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Abstract #3462

Contrast prediction-based regularization for iterative reconstructions (PROSIT)

Hendrik Mattern1, Alessandro Sciarra1,2, Max Dünnwald2,3, Soumick Chatterjee1,3,4, Ursula Müller1, Steffen Oeltze-Jafra2,5, and Oliver Speck1,5,6,7
1Biomedical Magnetic Resonance, Otto-von-Guericke University, Magdeburg, Germany, 2Medicine and Digitalization, Otto-von-Guericke University, Magdeburg, Germany, 3Faculty of Computer Science, Otto-von-Guericke University, Magdeburg, Germany, 4Data & Knowledge Engineering Group, Otto-von-Guericke-University, Magdeburg, Germany, 5Center for Behavioral Brain Sciences, Magdeburg, Germany, 6German Center for Neurodegenerative Disease, Magdeburg, Germany, 7Leibniz Institute for Neurobiology, Magdeburg, Germany

In this study, contrast prediction is used as an auxiliary tool to regularize underdetermined image reconstructions. This novel regularization strategy enables to share information across individual reconstructions and outperforms state of the art regularizations for high acceleration factors.

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