用强化学习动态选采样点，让低场MRI实现连续诊断。
Active Sampling for MRI-based Sequential Decision Making
- 通过多目标强化学习动态决定每步该采哪部分k空间数据。
- 在膝关节韧带和软骨评估任务中，采样量减少超40%仍保持高诊断准确率。
- 适合希望降低MRI成本、做连续健康评估的研究者与临床医生。
尽管磁共振成像（MRI）具有出色的诊断能力，但其高昂成本和复杂性限制了其作为床旁设备的应用。为实现这一愿景，需降低磁场强度，关键在于改进采样策略。已有研究证明可从少样本k空间数据直接做出单次诊断决策，但若要使MRI真正成为床旁设备，必须支持多轮、连续的诊断决策，同时尽量减少采样数量。本文提出一种新型多目标强化学习框架，实现从欠采样k空间数据中进行综合、连续的诊断评估。推理阶段，该方法能主动适应连续决策需求，最优选择采样点。为此，我们引入了一种逐步加权奖励函数的训练方法，识别对各诊断目标贡献最大的采样点。我们在两个膝关节病理评估任务中进行验证：前交叉韧带拉伤检测和软骨厚度流失评估。结果表明，该框架在疾病检测、严重程度量化及整体序列诊断任务中，性能媲美多种基于策略的基准模型，同时显著减少k空间采样量。本方法为未来低成本、全面的MRI床旁设备奠定了基础。代码已开源：https://github.com/vios-s/MRI_Sequential_Active_Sampling
原文摘要 · Abstract (English)
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
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