# mach 5 beamformer
[](https://pypi.org/project/mach-beamform/)
[](https://pypi.org/project/mach-beamform/)
[](https://github.com/Forest-Neurotech/mach/blob/main/LICENSE)
[](https://github.com/Forest-Neurotech/mach/actions/)
An ultrafast CUDA-accelerated ultrasound beamformer for Python users. Developed at [Forest Neurotech](https://forestneurotech.org/).
_[Benchmark](https://github.com/Forest-Neurotech/mach/blob/main/BENCHMARKS.md): Beamforming PyMUST's [rotating-disk Doppler dataset](https://github.com/creatis-ULTIM/PyMUST/blob/170ba68/examples/rotatingDisk_real.ipynb) at 0.86 trillion points per second ([**5**x the speed of sound](https://github.com/Forest-Neurotech/mach/blob/main/BENCHMARKS.md))._
> **⚠️ Alpha Release**
>
> This library is currently under active development and is released as an alpha version. The primary goal of this release is to collect community feedback.
## Highlights
* ⚡ **Ultra-fast beamforming**: ~10x faster than prior state-of-the-art
* 🚀 **GPU-accelerated**: Leverages CUDA for maximum performance on NVIDIA GPUs
* 🎯 **Optimized for research**: Designed for functional ultrasound imaging (fUSI) and other ultrafast, high-channel-count, or volumetric-ensemble imaging
* 🐍 **Python bindings**: Zero-copy integration with CuPy, and JAX arrays via [nanobind](https://nanobind.readthedocs.io/en/latest/index.html). NumPy support included.
* 🔬 **Validated**: Matches [vbeam](https://github.com/magnusdk/vbeam) and [PyMUST](https://github.com/creatis-ULTIM/PyMUST) [outputs](https://github.com/Forest-Neurotech/mach/tree/812062f/tests/compare)
## Installation
### Install from PyPI (recommended):
```bash
pip install mach-beamform
```
Wheel prerequisites:
* [Linux](https://github.com/pypa/manylinux)
* CUDA-enabled GPU with driver >= 12.3, [compute-capability >= 7.5](https://developer.nvidia.com/cuda-gpus)
### Build from source
```bash
make compile
```
Build prerequisites:
* Linux
* `make`
* `uv >= 0.6.10`
* `gcc >= 8`
* `nvcc >= 11.0`
## Examples
Try our [examples](https://forest-neurotech.github.io/mach/examples/):
* [📊 Plane Wave Imaging with PICMUS Dataset](examples/plane_wave_compound.py)
* [🩸 Doppler Imaging](examples/doppler.py)
If you don't have a CUDA-enabled GPU, you can download the notebook from the [docs](https://forest-neurotech.github.io/mach/examples/) and open in Google Colab (select a GPU instance).
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](https://github.com/Forest-Neurotech/mach/blob/812062f/CONTRIBUTING.md) for guidelines.
## Roadmap
### Alpha release (v0.0.Z)
- ✅ Single-wave transmissions (plane wave, focused, diverging)
- ✅ Linear interpolation beamforming
- ✅ Allow NumPy/CuPy/JAX/PyTorch inputs through Array API
- ✅ Comprehensive error handling
- ✅ PyPI packaging and distribution
### Experimental features
- Coherent compounding
### Tentative Future Plans
- Additional interpolation methods (spline, sinc)
- Additional apodization windows
See the [project page](https://github.com/orgs/Forest-Neurotech/projects/14) for our up-to-date roadmap.
We welcome [feature requests](https://github.com/Forest-Neurotech/mach/issues)!
