Despite the high diagnostic accuracy of Magnetic Resonance Imaging (MRI),
using MRI as a Point-of-Care (POC) disease identification tool poses
significant accessibility challenges due to the use of high magnetic field
strength and lengthy acquisition times. We ask a simple question: Can we
dynamically optimise acquired samples, at the patient level, according to an
(automated) downstream decision task, while discounting image reconstruction?
We propose an ML-based framework that learns an active sampling strategy, via
reinforcement learning, at a patient-level to directly infer disease from
undersampled k-space. We validate our approach by inferring Meniscus Tear in
undersampled knee MRI data, where we achieve diagnostic performance comparable
with ML-based diagnosis, using fully sampled k-space data. We analyse
task-specific sampling policies, showcasing the adaptability of our active
sampling approach. The introduced frugal sampling strategies have the potential
to reduce high field strength requirements that in turn strengthen the
viability of MRI-based POC disease identification and associated preliminary
screening tools.

通过基于机器学习的强化学习方法，在病人级别上学习一个主动采样策略，通过对被欠采样的 k 空间进行直接推断，本研究验证了在膝关节 MRI 数据中，可以获得与基于机器学习诊断使用全采样 k 空间数据相媲美的诊断性能，同时分析了任务特定的采样策略，展示了主动采样方法的适应性。引入的经济采样策略有潜力降低高磁场强度需求，从而增强基于 MRI 的 POC 疾病识别和相关初步筛查工具的可行性。