BART Online Open-Source Sequence Toolbox for Computational MRI

Abstract

Purpose In advanced computational MRI techniques, acquisition and reconstruction techniques are jointly designed. For reproducibility, it is therefore important to provide an open implementation of both. At the same time, any use in a clinical environment usually requires a close integration with the MRI scanner. Ensuring long-time reproducibility and maintenance then poses additional challenges. In this work, we aim to provide a fully integrated open-source framework that can meet these demands. Methods A software framework to develop pulse sequences is added to BART, an open-source toolbox for computational MRI. In addition, a vendor-specific driver sequence is developed that can be used to run the sequence on a clinical MRI scanner enabling online adjustment of all relevant sequence parameters. Using the Pulseq format, the exact same sequence can also be reproduced offline using a widely used vendor-neutral open-source standard. Using the Pulseq format, the exact same sequence can also be reproduced offline. As proof-of-concept, quantitative MRI methods for T1 and joint water/fat R2*, B0 mapping using radial FLASH and model-based reconstruction are implemented in the proposed framework. Consistency between online and offline acquisition is validated in phantom and in vivo experiments. Results Quantitative MRI methods highlighting specific challenges of acquisition and reconstruction were successfully implemented in BART. Acquisition parameters and FOV can be adapted online on a clinical MRI system. Quantitative parameter maps from model-based reconstruction agree for online and offline regenerated Pulseq acquisitions. Conclusion This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework.

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Cite

BibTeX RIS

BibTeX

@misc{mackner2026bart,
  title = {{BART Online Open-Source Sequence Toolbox for Computational MRI}},
  author = {Daniel Mackner and Philip Schaten and Markus Huemer and Viktoria Buchegger and Moritz Blumenthal and Xiaoqing Wang and Martin Uecker},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2607.19099},
  eprint = {2607.19099},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.19099},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - BART Online Open-Source Sequence Toolbox for Computational MRI
AU  - Daniel Mackner
AU  - Philip Schaten
AU  - Markus Huemer
AU  - Viktoria Buchegger
AU  - Moritz Blumenthal
AU  - Xiaoqing Wang
AU  - Martin Uecker
PY  - 2026
DA  - 2026/09/29
PB  - arXiv
DO  - 10.48550/arxiv.2607.19099
UR  - https://arxiv.org/abs/2607.19099
AB  - Purpose In advanced computational MRI techniques, acquisition and reconstruction techniques are jointly designed. For reproducibility, it is therefore important to provide an open implementation of both. At the same time, any use in a clinical environment usually requires a close integration with the MRI scanner. Ensuring long-time reproducibility and maintenance then poses additional challenges. In this work, we aim to provide a fully integrated open-source framework that can meet these demands. Methods A software framework to develop pulse sequences is added to BART, an open-source toolbox for computational MRI. In addition, a vendor-specific driver sequence is developed that can be used to run the sequence on a clinical MRI scanner enabling online adjustment of all relevant sequence parameters. Using the Pulseq format, the exact same sequence can also be reproduced offline using a widely used vendor-neutral open-source standard. Using the Pulseq format, the exact same sequence can also be reproduced offline. As proof-of-concept, quantitative MRI methods for T1 and joint water/fat R2*, B0 mapping using radial FLASH and model-based reconstruction are implemented in the proposed framework. Consistency between online and offline acquisition is validated in phantom and in vivo experiments. Results Quantitative MRI methods highlighting specific challenges of acquisition and reconstruction were successfully implemented in BART. Acquisition parameters and FOV can be adapted online on a clinical MRI system. Quantitative parameter maps from model-based reconstruction agree for online and offline regenerated Pulseq acquisitions. Conclusion This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework.
ER  -

Abstract

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