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| Autors principals: | , |
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| Format: | Recurso digital |
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Zenodo
2025
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| Matèries: | |
| Accés en línia: | https://doi.org/10.5281/zenodo.17817186 |
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- <h3>Description</h3> <div>Fulll pipeline for training a deep-learning model to separate fat and water from Dixon-MRI magnitude images.</div> <h3>Output</h3> <div>The trained model weights can be found on: <a href="https://zenodo.org/records/17791059">https://zenodo.org/records/17791059</a></div> <h3>Details</h3> <div>See the <a href="https://github.com/openmiblab/iBEAt-fatwater">README on GitHub</a></div> <h3>Summary</h3> <div> <div> <div>Computation of fat and water images from a 2-point MRI Dixon acquisition is usually done in-line by the scanner software, and requires access to the phase and magnitude data.</div> <br> <div>In some cases one may want to compute fat and water images retrospectively - for instance when they were not originally exported, or in order to reconstruct them with different models (e.g. with correction for T2* decay, B0-effects, etc). This causes a practical problem when, as is common, phase images are not stored and only magnitude images of in-phase and opposed-phase scans are available.</div> <br> <div>The crucial bit of information that is missing with magnitude-only data is the sign of the opposed phase image - does the pixel contain mostly water or mostly fat? This pipeline trains a deep learning model to recover this binary information from magnitude images of in-phase and opposed-phase data.</div> </div> </div>