High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects using Deep Neural Networks
Abstract
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 16, 2019
- Accession Number
- AD1104234
Entities
People
- Akintunde I. Akinwande
- Alexandre Goy
- George Barbastathis
- Girish Rughoobur
- Kwabena Arthur
- Shuai Li
Organizations
- Massachusetts Institute of Technology