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Abstract #2453

Adaptive k-Space Sampling in Magnetic Resonance Imaging Using Reinforcement Learning

Edith Franziska Baader1, Fabian Theißen1, Nicola Pezzotti2,3, and Volkmar Schulz1,4,5,6
1Department of Physics of Molecular Imaging Systems, Institute for Experimental Molecular Imaging, RWTH Aachen University, Aachen, Germany, 2Philips Research, Eindhoven, Netherlands, 3Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands, 4Physics Institute III B, RWTH Aachen University, Aachen, Germany, 5Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany, 6Hyperion Hybrid Imaging Systems GmbH, Aachen, Germany


One approach to accelerate MRI scans is to acquire fewer k-space samples. Commonly, the sampling pattern is selected before the scan, ignoring the sequential nature of the sampling process. A field of machine learning addressing sequential decision processes is reinforcement learning (RL). We present an approach for creating adaptive two-dimensional (2D) k-space trajectories using RL and the so-called action space shaping. The trained RL algorithm adapts to a variety of basic 2D shapes outperforming simple baseline trajectories. By shaping the action space of the RL agent we achieve better generalization and interpretability of the agent.

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