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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 Baye...
A Bayesian inference framework using Sylvester normalizing flows (SNFs) to approximate posterior distributions over metabolite concentrations, enhancing quantification reliability, and a physics-based decoder incorporates prior knowledge of MRS signal formation, ensuring realistic distribution representations.
The conditional neural manifold is proposed, which replaces the fixed manifold with an observation-conditioned mapping from source parameters to steering vectors, and resolves the angle-frequency ambiguity inherent to the nominal spatial manifold.
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