按频率层次主动采样，提升加速MRI的诊断准确性。
Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI
- 分层设计采样策略，低频保全结构，高频精准选点。
- 4倍加速下膝关节韧带诊断准确率媲美全采样，严重度评估提升20.4点AUC。
- 适合需要高精度诊断的临床MRI加速场景。
加速MRI的主动采样需在承载不同信息的空间频率间合理分配采样预算：低频包含主要解剖上下文，高频则决定病理判断细节。现有方法或对高低频一视同仁，或仅能整行采样，导致高加速下性能下降。本文提出HieraSample，一种任务驱动的分层框架：采用余弦退火课程，将加速比从20x逐步降至4x，每一步保持完整低频盘；基于Mamba的策略网络从双疾病与严重度分类器提取特征，选取高频频点。奖励函数为每次动作后类别加权交叉熵的降低量，正奖励直接对应更自信的正确预测。在fastMRI+膝关节基准上，HieraSample在4x至10x加速下实现与全采样理想模型相当的ACL诊断性能，且在ACL严重度评估上相较近期笛卡尔基线提升最多达20.4 AUC点。
原文摘要 · Abstract (English)
Active sampling for accelerated MRI must distribute a tight sampling budget across spatial frequencies that carry very different kinds of information. Low frequencies hold most of the anatomical context; high frequencies carry the fine details that drive pathology assessment. Existing active samplers either treat both regions identically or restrict the action space to entire Cartesian rows, which forces a poor compromise at high acceleration. We propose HieraSample, a task-driven framework built around this hierarchy. A cosine-annealed curriculum lowers the acceleration factor from 20x to 4x across 80 acquisition steps while keeping a fully-sampled low-frequency disk at every step; a Mamba-based policy then picks individual high-frequency coordinates from features extracted by dual disease and severity classifiers. The reward is the per-sample reduction in class-weighted cross-entropy after each action, so a positive reward corresponds directly to a more confident correct prediction. On the fastMRI+ knee benchmark, HieraSample matches the fully-sampled oracle on ACL diagnosis from 4x to 10x acceleration, and improves on a recent Cartesian baseline by as much as 20.4 AUC points on ACL severity.
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