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Abstract #1840

Data-Efficient Uncertainty Quantification for Radial Cardiac Cine MR Image Reconstruction

Sherine Brahma1, Tobias Schäffter1,2,3, Christoph Kolbitsch1,3, and Andreas Kofler1
1Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany, 2Department of Biomedical Engineering, Technical University of Berlin, Berlin, Germany, 3School of Imaging Sciences and Biomedical Engineering, King’s College London, London, United Kingdom


Due to the black-box nature of Deep Learning (DL) algorithms, uncertainty quantification (UQ) is a promising approach to assess their risk in medical applications. However, UQ is challenging in imaging techniques like non-Cartesian multi-coil CINE MRI because the data is high-dimensional, and the acquisition process is computationally demanding. In this work, i) we propose to utilize spatio-temporal (ST) networks, demonstrating efficient UQ in a high-dimensional setting, and ii) we show a reduction in uncertainty by adopting the forward model of radial multi-coil 2D cine MR in the reconstruction process. UQ is performed using MCMC dropout with additional aleatoric loss terms.

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