## Acknowledgments
mach builds upon the excellent work of the ultrasound imaging community:
* **[vbeam](https://github.com/magnusdk/vbeam)** - For educational examples and validation benchmarks
* **[PyMUST](https://github.com/creatis-ULTIM/PyMUST) / [PICMUS](https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/)** - For standardized evaluation datasets
* **Community contributors** - Gev and Qi for CUDA optimization guidance
## Citation
If you use mach in your research, please cite:
```bibtex
@software{mach,
title={mach: Ultra-fast GPU-accelerated ultrasound beamforming},
author={Guan, Charles and Rockhill, Alex and Pinton, Gianmarco},
organization={Forest Neurotech},
year={2025},
url={https://github.com/Forest-Neurotech/mach}
}
```
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"description": "# mach 5 beamformer\n\n[](https://pypi.org/project/mach-beamform/)\n[](https://pypi.org/project/mach-beamform/)\n[](https://github.com/Forest-Neurotech/mach/blob/main/LICENSE)\n[](https://github.com/Forest-Neurotech/mach/actions/)\n\nAn ultrafast CUDA-accelerated ultrasound beamformer for Python users. Developed at [Forest Neurotech](https://forestneurotech.org/).\n\n\n\n_[Benchmark](https://github.com/Forest-Neurotech/mach/blob/main/BENCHMARKS.md): Beamforming PyMUST's [rotating-disk Doppler dataset](https://github.com/creatis-ULTIM/PyMUST/blob/170ba68/examples/rotatingDisk_real.ipynb) at 0.86 trillion points per second ([**5**x the speed of sound](https://github.com/Forest-Neurotech/mach/blob/main/BENCHMARKS.md))._\n\n> **\u26a0\ufe0f Alpha Release**\n>\n> This library is currently under active development and is released as an alpha version. The primary goal of this release is to collect community feedback.\n\n\n## Highlights\n\n* \u26a1 **Ultra-fast beamforming**: ~10x faster than prior state-of-the-art\n* \ud83d\ude80 **GPU-accelerated**: Leverages CUDA for maximum performance on NVIDIA GPUs\n* \ud83c\udfaf **Optimized for research**: Designed for functional ultrasound imaging (fUSI) and other ultrafast, high-channel-count, or volumetric-ensemble imaging\n* \ud83d\udc0d **Python bindings**: Zero-copy integration with CuPy, and JAX arrays via [nanobind](https://nanobind.readthedocs.io/en/latest/index.html). NumPy support included.\n* \ud83d\udd2c **Validated**: Matches [vbeam](https://github.com/magnusdk/vbeam) and [PyMUST](https://github.com/creatis-ULTIM/PyMUST) [outputs](https://github.com/Forest-Neurotech/mach/tree/812062f/tests/compare)\n\n\n## Installation\n\n### Install from PyPI (recommended):\n\n```bash\npip install mach-beamform\n```\n\nWheel prerequisites:\n* [Linux](https://github.com/pypa/manylinux)\n* CUDA-enabled GPU with driver >= 12.3, [compute-capability >= 7.5](https://developer.nvidia.com/cuda-gpus)\n\n### Build from source\n\n```bash\nmake compile\n```\nBuild prerequisites:\n* Linux\n* `make`\n* `uv >= 0.6.10`\n* `gcc >= 8`\n* `nvcc >= 11.0`\n\n## Examples\n\nTry our [examples](https://forest-neurotech.github.io/mach/examples/):\n\n* [\ud83d\udcca Plane Wave Imaging with PICMUS Dataset](examples/plane_wave_compound.py)\n* [\ud83e\ude78 Doppler Imaging](examples/doppler.py)\n\nIf you don't have a CUDA-enabled GPU, you can download the notebook from the [docs](https://forest-neurotech.github.io/mach/examples/) and open in Google Colab (select a GPU instance).\n\n## Contributing\n\nWe welcome contributions! Please see [CONTRIBUTING.md](https://github.com/Forest-Neurotech/mach/blob/812062f/CONTRIBUTING.md) for guidelines.\n\n## Roadmap\n\n### Alpha release (v0.0.Z)\n- \u2705 Single-wave transmissions (plane wave, focused, diverging)\n- \u2705 Linear interpolation beamforming\n- \u2705 Allow NumPy/CuPy/JAX/PyTorch inputs through Array API\n- \u2705 Comprehensive error handling\n- \u2705 PyPI packaging and distribution\n\n### Experimental features\n- Coherent compounding\n\n### Tentative Future Plans\n- Additional interpolation methods (spline, sinc)\n- Additional apodization windows\n\nSee the [project page](https://github.com/orgs/Forest-Neurotech/projects/14) for our up-to-date roadmap.\nWe welcome [feature requests](https://github.com/Forest-Neurotech/mach/issues)!\n\n## Acknowledgments\n\nmach builds upon the excellent work of the ultrasound imaging community:\n\n* **[vbeam](https://github.com/magnusdk/vbeam)** - For educational examples and validation benchmarks\n* **[PyMUST](https://github.com/creatis-ULTIM/PyMUST) / [PICMUS](https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/)** - For standardized evaluation datasets\n* **Community contributors** - Gev and Qi for CUDA optimization guidance\n\n## Citation\n\nIf you use mach in your research, please cite:\n\n```bibtex\n@software{mach,\n title={mach: Ultra-fast GPU-accelerated ultrasound beamforming},\n author={Guan, Charles and Rockhill, Alex and Pinton, Gianmarco},\n organization={Forest Neurotech},\n year={2025},\n url={https://github.com/Forest-Neurotech/mach}\n}\n```\n",
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