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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