Learning end-to-end inversion of circular Radon transforms in the partial radial setup
This addresses a computational bottleneck in photoacoustic tomography for medical imaging, though it appears incremental as it applies an existing deep learning architecture to a specific inversion problem.
The paper tackled the problem of inverting circular Radon transforms in photoacoustic tomography, where traditional methods cause severe artifacts, and proposed a deep learning-based algorithm using a ResBlock U-Net that shows superior performance with noisy data.
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.