Despite its exceptional soft tissue contrast, Magnetic Resonance Imaging (MRI) faces the challenge of long scanning times compared to other modalities like X-ray radiography. Shortening scanning times is crucial in clinical settings, as it increases patient comfort, decreases examination costs and improves throughput. Recent advances in compressed sensing (CS) and deep learning allow accelerated MRI acquisition by reconstructing high-quality images from undersampled data. While reconstruction algorithms have received most of the focus, designing acquisition trajectories to optimize reconstruction quality remains an open question. This thesis explores two approaches to address this gap in the context of Cartesian MRI. First, we propose two algorithms, lazy LBCS and stochastic LBCS, that significantly improve upon G\"ozc\"u et al.'s greedy learning-based CS (LBCS) approach. These algorithms scale to large, clinically relevant scenarios like multi-coil 3D MR and dynamic MRI, previously inaccessible to LBCS. Additionally, we demonstrate that generative adversarial networks (GANs) can serve as a natural criterion for adaptive sampling by leveraging variance in the measurement domain to guide acquisition. Second, we delve into the underlying structures or assumptions that enable mask design algorithms to perform well in practice. Our experiments reveal that state-of-the-art deep reinforcement learning (RL) approaches, while capable of adaptation and long-horizon planning, offer only marginal improvements over stochastic LBCS, which is neither adaptive nor does long-term planning. Altogether, our findings suggest that stochastic LBCS and similar methods represent promising alternatives to deep RL. They shine in particular by their scalability and computational efficiency and could be key in the deployment of optimized acquisition trajectories in Cartesian MRI.

翻译：尽管磁共振成像（MRI）具有卓越的软组织对比度，但与X射线照相术等其他影像学方法相比，它仍面临扫描时间过长的挑战。缩短扫描时间在临床环境中至关重要，因为这能提升患者舒适度、降低检查成本并提高吞吐量。近年来，压缩感知（CS）和深度学习领域的进展通过从欠采样数据中重建高质量图像，实现了加速MRI采集。虽然重建算法得到了大部分关注，但如何设计采集轨迹以优化重建质量仍是一个未解问题。本论文探讨了两种弥补这一空白的方法，聚焦于笛卡尔MRI场景。首先，我们提出了两种算法——惰性LBCS和随机LBCS，它们显著改进了Gözcü等人基于贪心学习的CS（LBCS）方法。这些算法可扩展至大规模临床相关场景，如多线圈三维MRI和动态MRI，而此前LBCS无法处理此类问题。此外，我们证明生成对抗网络（GAN）可通过利用测量域中的方差指导采集，作为自适应采样的天然准则。其次，我们深入研究了支撑掩膜设计算法在实践中有良好表现的底层结构或假设。实验表明，尽管先进的深度强化学习（RL）方法具备自适应和长期规划能力，但相较于既非自适应也非长期规划的随机LBCS，其改进幅度极为有限。总体而言，我们的发现表明随机LBCS及类似方法有望成为深度RL的有效替代方案。它们尤其以可扩展性和计算效率见长，或将成为笛卡尔MRI中优化采集轨迹部署的关键技术。