CT scans without X-rays: parallel-beam imaging from nonlinear current flows

作     者：Alsaker, Melody Rautio, Siiri Moura, Fernando Agnelli, Juan Pablo Murthy, Rashmi Lassas, Matti Mueller, Jennifer L. Siltanen, Samuli 

作者机构：Department of Mathematics Gonzaga University SpokaneWA99258 United States Department of Mathematics and Statistics University of Helsinki Helsinki Finland Engineering Modeling and Applied Social Sciences Center Federal University of ABC São Paulo Brazil FaMAF National University of Córdoba Córdoba Argentina  Argentina Department of Mathematics Bangalore University Bangalore India Department of Mathematics School of Biomedical Engineering Colorado State University Fort CollinsCO80521 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2024年

核心收录：

主　　题：Computerized tomography 

摘      要：Parallel-beam X-ray computed tomography (CT) and electrical impedance tomography (EIT) are two imaging modalities which stem from completely different underlying physics, and for decades have been thought to have little in common either practically or mathematically. CT is only mildly ill-posed and uses straight X-rays as measurement energy, which admits simple linear mathematics. However, CT relies on exposing targets to ionizing radiation and requires cumbersome setups with expensive equipment. In contrast, EIT uses harmless electrical currents as measurement energy and can be implemented using simple low-cost portable setups. But EIT is burdened by nonlinearity stemming from the curved paths of electrical currents, as well as extreme ill-posedness which causes characteristic low spatial resolution. In practical EIT reconstruction methods, nonlinearity and ill-posedness have been considered intertwined in a complicated fashion. In this work we demonstrate a surprising connection between CT and EIT which partly unravels the main problems of EIT and leads directly to a proposed imaging modality which we call virtual hybrid parallel-beam tomography (VHPT). We show that hidden deep within EIT data is information which possesses the same linear geometry as parallel-beam CT data. This admits a fundamental restructuring of EIT, separating ill-posedness and nonlinearity into simple modular sub-problems, and yields virtual radiographs and CT-like images which reveal previously concealed information. Furthermore, as proof of concept we present VHPT images of real-world objects. © 2024, CC BY.