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

Deep Ray; Souvik Roy

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arXiv · 2308.14144

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

Deep Ray·Souvik Roy

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Abstract

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.

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Deep Ray, Souvik Roy. 2023-08-27. Learning end-to-end inversion of circular Radon transforms in the partial radial setup. https://arxiv.org/abs/2308.14144

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