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Abstract #0228

A unified model for simultaneous reconstruction and R2* mapping of accelerated 7T data using the Recurrent Inference Machine

Chaoping Zhang1, Dirk Poot2, Bram Coolen1, Hugo Vrenken3, Pierre-Louis Bazin4,5, Birte Forstmann4, and Matthan W.A. Caan1
1Biomedical Engineering & Physics, Amsterdam UMC, Amsterdam, Netherlands, 2Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, Netherlands, 3Radiology, Amsterdam UMC, Amsterdam, Netherlands, 4Integrative Model-based Cognitive Neuroscience research unit, University of Amsterdam, Amsterdam, Netherlands, 5Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Quantitative MRI often relies on the acquisition of multiple images with different scan settings. Therefore, data redundancy can be exploited to further accelerate imaging by deep learning. We propose a unified model for joint reconstruction and $$$R_2^*$$$-mapping from sparse data and embed this in a Recurrent Inference Machine, an iterative inverse problem solving network. Applied to high-resolution multi-echo gradient echo data of a cohort study covering the entire adult life span, the error in $$$R_2^*$$$ significantly decreases. With increasing acceleration factor, an increasing reduction in error is observed, pointing to a larger benefit for sparser data.

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