11 research outputs found
Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge
No full textPurpose T-1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T-1 mapping technique, using acquisition details from a seminal T-1 mapping paper on a standardized phantom and in human brains. Methods The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T-1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. Results Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also eveloped: https://rrsg2020.dashboards.neurolibre.org. Conclusion The T-1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T-1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T-1 variations in vivo
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
No full textDataset provided for NeuroLibre preprint.
Author repo: https://www.github.com/rrsg2020/note
NeuroLibre fork:https://github.com/roboneurolibre/note <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p>
<p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
No full textGitHub archive of the <a href="https://github.com/roboneurolibre/note/commit/7e005e7e02aacd1b6404f6ee8eaf978415ca7c6a"> reference repository/commit by roboneuro</a>, based on the <a href="https://www.github.com/rrsg2020/note/commit/af67956e29e9f37ffaedd772552872eeb5c8ad6f">latest change by the author</a>. <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p>
<p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
Results of the ISMRM 2020 joint Reproducible Research & Quantitative MR study groups reproducibility challenge on phantom and human brain T1 mapping
No full textThis dataset includes both raw data and processed T1 maps obtained from the 2020 challenge on inversion recovery T1 mapping organized by the International Society in Magnetic Resonance in Medicine (ISMRM) Reproducible Research Study Group (RRSG).
For a comprehensive overview of the data distribution submitted for this challenge, please visit: https://rrsg2020.db.neurolibre.org.
It's important to note that this dataset exclusively comprises ISMRM-NIST system phantom data.
Dataset provided for NeuroLibre preprint.
Author repo: https://github.com/rrsg2020/paper
NeuroLibre fork:https://github.com/roboneurolibre/paper
For details, please visit the corresponding NeuroLibre technical screening: https://neurolibre.or
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
No full textNeuroLibre JupyterBook built at this <a href="https://github.com/roboneurolibre/note/commit/7e005e7e02aacd1b6404f6ee8eaf978415ca7c6a"> reference repository/commit by roboneuro</a>, based on the <a href="https://www.github.com/rrsg2020/note/commit/af67956e29e9f37ffaedd772552872eeb5c8ad6f">latest change by the author</a>. <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p>
<p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
Paper is not enough: Crowdsourcing the T<sub>1</sub> mapping common ground via the ISMRM reproducibility challenge
No full textDocker image built from the <a href="https://github.com/roboneurolibre/note/commit/7e005e7e02aacd1b6404f6ee8eaf978415ca7c6a"> reference repository/commit by roboneuro</a>, based on the <a href="https://www.github.com/rrsg2020/note/commit/af67956e29e9f37ffaedd772552872eeb5c8ad6f">latest change by the author</a>, using repo2docker (through BinderHub). <br> To run locally: <ol> <li><pre><code>docker load < DockerImage_10.55458_NeuroLibre_00023_7e005e.tar.gz</code><pre></pre></pre></li><li><pre><code>docker run -it --rm -p 8888:8888 DOCKER_IMAGE_ID jupyter lab --ip 0.0.0.0</code></pre> </li></ol> <p><strong>by replacing <code>DOCKER_IMAGE_ID</code> above with the respective ID of the Docker image loaded from the zip file.</strong></p> <p>For details, please visit the corresponding <a href="https://github.com/neurolibre/neurolibre-reviews/issues/23">NeuroLibre technical screening.</a></p>
<p><strong><a href="https://neurolibre.org" target="NeuroLibre">https://neurolibre.org</a></strong></p>
Paper is not enough: Crowdsourcing the T₁ mapping common ground via the ISMRM reproducibility challenge
No full text
