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.

通过压缩感知和重建算法，优化磁共振成像 (MRI) 的采样轨迹在临床设置中具有潜在作用，提高采样速度并减少成本。