Abstract #3462

Contrast prediction-based regularization for iterative reconstructions (PROSIT)

Hendrik Mattern¹, Alessandro Sciarra^1,2, Max Dünnwald^2,3, Soumick Chatterjee^1,3,4, Ursula Müller¹, Steffen Oeltze-Jafra^2,5, and Oliver Speck^1,5,6,7

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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