Local reconstruction analysis of inverting the Radon transform in the plane from noisy discrete data
<|reference_start|>Local reconstruction analysis of inverting the Radon transform in the plane from noisy discrete data: In this paper, we investigate the reconstruction error, $N_\e^{\text{rec}}(x)$, when a linear, filtered back-projection (FBP) algorithm is applied to noisy, discrete Radon transform data with sampling step size $\epsilon$ in two-dimensions. Specifically, we analyze $N_\e^{\text{rec}}(x)$ for $x$ in small, $O(\e)$-sized neighborhoods around a generic fixed point, $x_0$, in the plane, where the measurement noise values, $\eta_{k,j}$ (i.e., the errors in the sinogram space), are random variables. The latter are independent, but not necessarily identically distributed. We show, under suitable assumptions on the first three moments of the $\eta_{k,j}$, that the following limit exists: $N^{\text{rec}}(\chx;x_0) = \lim_{\e\to0}N_\e^{\text{rec}}(x_0+\e\chx)$, for $\check x$ in a bounded domain. Here, $N_\e^{\text{rec}}$ and $ N^{\text{rec}}$ are viewed as continuous random variables, and the limit is understood in the sense of distributions. Once the limit is established, we prove that $N^{\text{rec}}$ is a zero mean Gaussian random field and compute explicitly its covariance. In addition, we validate our theory using numerical simulations and pseudo random noise.<|reference_end|>
@article{abhishek2024local,
    title={Local reconstruction analysis of inverting the Radon transform in the
  plane from noisy discrete data},
    author={Anuj Abhishek, Alexander Katsevich, James W. Webber},
    journal={arXiv preprint arXiv:2403.12909},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12909},
    primaryClass={math.NA cs.NA}
}