用于诊断性MRI的频率分层主动k空间采样
Frequency-Hierarchical Active k-Space Sampling for Diagnostic MRI
- Istanbul Technical University（伊斯坦布尔技术大学）
- University of Toronto（多伦多大学）
- Sunnybrook Research Institute（桑尼布鲁克研究所）
- KTH Royal Institute of Technology（皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对MRI加速采样中不同频率信息处理难题，提出HieraSample框架，通过余弦退火课程和基于Mamba的策略，在不同加速倍数下实现高效采样，在fastMRI+膝盖基准测试中提升了ACL诊断性能。
AI中文摘要：
加速MRI的主动采样必须在携带非常不同类型信息的空间频率上分配紧凑的采样预算。低频包含大部分解剖背景；高频携带驱动病理评估的精细细节。现有主动采样器要么对两个区域一视同仁，要么将动作空间限制在整个笛卡尔行上，这在高加速时会导致糟糕的折衷。我们提出了HieraSample，一个围绕这种分层构建的任务驱动框架。一个余弦退火课程在80个采集步骤中将加速因子从20倍降低到4倍，同时在每个步骤保持一个全采样的低频盘；然后基于Mamba的策略从双疾病和严重程度分类器提取的特征中选择单个高频坐标。奖励是每次动作后类加权交叉熵的每样本减少量，因此正奖励直接对应于更自信的正确预测。在fastMRI+膝盖基准测试中，HieraSample在4倍到10倍加速下的ACL诊断方面与全采样预言机匹配，并在ACL严重程度方面比最近的笛卡尔基线提高了多达20.4个AUC点。
英文摘要：
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.
