Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views
Name
s41377-021-00512-x.pdf
Description
Published version
Size
3.1 MB
Format
Unknown
Checksum (MD5)
7a2453da7b9d2328423fdb2307bd4a7c
Author(s)
Kang, Iksung
Goy, Alexandre Sydney Robert
Barbastathis, George
Date Issued
2021
Journal
Light: Science & Applications
Publisher
Springer Science and Business Media LLC
Citation
Kang, Iksung, Goy, Alexandre and Barbastathis, George. 2021. "Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views." Light: Science & Applications, 10 (1).
Version
Final published version
Abstract
Limited-angle tomography of an interior volume is a challenging, highly ill-posed problem with practical implications in medical and biological imaging, manufacturing, automation, and environmental and food security. Regularizing priors are necessary to reduce artifacts by improving the condition of such problems. Recently, it was shown that one effective way to learn the priors for strongly scattering yet highly structured 3D objects, e.g. layered and Manhattan, is by a static neural network [Goy et al. Proc. Natl. Acad. Sci. 116, 19848–19856 (2019)]. Here, we present a radically different approach where the collection of raw images from multiple angles is viewed analogously to a dynamical system driven by the object-dependent forward scattering operator. The sequence index in the angle of illumination plays the role of discrete time in the dynamical system analogy. Thus, the imaging problem turns into a problem of nonlinear system identification, which also suggests dynamical learning as a better fit to regularize the reconstructions. We devised a Recurrent Neural Network (RNN) architecture with a novel Separable-Convolution Gated Recurrent Unit (SC-GRU) as the fundamental building block. Through a comprehensive comparison of several quantitative metrics, we show that the dynamic method is suitable for a generic interior-volumetric reconstruction under a limited-angle scheme. We show that this approach accurately reconstructs volume interiors under two conditions: weak scattering, when the Radon transform approximation is applicable and the forward operator well defined; and strong scattering, which is nonlinear with respect to the 3D refractive index distribution and includes uncertainty in the forward operator.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41377-021-00512-X
