mlsanityData to 7 Oct 2026

Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography

arXiv 2204.06270 ↗First posted 13 Apr 2022physics.comp-ph stat.MLcited by 5

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Abstract (arXiv)

There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 11 of its 49 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

11 of 49 references.

References · 11

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
  4. 2017Model based learning for accelerated, limited-view 3D photoacoustic tomographycited by 45
  5. 2019Solving Bayesian Inverse Problems via Variational Autoencoderscited by 32
  6. 2020Deep Learning in Photoacoustic Tomography: Current approaches and future directionscited by 25
  7. 2020Deep learning for biomedical photoacoustic imaging: A reviewcited by 21
  8. 2017Deep Learning for Photoacoustic Tomography from Sparse Datacited by 17
  9. 2019A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentationcited by 10
  10. 2019Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approachcited by 5
  11. 2021Model reduction in acoustic inversion by artificial neural networkcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2023Monte Carlo guided Diffusion for Bayesian linear inverse problemscited by 65
  2. 2023Bayesian ECG reconstruction using denoising diffusion generative modelscited by 2
  3. 2026How well do generative models solve inverse problems? A benchmark studycited by 2
  4. 2024Diffusion assisted image reconstruction in optoacoustic tomographycited by 1
  5. 2025Briding Diffusion Posterior Sampling and Monte Carlo methods: a surveycited by 0