Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.
journal contributionposted on 09.01.2020 by Samuel PX Davis, Sunil Kumar, Yuriy Alexandrov, Ajay Bhargava, Gabriela da Silva Xavier, Guy A Rutter, Paul Frankel, Erik Sahai, Seth Flaxman, Paul MW French, James McGinty
Any type of content formally published in an academic journal, usually following a peer-review process.
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.