用贝叶斯方法动态选择超声扫描线，提升重建质量并实现实时成像。
Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging
- 基于流模型的后验推理，实时评估每条扫描线的信息价值。
- 相比均匀与随机采样，重建误差降低15%，帧率更高。
- 适合需要高速、低能耗超声成像的临床实时应用。
超声图像通常通过逐条扫描线的束波转向采集形成。减少所需扫描线数量可显著提升帧率、视野范围、能效及数据传输速度。现有方法多采用静态子采样结合稀疏性或深度学习重建。本文提出一种自适应子采样方法，在线最大化内在信息增益，利用Sylvester归一化流编码器在部分观测下实时推断近似贝叶斯后验。结合该后验与深度生成模型对后续观测的预测，确定使当前采样观测与下一帧视频间互信息最大化的子采样策略。在EchoNet心脏超声视频数据集上验证，所提主动采样方法优于均匀、变密度随机采样及等距扫描线基线方法，平均绝对重建误差降低15%。后验推断与采样策略生成仅需0.015秒（66Hz），满足2D超声实时成像需求。
原文摘要 · Abstract (English)
Ultrasound images are commonly formed by sequential acquisition of beam-steered scan-lines. Minimizing the number of required scan-lines can significantly enhance frame rate, field of view, energy efficiency, and data transfer speeds. Existing approaches typically use static subsampling schemes in combination with sparsity-based or, more recently, deep-learning-based recovery. In this work, we introduce an adaptive subsampling method that maximizes intrinsic information gain in-situ, employing a Sylvester Normalizing Flow encoder to infer an approximate Bayesian posterior under partial observation in real-time. Using the Bayesian posterior and a deep generative model for future observations, we determine the subsampling scheme that maximizes the mutual information between the subsampled observations, and the next frame of the video. We evaluate our approach using the EchoNet cardiac ultrasound video dataset and demonstrate that our active sampling method outperforms competitive baselines, including uniform and variable-density random sampling, as well as equidistantly spaced scan-lines, improving mean absolute reconstruction error by 15%. Moreover, posterior inference and the sampling scheme generation are performed in just 0.015 seconds (66Hz), making it fast enough for real-time 2D ultrasound imaging applications.
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