mlsanityData to 14 Oct 2026

Learning end-to-end inversion of circular Radon transforms in the partial radial setup

arXiv 2308.14144 ↗First posted 27 Aug 2023cs.LG eess.IV math-phcited by 0

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Abstract (arXiv)

We present a deep learning-based computational algorithm for inversion of circular Radon transforms in the partial radial setup, arising in photoacoustic tomography. We first demonstrate that the truncated singular value decomposition-based method, which is the only traditional algorithm available to solve this problem, leads to severe artifacts which renders the reconstructed field as unusable. With the objective of overcoming this computational bottleneck, we train a ResBlock based U-Net to recover the inferred field that directly operates on the measured data. Numerical results with augmented Shepp-Logan phantoms, in the presence of noisy full and limited view data, demonstrate the superiority of the proposed algorithm.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 27 Aug 2023.

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17 of 47 references.

References · 17

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  9. 2017Model based learning for accelerated, limited-view 3D photoacoustic tomographycited by 45
  10. 2021End-to-end reconstruction meets data-driven regularization for inverse problemscited by 32
  11. 2021Equivariant neural networks for inverse problemscited by 29
  12. 2021Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priorscited by 29
5 more
  1. 2020Deep Learning in Photoacoustic Tomography: Current approaches and future directionscited by 25
  2. 2021Learning Regularization Parameters of Inverse Problems via Deep Neural Networkscited by 19
  3. 2017Deep Learning for Photoacoustic Tomography from Sparse Datacited by 17
  4. 2022The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problemscited by 15
  5. 2019A Partially Learned Algorithm for Joint Photoacoustic Reconstruction and Segmentationcited by 10

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