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

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.