Learning end-to-end inversion of circular Radon transforms in the
  partial radial setup
Deep Ray Souvik Roy
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.