Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography
arXiv:2204.06270 · doi:10.1137/22M1489897
Abstract
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.
References in corpus (16)
- Generative Adversarial Networks
- Normalizing Flows: An Introduction and Review of Current Methods
- Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal
- Model based learning for accelerated, limited-view 3D photoacoustic tomography
- Deep learning for biomedical photoacoustic imaging: A review
- Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
- Deep Learning in Photoacoustic Tomography: Current approaches and future directions
- Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
- A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography
- Uniqueness of reconstruction and an inversion procedure for thermoacoustic and photoacoustic tomography
- Universal inversion formulas for recovering a function from spherical means
- A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentation
- Accelerating Image Reconstruction in Three-Dimensional Optoacoustic Tomography on Graphics Processing Units
- Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties
- Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach
- Model reduction in acoustic inversion by artificial neural network