In the area of magnetic resonance imaging (MRI), an extensive range of
non-linear reconstruction algorithms have been proposed that can be used with
general Fourier subsampling patterns. However, the design of these subsampling
patterns has typically been considered in isolation from the reconstruction
rule and the anatomy under consideration. In this paper, we propose a
learning-based framework for optimizing MRI subsampling patterns for a specific
reconstruction rule and anatomy, considering both the noiseless and noisy
settings. Our learning algorithm has access to a representative set of training
signals, and searches for a sampling pattern that performs well on average for
the signals in this set. We present a novel parameter-free greedy mask
selection method, and show it to be effective for a variety of reconstruction
rules and performance metrics. Moreover we also support our numerical findings
by providing a rigorous justification of our framework via statistical learning
theory.

这篇论文介绍了一种基于学习的框架，用于优化针对特定重建规则和解剖学的 MRI 子采样模式，在噪声和无噪声情况下，通过访问代表性的训练信号集合，并搜索一种在该集合中的平均表现良好的采样模式，我们提出了一种参数自由的贪婪掩模选择方法，并通过统计学习理论严谨地证明了该框架的正确性。