Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.

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Title: Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.
Authors: Davis, SPX
Kumar, S
Alexandrov, Y
Bhargava, A
Da Silva Xavier, G
Rutter, GA
Frankel, P
Sahai, E
Flaxman, S
French, PMW
McGinty, J
Item Type: Journal Article
Abstract: Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using the Radon transform, as is typically implemented with optically cleared samples of the mm-to-cm scale. For in vivo imaging, considerations of phototoxicity and the need to maintain animals under anesthesia typically preclude the acquisition of OPT data at a sufficient number of angles to avoid artifacts in the reconstructed images. For sparse samples, this can be addressed with iterative algorithms to reconstruct 3D images from undersampled OPT data, but the data processing times present a significant challenge for studies imaging multiple animals. We show here that convolutional neural networks (CNN) can be used in place of iterative algorithms to remove artifacts - reducing processing time for an undersampled in vivo zebrafish dataset from 77 to 15 minutes. We also show that using CNN produces reconstructions of equivalent quality to CS with 40% fewer projections. We further show that diverse training data classes, for example ex vivo mouse tissue data, can be used for CNN-based reconstructions of OPT data of other species including live zebrafish. This article is protected by copyright. All rights reserved.
Issue Date: 6-Aug-2019
Date of Acceptance: 4-Aug-2019
ISSN: 1864-063X
Publisher: Wiley-VCH Verlag
Journal / Book Title: Journal of Biophotonics
Copyright Statement: © 2019 The Authors. Journal of Biophotonics published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the Creative Commons Attribution License (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Medical Research Council (MRC)
Funder's Grant Number: MR/K011561/1
Keywords: neural networks
optical tomography
preclinical imaging
neural networks
optical tomography
preclinical imaging
Optoelectronics & Photonics
Publication Status: Published online
Conference Place: Germany
Online Publication Date: 2019-08-06
Appears in Collections:Physics
Faculty of Medicine
Faculty of Natural Sciences

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