Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning

Abstract

Limited-angle tomography of strongly scattering quasi-transparent objects 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 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 better fit to regularize the reconstructions. We devised a recurrent neural network (RNN) architecture with a novel split-convolutional gated recurrent unit (SC-GRU) as the fundamental building block. Through comprehensive comparison of several quantitative metrics, we show that the dynamic method improves upon previous static approaches with fewer artifacts and better overall reconstruction fidelity.

12 pages, 7 figures, 2 tables

References in corpus (4)

• Phase recovery and holographic image reconstruction using deep learning in neural networks
• Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
• On the interplay between physical and content priors in deep learning for computational imaging
• Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

Cited by in corpus (5)

• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• Adaptive 3D descattering with a dynamic synthesis network
• Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
• Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
• Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime