In this paper, we introduce silhouette tomography, a novel formulation of X-ray computed tomography that relies only on the geometry of the imaging system. We formulate silhouette tomography mathematically and provide a simple method for obtaining a particular solution to the problem, assuming that any solution exists. We then propose a supervised reconstruction approach that uses a deep neural network to solve the silhouette tomography problem. We present experimental results on a synthetic dataset that demonstrate the effectiveness of the proposed method.
@article{arxiv.2402.07298,
title = {Supervised Reconstruction for Silhouette Tomography},
author = {Evan Bell and Michael T. McCann and Marc Klasky},
journal= {arXiv preprint arXiv:2402.07298},
year = {2024}
}