作为诊断工具的MRI扫描仪：无图像主动采样
The MRI Scanner as a Diagnostic: Image-less Active Sampling
AI总结：
针对MRI作为床旁诊断工具的可及性难题，提出基于强化学习的患者级主动采样框架，无需图像重建即可从欠采样k空间直接诊断疾病，性能接近全采样诊断，有望降低磁场要求、提升床旁MRI可行性。
摘要：
尽管磁共振成像（MRI）具有较高的诊断准确率，但由于高磁场强度和较长的采集时间，将MRI用作床旁（POC）疾病识别工具在可及性方面面临重大挑战。我们提出一个简单的问题：我们能否在不考虑图像重建的情况下，根据（自动化的）下游决策任务，在患者层面动态优化采集的样本？我们提出了一种基于机器学习（ML）的框架，该框架通过强化学习在患者层面学习主动采样策略，直接从欠采样k空间推断疾病。我们通过在欠采样膝关节MRI数据中推断半月板撕裂（Meniscus Tear）来验证我们的方法，其诊断性能与使用全采样k空间数据的基于ML的诊断相当。我们分析了任务特定的采样策略，展示了我们主动采样方法的适应性。所提出的节俭采样策略有望降低高磁场强度要求，进而增强基于MRI的POC疾病识别及相关初步筛查工具的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
