Adaptive k-space Radial Sampling for Cardiac MRI with Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based framework for optimizing radial k-space sampling trajectories in cardiac MRI, enhancing image quality and acquisition efficiency through a dual-branch architecture and anatomically-aware rewards.

Contribution

It presents the first RL approach for non-Cartesian k-space sampling optimization in cardiac MRI, incorporating a novel dual-branch network and reward design for improved sampling strategies.

Findings

Improved reconstruction quality over conventional methods.

Effective learning of sampling strategies across multiple acceleration factors.

Enhanced k-space coverage and cardiac detail preservation.

Abstract

Accelerated Magnetic Resonance Imaging (MRI) requires careful optimization of k-space sampling patterns to balance acquisition speed and image quality. While recent advances in deep learning have shown promise in optimizing Cartesian sampling, the potential of reinforcement learning (RL) for non-Cartesian trajectory optimization remains largely unexplored. In this work, we present a novel RL framework for optimizing radial sampling trajectories in cardiac MRI. Our approach features a dual-branch architecture that jointly processes k-space and image-domain information, incorporating a cross-attention fusion mechanism to facilitate effective information exchange between domains. The framework employs an anatomically-aware reward design and a golden-ratio sampling strategy to ensure uniform k-space coverage while preserving cardiac structural details. Experimental results demonstrate that our method effectively learns optimal radial sampling strategies across multiple acceleration factors, achieving improved reconstruction quality compared to conventional approaches. Code available: https://github.com/Ruru-Xu/RL-kspace-Radial-Sampling