Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström; Tanja Tarvainen

doi:10.1117/12.2670860

11 August 2023 Deep learning in photoacoustic tomography utilizing variational autoencoders

Teemu Sahlström, Tanja Tarvainen

Proceedings Volume 12631, Opto-Acoustic Methods and Applications in Biophotonics VI; 1263108 (2023) https://doi.org/10.1117/12.2670860
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

In Photoacoustic Tomography (PAT), the aim is to estimate the initial pressure distribution based on measured ultrasound data. While several approaches utilizing deep learning for PAT have been proposed, many of these do not provide estimates on the reliability of the reconstruction. In this work, we propose a deep learning approach for the Bayesian inverse problem for PAT based on the uncertainty quantification variational autoencoder. The approach enables simultaneous image reconstruction and reliability estimation.

Citation Download Citation

Teemu Sahlström and Tanja Tarvainen "Deep learning in photoacoustic tomography utilizing variational autoencoders", Proc. SPIE 12631, Opto-Acoustic Methods and Applications in Biophotonics VI, 1263108 (11 August 2023); https://doi.org/10.1117/12.2670860

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