发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出深度贝叶斯REFoCUS，将超声多静态恢复建模为贝叶斯推断，利用深度生成先验应对秩亏缺，优于线性基线并表达不确定性，且对分布偏移具有强泛化能力。
AI 中文摘要
在这项工作中，我们将任意发射序列下的超声多静态恢复问题表述为贝叶斯推断问题。为此，我们在多静态数据集上训练一个深度生成先验，以应对经典线性REFoCUS解码器失效的秩亏缺情形。这种我们称之为深度贝叶斯REFoCUS的方法，在所有秩亏缺程度和噪声水平下均优于线性基线，并且在反演精确时回归到线性解码。该模型还表达了采集零空间中的不确定性，而线性REFoCUS解码器仅提供点估计。最后，我们分析了模拟与体内采集之间分布偏移的影响，显示出无需任何微调或自适应即可实现显著泛化能力。
英文摘要
In this work we formulate ultrasound multistatic recovery from arbitrary transmit sequences as a Bayesian inference problem. To that end, we train a deep generative prior on multistatic data sets to tackle the rank-deficient regime in which classical linear REFoCUS decoders fail. This appproach, which we term Deep Bayesian REFoCUS, outperforms the linear baselines for all regimes of rank-deficiency and noise levels, and regresses to linear decoding when inversion is exact. The model also expresses uncertainty in the null space of the acquisitions, whereas the linear REFoCUS decoders only provide point estimates. Finally, we analyze the impact of distribution shift between simulation and in-vivo acquisitions, showing remarkable generalization ability without any fine-tuning or adaptation.
