用强化学习统一优化金属伪影抑制与加速成像采样策略。
MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRI
- 设计强化学习框架，让智能体在有限采样下自适应选择相位编码线。
- 在模拟数据上训练，使采样与伪影校正联合优化，提升重建质量。
- 适用于临床金属植入患者，可推广至真实MRI数据集。
金属植入物在磁共振成像中引发严重伪影，降低图像质量并影响临床诊断。传统方法将金属伪影抑制（MAR）与加速成像采集视为独立问题。本文提出MASC，一种统一的强化学习框架，联合优化金属感知k空间采样与伪影校正。为实现监督训练，构建基于物理模拟的配对MRI数据集，生成带金属与无金属植入的3D MRI扫描数据，每例金属污染样本均配有精确匹配的干净参考，支持伪影消除与采集策略学习的直接监督。将主动成像采集建模为序列决策问题，采用基于伪影感知的近端策略优化（PPO）智能体，在受限采集预算下选择k空间相位编码线。智能体基于U-Net结构的伪影校正网络处理的欠采样重建结果，学习最大化重建质量的模式。进一步提出端到端训练方案，使采集策略学习如何选择最利于伪影去除的采样模式，同时伪影校正网络同步适应实际采样模式。实验表明，MASC所学策略优于传统采样方法，且端到端训练性能高于使用预训练固定伪影校正网络的情况，验证了联合优化的有效性。在FastMRI数据集上通过物理模拟伪影进行跨数据集实验，进一步证实其在真实临床MRI数据上的泛化能力。MASC代码与模型已公开：https://github.com/hrlblab/masc
原文摘要 · Abstract (English)
Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis. Traditional approaches address metal artifact reduction (MAR) and accelerated MRI acquisition as separate problems. We propose MASC, a unified reinforcement learning framework that jointly optimizes metal-aware k-space sampling and artifact correction for accelerated MRI. To enable supervised training, we construct a paired MRI dataset using physics-based simulation, generating k-space data and reconstructions for phantoms with and without metal implants. This paired dataset provides simulated 3D MRI scans with and without metal implants, where each metal-corrupted sample has an exactly matched clean reference, enabling direct supervision for both artifact reduction and acquisition policy learning. We formulate active MRI acquisition as a sequential decision-making problem, where an artifact-aware Proximal Policy Optimization (PPO) agent learns to select k-space phase-encoding lines under a limited acquisition budget. The agent operates on undersampled reconstructions processed through a U-Net-based MAR network, learning patterns that maximize reconstruction quality. We further propose an end-to-end training scheme where the acquisition policy learns to select k-space lines that best support artifact removal while the MAR network simultaneously adapts to the resulting undersampling patterns. Experiments demonstrate that MASC's learned policies outperform conventional sampling strategies, and end-to-end training improves performance compared to using a frozen pre-trained MAR network, validating the benefit of joint optimization. Cross-dataset experiments on FastMRI with physics-based artifact simulation further confirm generalization to realistic clinical MRI data. The code and models of MASC have been made publicly available: https://github.com/hrlblab/masc
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