A reinforcement learning approach for optimized MRI sampling with region-specific fidelity

Abstract

Accelerating Magnetic Resonance Imaging (MRI) acquisition while preserving diagnostic quality remains a significant challenge in medical imaging. This paper introduces a novel reinforcement learning approach for optimizing k-space sampling, addressing the critical need for maintaining high-fidelity reconstructions, particularly in clinically significant regions. Our method bridges k-space and image domains by employing a multi-layer Fast Fourier Transform (FFT) network architecture coupled with a comprehensive reward function. The reward function integrates global SSIM, region-specific metrics, and k-space MSE, achieving SSIM scores of 0.9764 (global) and PSNR of 43.427 in cardiac imaging, with a Dice score of 0.9606 for critical regions. This function uniquely balances global image fidelity with region-specific accuracy, ensuring optimal sampling in areas of high diagnostic value. We present a reinforcement learning framework that maintains consistency with MRI physics throughout the optimization process by utilizing Proximal Policy Optimization (PPO) for concurrent refinement of policy and value networks, while minimizing unnecessary domain transformations. We validate our approach on two diverse datasets: the ACDC cardiac MRI and the FastMRI knee dataset, demonstrating its effectiveness across different regions of clinical interest and showing consistent improvements across 4x and 6x acceleration factors. The proposed k-space sampling optimization technique not only enhances reconstruction quality but also serves as a versatile front-end tool, adaptable to various MRI reconstruction algorithms. The code is publicly available at https://github.com/Ruru-Xu/KSRO