ArticleJournal of biophotonics2019
Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.
Article in Journal of biophotonics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Optical projection tomography implemented for accessibility and low cost (Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Article
- Model-based deep learning framework for accelerated optical projection tomography.Scientific reports · 2023Article
- Optical projection tomography reconstruction with few views using highly-generalizable deep learning at sinogram domain.Biomedical optics express · 2023Article
- Self-supervised next view prediction for limited-angle optical projection tomography.Biomedical optics express · 2022Article
- Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography.Quantitative imaging in medicine and surgery · 2022Article
- Smad4 controls signaling robustness and morphogenesis by differentially contributing to the Nodal and BMP pathways.Nature communications · 2021Article
- Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.Journal of biophotonics · 2019Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.