Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning

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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 can effectively shorten the imaging time without substantially sacrificing the image quality.
Funding Information
  • National Institutes of Health (R01 EB028143, R01 NS111039, R01 NS115581, R21 EB027304, R43 CA243822, R43 CA239830, R44 HL138185)
  • Duke MEDx Basic Science Grant
  • Duke Center for Genomic and Computational Biology Faculty Research Grant
  • Duke Institute of Brain Science Incubator Award
  • American Heart Association Collaborative Sciences (18CSA34080277)

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