Undersampling is a common method in Magnetic Resonance Imaging (MRI) to
subsample the number of data points in k-space and thereby reduce acquisition
times at the cost of decreased image quality. In this work, we directly learn
the undersampling masks to derive task- and domain-specific patterns. To solve
this discrete optimization challenge, we propose a general optimization routine
called ProM: A fully probabilistic, differentiable, versatile, and model-free
framework for mask optimization that enforces acceleration factors through a
convex constraint. Analyzing knee, brain, and cardiac MRI datasets with our
method, we discover that different anatomic regions reveal distinct optimal
undersampling masks. Furthermore, ProM can create undersampling masks that
maximize performance in downstream tasks like segmentation with networks
trained on fully-sampled MRIs. Even with extreme acceleration factors, ProM
yields reasonable performance while being more versatile than existing methods,
paving the way for data-driven all-purpose mask generation.

本文提出了一种名为 ProM 的方法，该方法可用于直接学习 MRI 下采样掩蔽模式。通过在 MRI 数据集上进行的实验，该方法显示出优越性能，可创建最大化下游任务性能的下采样掩蔽，并具有比现有方法更多的使命和域特定的模式。