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Abstract #1875

Uncertainty quantification for ground-truth free evaluation of deep learning reconstructions

Soumick Chatterjee1,2,3, Alessandro Sciarra1,4, Max Dünnwald3,4, Anitha Bhat Talagini Ashoka3, Mayura Gurjar Cheepinahalli Vasudeva3, Shudarsan Saravanan3, Venkatesh Thirugnana Sambandham3, Steffen Oeltze-Jafra4,5, Oliver Speck1,5,6,7, and Andreas Nürnberger2,3,5
1Department of Biomedical Magnetic Resonance, Otto von Guericke University Magdeburg, Magdeburg, Germany, 2Data and Knowledge Engineering Group, Otto von Guericke University Magdeburg, Magdeburg, Germany, 3Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany, 4MedDigit, Department of Neurology, Medical Faculty, University Hospital, Magdeburg, Germany, 5Center for Behavioral Brain Sciences, Magdeburg, Germany, 6German Centre for NeurodegenerativeDiseases, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany


Many deep learning-based techniques have been proposed in recent years to reconstruct undersampled MRI – showing their potential for shortening the acquisition time. Before using them in actual practice, they are usually evaluated by comparing their results against the available ground-truth – which is not available during real applications. This research shows the potential of using uncertainty estimation to evaluate the reconstructions without using any ground-truth images. The method has been evaluated for the task of super-resolution MRI, for acceleration factors ranging from two to four – in all three dimensions.

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