The current standard photoacoustic (PA) technology is based on heavy, expensive and hazardous laser system for excitation of a tissue sample. As an alternative, light emitting diode (LED) offers safe, compact and inexpensive light source. However, the PA images of an LED-based system significantly suffer from low signal-to-noise-ratio due to limited LED-power. With an aim to improve the quality of PA images, in this work we propose to use deep convolutional neural networks that is built upon a previous state-of-the-art image enhancement approach. The key contribution is to improve the optimization of the network by guiding its feature extraction at different layers of the architecture. In addition to using a high quality target image at the output of the network, multiple target images with intermediate qualities are employed at in-betweens layers of the architecture to guide the feature extraction. We perform an end-to-end training of the network using a set of 4,536 low quality PA images from 24 experiments. On the test set from 15 experiments, we achieve a mean peak signal-to-noise ratio of 34.5 dB and a mean structural similarity index of 0.86 with a gain in the frame rate of 6 times compared to the conventional approach.
CITATION STYLE
Anas, E. M. A., Zhang, H. K., Kang, J., & Boctor, E. M. (2018). Towards a Fast and Safe LED-Based Photoacoustic Imaging Using Deep Convolutional Neural Network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11073 LNCS, pp. 159–167). Springer Verlag. https://doi.org/10.1007/978-3-030-00937-3_19
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