Diffraction tomography with a deep image prior.

Published

Journal Article

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.

Full Text

Duke Authors

Cited Authors

  • Zhou, KC; Horstmeyer, R

Published Date

  • April 2020

Published In

Volume / Issue

  • 28 / 9

Start / End Page

  • 12872 - 12896

PubMed ID

  • 32403775

Pubmed Central ID

  • 32403775

Electronic International Standard Serial Number (EISSN)

  • 1094-4087

International Standard Serial Number (ISSN)

  • 1094-4087

Digital Object Identifier (DOI)

  • 10.1364/oe.379200

Language

  • eng