Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish. Issue 12 (29th August 2019) - Davis, Samuel P. X.; Kumar, Sunil; Alexandrov, Yuriy; Bhargava, Ajay; da Silva Xavier, Gabriela; Rutter, Guy A.; Frankel, Paul; Sahai, Erik; Flaxman, Seth; French, Paul M. W.; McGinty, James
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 compressed sensing 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. Abstract : Optical projection tomography is typically used for 3D imaging of fixed cleared tissue or whole organisms. in vivo applications require faster imaging, which is realized by acquiring less image data and reconstructing the tomographic images using iterative compressive sensing algorithms. However, these algorithms can be prohibitively slow for systematic studies. Instead, a neural network can reconstruct images much faster, with improved quality, and can be trained on ex vivo data.