基于MRI的序列决策主动采样
Active Sampling for MRI-based Sequential Decision Making
AI总结：
本文提出了一种多目标强化学习框架，通过主动采样策略提升MRI在连续决策中的诊断性能，实现更高效、经济的点-of-care应用。
摘要：
尽管磁共振成像（MRI）具有优越的诊断能力，但将其作为点-of-care（PoC）设备的使用仍然受到高成本和复杂性的限制。为了通过降低磁场强度来实现这一未来目标，改进采样策略是一个关键方法。先前的工作表明，可以从k空间中用更少的样本直接做出诊断决策。此类工作表明可以做出单个诊断决策，但如果我们希望将MRI视为真正的PoC设备，则需要进行多个和连续的决策，同时最小化所获取的样本数量。我们提出了一种新的多目标强化学习框架，能够从欠采样的k空间数据中进行全面的连续诊断评估。我们的方法在推理过程中主动适应连续决策，以最优方式采样。为此，我们引入了一种训练方法，利用逐步加权奖励函数来识别对每个诊断目标贡献最大的样本。我们在两个连续的膝关节病理评估任务中评估了我们的方法：ACL撕裂检测和软骨厚度损失评估。我们的框架在疾病检测、严重程度量化和整体连续诊断方面实现了与各种基于策略的基准相当的诊断性能，同时显著减少了k空间样本的数量。我们的方法为MRI作为全面且经济的PoC设备的未来铺平了道路。我们的代码可在https://github.com/vios-s/MRI_Sequential_Active_Sampling公开获取。
英文摘要：
Despite the superior diagnostic capability of Magnetic Resonance Imaging (MRI), its use as a Point-of-Care (PoC) device remains limited by high cost and complexity. To enable such a future by reducing the magnetic field strength, one key approach will be to improve sampling strategies. Previous work has shown that it is possible to make diagnostic decisions directly from k-space with fewer samples. Such work shows that single diagnostic decisions can be made, but if we aspire to see MRI as a true PoC, multiple and sequential decisions are necessary while minimizing the number of samples acquired. We present a novel multi-objective reinforcement learning framework enabling comprehensive, sequential, diagnostic evaluation from undersampled k-space data. Our approach during inference actively adapts to sequential decisions to optimally sample. To achieve this, we introduce a training methodology that identifies the samples that contribute the best to each diagnostic objective using a step-wise weighting reward function. We evaluate our approach in two sequential knee pathology assessment tasks: ACL sprain detection and cartilage thickness loss assessment. Our framework achieves diagnostic performance competitive with various policy-based benchmarks on disease detection, severity quantification, and overall sequential diagnosis, while substantially saving k-space samples. Our approach paves the way for the future of MRI as a comprehensive and affordable PoC device. Our code is publicly available at https://github.com/vios-s/MRI_Sequential_Active_Sampling
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。
