# Model based learning for accelerated, limited-view 3D photoacoustic tomography

arXiv: https://arxiv.org/abs/1708.09832 · HTML: https://mlsanity.com/paper/1708.09832/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 30 of 45)

- [Online Deep Equilibrium Learning for Regularization by Denoising](https://mlsanity.com/paper/2205.13051/) (2022)
- [SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees](https://mlsanity.com/paper/2101.09379/) (2021)
- [An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction](https://mlsanity.com/paper/2111.11926/) (2021)
- [Fourier Image Transformer](https://mlsanity.com/paper/2104.02555/) (2021)
- [Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis](https://mlsanity.com/paper/2310.00133/) (2023)
- [Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problems](https://mlsanity.com/paper/2103.15138/) (2021)
- [Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation](https://mlsanity.com/paper/2108.09374/) (2021)
- [Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms](https://mlsanity.com/paper/2306.06748/) (2023)
- [Block Coordinate Plug-and-Play Methods for Blind Inverse Problems](https://mlsanity.com/paper/2305.12672/) (2023)
- [Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning](https://mlsanity.com/paper/2108.10163/) (2021)
- [Deep Algorithm Unrolling for Biomedical Imaging](https://mlsanity.com/paper/2108.06637/) (2021)
- [Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography](https://mlsanity.com/paper/2304.01963/) (2023)
- [Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography](https://mlsanity.com/paper/2204.06270/) (2022)
- [CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping](https://mlsanity.com/paper/2210.06330/) (2022)
- [Unsupervised Knowledge-Transfer for Learned Image Reconstruction](https://mlsanity.com/paper/2107.02572/) (2021)
- [Deep network series for large-scale high-dynamic range imaging](https://mlsanity.com/paper/2210.16060/) (2022)
- [Inverse Problems with Learned Forward Operators](https://mlsanity.com/paper/2311.12528/) (2023)
- [Sliding Gaussian ball adaptive growth (SlingBAG): point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging](https://mlsanity.com/paper/2407.11781/) (2024)
- [Digital twins enable full-reference quality assessment of photoacoustic image reconstructions](https://mlsanity.com/paper/2505.24514/) (2025)
- [AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data](https://mlsanity.com/paper/2101.08934/) (2021)
- [Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imaging](https://mlsanity.com/paper/2204.11850/) (2022)
- [Score-based Generative Models for Photoacoustic Image Reconstruction with Rotation Consistency Constraints](https://mlsanity.com/paper/2306.13843/) (2023)
- [Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstruction](https://mlsanity.com/paper/2410.10836/) (2024)
- [On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector](https://mlsanity.com/paper/2204.10001/) (2022)
- [Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction](https://mlsanity.com/paper/2104.03130/) (2021)
- [Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks](https://mlsanity.com/paper/2203.02166/) (2022)
- [Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles](https://mlsanity.com/paper/2305.01165/) (2023)
- [Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imaging](https://mlsanity.com/paper/2306.11034/) (2023)
- [Diffusion assisted image reconstruction in optoacoustic tomography](https://mlsanity.com/paper/2404.10239/) (2024)
- [ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstruction](https://mlsanity.com/paper/2411.16535/) (2024)

## Abstract (arXiv)

Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data.