Reconstructing undersampled photoacoustic microscopy images using deep
learning
- URL: http://arxiv.org/abs/2006.00251v1
- Date: Sat, 30 May 2020 12:39:52 GMT
- Title: Reconstructing undersampled photoacoustic microscopy images using deep
learning
- Authors: Anthony DiSpirito III, Daiwei Li, Tri Vu, Maomao Chen, Dong Zhang,
Jianwen Luo, Roarke Horstmeyer, and Junjie Yao
- Abstract summary: We propose a novel application of deep learning principles to reconstruct undersampled PAM images.
Our results have collectively demonstrated the robust performance of our model to reconstruct PAM images with as few as 2% of the original pixels.
- Score: 11.74890470096844
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One primary technical challenge in photoacoustic microscopy (PAM) is the
necessary compromise between spatial resolution and imaging speed. In this
study, we propose a novel application of deep learning principles to
reconstruct undersampled PAM images and transcend the trade-off between spatial
resolution and imaging speed. We compared various convolutional neural network
(CNN) architectures, and selected a fully dense U-net (FD U-net) model that
produced the best results. To mimic various undersampling conditions in
practice, we artificially downsampled fully-sampled PAM images of mouse brain
vasculature at different ratios. This allowed us to not only definitively
establish the ground truth, but also train and test our deep learning model at
various imaging conditions. Our results and numerical analysis have
collectively demonstrated the robust performance of our model to reconstruct
PAM images with as few as 2% of the original pixels, which may effectively
shorten the imaging time without substantially sacrificing the image quality.
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