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High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects using Deep Neural Networks

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Journal Article - Open Access

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Massachusetts Institute of Technology, Mechanical Engineering, 3D Optics Laboratory Cambridge United States

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We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to plus-minus sign 10 degrees. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network DNN conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

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  • Computer Systems

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