Zobrazit v EDS

A Physics-informed Conditional Wasserstein Autoencoder to Quantify Uncertainties in Accelerated 2D Dynamic Radial MRI

Uloženo v:

Podrobná bibliografie
Název:	A Physics-informed Conditional Wasserstein Autoencoder to Quantify Uncertainties in Accelerated 2D Dynamic Radial MRI
Autoři:	Sherine Brahma, Tobias Schaeffter, Christoph Kolbitsch, Andreas Kofler
Zdroj:	ISMRM Annual Meeting.
Informace o vydavateli:	ISMRM, 2024.
Rok vydání:	2024
Popis:	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.
Druh dokumentu:	Article
ISSN:	1545-4428
DOI:	10.58530/2023/4799
Přístupové číslo:	edsair.doi...........a9b62ff7cb9a5b47a08b61dba2445a98
Databáze:	OpenAIRE

Popis
Abstrakt:	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.
ISSN:	15454428
DOI:	10.58530/2023/4799