https://github.com/schote/nexus-console
MRI console application based on spectrum-instrumentation measurement cards and pulse sequences from pypulseq. Read more in the docs!
https://github.com/schote/nexus-console
acquisition console health imaging medical mri pypulseq spectroscopy spectrum-cards spectrum-instrumentation
Last synced: 4 months ago
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MRI console application based on spectrum-instrumentation measurement cards and pulse sequences from pypulseq. Read more in the docs!
- Host: GitHub
- URL: https://github.com/schote/nexus-console
- Owner: schote
- License: gpl-3.0
- Created: 2023-06-27T15:51:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-06-10T11:13:30.000Z (4 months ago)
- Last Synced: 2025-06-10T12:25:54.177Z (4 months ago)
- Topics: acquisition, console, health, imaging, medical, mri, pypulseq, spectroscopy, spectrum-cards, spectrum-instrumentation
- Language: Python
- Homepage: https://schote.github.io/nexus-console/
- Size: 18.9 MB
- Stars: 17
- Watchers: 5
- Forks: 5
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Nexus-Console for Advanced Low-Field MRI
[](https://www.gnu.org/licenses/gpl-3.0.de.html)
[](https://github.com/schote/spectrum-console/actions/workflows/docs.yml)
[](https://github.com/schote/spectrum-console/actions/workflows/static-tests.yml)
[](https://github.com/schote/spectrum-console/actions/workflows/pytest.yml)
This project aims to implement a versatile console for low-field magnetic resonance imaging (MRI) acquisitions. The central hardware components are two spectrum cards from Spectrum Instrumentation.
They serve as arbitrary waveform generators (AWG) and analog to digital converter (digitizer).
Depending on the measurement card specification, the Nexus console can also be used for higher frequencies (first experiments at 7T were conducted).
This application controls AWG and digitizer cards to perform MRI scans by directly interpreting sequences defined by the open-source pulseq framework, i.e. the python implementation [pypulseq](https://github.com/imr-framework/pypulseq).
An interfaces to the open data MR raw data format [ISMRMRD](https://ismrmrd.github.io/apidocs/1.5.0/) is implemented to directly enable Nexus with the latest reconstruction algorithms, e.g. from the [Gadgetron](https://gadgetron.github.io/) toolbox.
## Installation
It is recommended to install the package in a virtual environment (e.g. [conda](https://docs.conda.io/projects/conda/en/stable/)).
Further documentation on setting up miniconda can be found [here](https://conda.io/projects/conda/en/stable/user-guide/install/index.html).
The package was developed under [Python 3.10](https://www.python.org/downloads/release/python-3100/) so it is recommended to use `python==3.10`.
To install the Nexus console application, clone the repository an ensure that you are in the repository directory, which is `*/nexus-console/`.
The package can be installed with different dependencies depending on the specific requirements:
`pip install -e .`
Installs all the necessary base dependencies to use the package (minimum required).
`pip install -e ".[lint]"`
Installs additional (optional) dependencies that are required to run the linter.
`pip install -e ".[test]"`
Installs additional (optional) dependencies that are required to run the linter.
`pip install -e ".[docs]"`
Installs additional (optional) dependencies that are required to build the sphinx documentation locally.
`pip install -e ".[dev]"`
Installs additional (optional) developer dependencies for profiling and developing in vs code.
_Hint: Multiple dependency groups can be installed using `".[lint, test]"` for instance._
## Usage
Please follow the project [documentation](https://schote.github.io/nexus-console/) which contains a quick-start guide, some examples and a user guide.
## Publications
If you find this useful in your work, please [cite](https://doi.org/10.1002/mrm.30406):
Schote D, Silemek B, O'Reilly T, Seifert F, Assmy JL, Kolbitsch C, Webb AG, Winter L. Nexus: A versatile console for advanced low-field MRI. Magn Reson Med. 2025. doi: 10.1002/mrm.30406.
## Acknowledgments
This work is part of the Metrology for Artificial Intelligence for Medicine (M4AIM) project, which is funded by the Federal Ministry of Economic Affairs and Climate Action (BMWK) as part of the QI-Digital initiative.
The projects 21NRM05 STASIS and 22HLT02 A4IM have received funding from the European Partnership on Metrology, cofinanced by the European Union's Horizon Europe Research and Innovation Program and by the Participating States.
Partial support for this project is provided by a European Research Council Advanced Grant (PASMAR 101021218).
This work is supported by the Open Source Imaging Initiative (OSI²)
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