Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
- Imperial College London
- The Francis Crick Institute, London
- Department of Medicine, Imperial College, London
- Institute of Cardiovascular Science, Division of Medicine, University College London, London, UK.
- Department of Mathematics and Data Science Institute, Imperial College London, London, UK.
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.
|Journal||Journal of Biophotonics|
|Early online date||6 Aug 2019|
|Publication status||Published - 29 Aug 2019|
- neural networks, optical tomography, preclinical imaging