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Deep Bayesian REFoCUS applies deep generative priors to solve rank-deficient ultrasound imaging problems where classical linear methods fail, offering superior performance across noise levels and distribution shifts. By framing multistatic recovery as a Bayesian inference problem, the model provides uncertainty estimates in null spaces that point-estimate decoders cannot, demonstrating strong generalization without fine-tuning.
Deep Bayesian REFoCUS
arXiv:2610.03419v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2610.03419 [cs.LG]
(or arXiv:2610.03419v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.03419 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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