Physics-assisted generative adversarial network for X-ray tomography

Author:

Guo Zhen1ORCID,Song Jung Ki1,Barbastathis George12,Glinsky Michael E.3,Vaughan Courtenay T.3,Larson Kurt W.3,Alpert Bradley K.4,Levine Zachary H.4ORCID

Affiliation:

1. Massachusetts Institute of Technology

2. Singapore-MIT Alliance for Research and Technology (SMART) Centre

3. Sandia National Laboratories

4. National Institute of Standards and Technology

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.

Funder

Intelligence Advanced Research Projects Activity

National Nuclear Security Administration

National Research Foundation Singapore

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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