standardized datasets used in examples

This package was developed by the Forest Neurotech team, a Focused Research Organization supported by Convergent Research and generous philanthropic funders.

Indices and tables#