A Physics-informed Conditional Wasserstein Autoencoder to Quantify Uncertainties in Accelerated 2D Dynamic Radial MRI
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| Titel: | A Physics-informed Conditional Wasserstein Autoencoder to Quantify Uncertainties in Accelerated 2D Dynamic Radial MRI |
|---|---|
| Autoren: | Sherine Brahma, Tobias Schaeffter, Christoph Kolbitsch, Andreas Kofler |
| Quelle: | ISMRM Annual Meeting. |
| Verlagsinformationen: | ISMRM, 2024. |
| Publikationsjahr: | 2024 |
| Beschreibung: | Uncertainty quantification (UQ) can provide important information about deep learning algorithms and help interpret the obtained results. UQ for multi-coil dynamic MRI is challenging due to the large scale of the problem and scarce training data. We approach these issues by learning distributions in a lower dimensional latent space using a conditional Wasserstein autoencoder while utilizing the MR data acquisition model and by exploiting spatio-temporal correlations of the cine MR images. Our results indicate excellent image quality accompanied with uncertainty maps that correlate well with estimation errors. |
| Publikationsart: | Article |
| ISSN: | 1545-4428 |
| DOI: | 10.58530/2023/4799 |
| Dokumentencode: | edsair.doi...........a9b62ff7cb9a5b47a08b61dba2445a98 |
| Datenbank: | OpenAIRE |
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