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

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...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Olivier J.M. Stam, Kalloor Joseph Francis, Navchetan Awasthi
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2025-10-01
Sarja:	Photoacoustics
Aiheet:	Photoacoustic imaging Sparse sampling Deep learning U-net Retraining-free Generalizable
Linkit:	http://www.sciencedirect.com/science/article/pii/S2213597925000631
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