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

翻译：尽管磁共振成像（MRI）具有卓越的诊断能力，但其作为即时诊断设备的应用仍受限于高成本和复杂性。为通过降低磁场强度实现这一愿景，改进采样策略将成为关键途径。先前研究表明，直接从k空间以较少样本进行诊断决策是可行的。这类工作证明了单一诊断决策的可能性，但若要将MRI视为真正的即时诊断设备，则需要在最小化采样数量的同时实现多重序贯决策。本文提出一种新颖的多目标强化学习框架，能够从欠采样k空间数据中实现全面、序贯的诊断评估。我们的方法在推理过程中主动适应序贯决策以实现最优采样。为此，我们引入一种训练方法，通过逐步加权奖励函数识别对每个诊断目标贡献最大的样本。我们在两个序贯膝关节病理评估任务中验证了该方法：前交叉韧带扭伤检测和软骨厚度损失评估。该框架在疾病检测、严重程度量化和整体序贯诊断方面取得了与多种基于策略的基准方法相当的诊断性能，同时显著节省了k空间样本。我们的研究为MRI成为全面且经济实惠的即时诊断设备奠定了基础。代码已公开于https://github.com/vios-s/MRI_Sequential_Active_Sampling