Physics-assisted Generative Adversarial Network for X-Ray Tomography

TL;DR

This paper introduces PGAN, a physics-assisted generative adversarial network that improves X-ray tomography reconstruction by integrating physics-based regularization with learned priors, enabling high-quality imaging with fewer photons.

Contribution

The paper presents a novel PGAN framework that combines physics-based maximum likelihood estimates with deep learning priors for improved tomographic reconstruction.

Findings

Reduces photon requirements for accurate reconstruction

Achieves high-quality images with limited projection angles

Enables potential low-photon nanoscale imaging

Abstract

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.