PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction.

ABSTRACT: For clinical translation of photoacoustic imaging cost-effective systems development is necessary. One approach is the use of fewer transducer elements and acquisition channels combined with sparse sampling. However, this approach introduces reconstruction artifacts that degrade image quality. While deep learning models such as U-net have shown promise in reconstructing images from limited data, they typically require retraining for each new system configuration, a process that demands more data and increased computational resources. In this work, we introduce PA OmniNet, a modified U-net model designed to generalize across different system configurations without the need for retraining. Instead of retraining, PA OmniNet adapts to a new system using only a small set of example images (betw

SUBMITTER: Stam OJM 

PROVIDER: S-EPMC12284555 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature