BART Online Open-Source Sequence Toolbox for Computational MRI.

Daniel Mackner, Philip Schaten, Markus Huemer, Viktoria Buchegger, Moritz Blumenthal, Xiaoqing Wang, Martin Uecker

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In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Daniel MacknerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Philip SchatenInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Markus HuemerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Viktoria BucheggerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Moritz BlumenthalInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Xiaoqing WangDepartment of Radiology, Harvard Medical School, Boston, Massachusetts, USA.

Martin UeckerInstitute of Biomedical Imaging, Graz University of Technology, Graz, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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. As proof-of-concept, quantitative MRI methods for 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.

Indexed as

model-based reconstructionOpen-sourcequantitative MRIreproducibilitysequence development

Identifiers

PMID42539102

PMCPMC13419618

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.