mlsanityData to 7 Oct 2026

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

arXiv 1708.09832 ↗First posted 31 Aug 2017cs.CV cs.NE math.OCcited by 45

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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.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Aug 2017.

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Citing papers per year: 2021: 12; 2022: 7; 2023: 11; 2024: 8; 2025: 3; 2026: 4.

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  1. 2022Online Deep Equilibrium Learning for Regularization by Denoisingcited by 22
  2. 2021SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guaranteescited by 18
  3. 2021An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstructioncited by 16
  4. 2021Fourier Image Transformercited by 12
  5. 2021Graph Convolutional Networks for Model-Based Learning in Nonlinear Inverse Problemscited by 10
  6. 2021Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equationcited by 10
  7. 2023Moving beyond simulation: data-driven quantitative photoacoustic imaging using tissue-mimicking phantomscited by 10
  8. 2023Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysiscited by 10
  9. 2023Block Coordinate Plug-and-Play Methods for Blind Inverse Problemscited by 8
  10. 2021Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learningcited by 7
  11. 2021Deep Algorithm Unrolling for Biomedical Imagingcited by 6
  12. 2023Model-corrected learned primal-dual models for fast limited-view photoacoustic tomographycited by 6
33 more
  1. 2022Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomographycited by 5
  2. 2022CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mappingcited by 5
  3. 2021Unsupervised Knowledge-Transfer for Learned Image Reconstructioncited by 4
  4. 2022Deep network series for large-scale high-dynamic range imagingcited by 4
  5. 2023Inverse Problems with Learned Forward Operatorscited by 4
  6. 2024Sliding Gaussian ball adaptive growth (SlingBAG): point cloud-based iterative algorithm for large-scale 3D photoacoustic imagingcited by 4
  7. 2025Digital twins enable full-reference quality assessment of photoacoustic image reconstructionscited by 4
  8. 2021AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Datacited by 3
  9. 2022Memory Efficient Invertible Neural Networks for 3D Photoacoustic Imagingcited by 3
  10. 2023Score-based Generative Models for Photoacoustic Image Reconstruction with Rotation Consistency Constraintscited by 3
  11. 2024Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstructioncited by 3
  12. 2022On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detectorcited by 2
  13. 2021Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstructioncited by 1
  14. 2022Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networkscited by 1
  15. 2023Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodlescited by 1
  16. 2023Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imagingcited by 1
  17. 2024Diffusion assisted image reconstruction in optoacoustic tomographycited by 1
  18. 2024ADOBI: Adaptive Diffusion Bridge For Blind Inverse Problems with Application to MRI Reconstructioncited by 1
  19. 2025Why the noise model matters: A performance gap in learned regularizationcited by 1
  20. 2025Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometrycited by 1
  21. 2026Efficient Unrolled Networks for Large-Scale 3D Inverse Problemscited by 1
  22. 2021A Machine-learning Based Initialization for Joint Statistical Iterative Dual-energy CT with Application to Proton Therapycited by 0
  23. 2021Deep Learning Adapted Acceleration for Limited-view Photoacoustic Computed Tomographycited by 0
  24. 2023Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstructioncited by 0
  25. 2023MB-DECTNet: A Model-Based Unrolled Network for Accurate 3D DECT Reconstructioncited by 0
  26. 2023Learning end-to-end inversion of circular Radon transforms in the partial radial setupcited by 0
  27. 2024Design, Implementation and Analysis of a Compressed Sensing Photoacoustic Projection Imaging Systemcited by 0
  28. 2024Efficient Deep Model-Based Optoacoustic Image Reconstructioncited by 0
  29. 2024Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applicationscited by 0
  30. 2024Photoacoustic Iterative Optimization Algorithm with Shape Prior Regularizationcited by 0
  31. 2026Benchmarking Deep Learning-Based Reconstruction Methods for Photoacoustic Computed Tomography with Clinically Relevant Synthetic Datasetscited by 0
  32. 2026Deep Image Prior for photoacoustic tomography can mitigate limited-view artifactscited by 0
  33. 2026PAGS: Autofocusing Photoacoustic Tomography via Speed-of-Sound-Adaptive Gaussian Splattingcited by 0