根据量化任务动态选扫描位置，大幅降低超声功耗与数据量
Task-Based Adaptive Transmit Beamforming for Efficient Ultrasound Quantification
- 基于任务信息增益自适应选择扫描区域，减少冗余发射
- 仅用2%常规扫描线即可准确恢复心室尺寸
- 适合可重构超声设备与低功耗连续监测场景
无线可穿戴超声设备有望实现持续超声监测，但功耗和数据吞吐量仍是关键挑战。每秒减少发射次数可直接缓解这些问题。本文提出一种基于任务的自适应发射波束成形方法，将其建模为贝叶斯主动感知问题，通过自适应选择扫描位置来获取下游定量测量所需信息，避免冗余发射。所提出的任务型信息增益（TBIG）策略适用于任意可微分的下游任务函数。在从超声心动图中恢复心室尺寸的任务中，TBIG仅需不到2%的常规扫描线即可获得准确结果，展现出显著降低监测所需功耗与数据率的潜力。代码已开源：https://github.com/tue-bmd/task-based-ulsa。
原文摘要 · Abstract (English)
Wireless and wearable ultrasound devices promise to enable continuous ultrasound monitoring, but power consumption and data throughput remain critical challenges. Reducing the number of transmit events per second directly impacts both. We propose a task-based adaptive transmit beamforming method, formulated as a Bayesian active perception problem, that adaptively chooses where to scan in order to gain information about downstream quantitative measurements, avoiding redundant transmit events. Our proposed Task-Based Information Gain (TBIG) strategy applies to any differentiable downstream task function. When applied to recovering ventricular dimensions from echocardiograms, TBIG recovers accurate results using fewer than 2% of scan lines typically used, showing potential for large reductions in the power usage and data rates necessary for monitoring. Code is available at https://github.com/tue-bmd/task-based-ulsa.
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