E-Unet: a deep learning method for photoacoustic signal enhancement

Deepika Aggrawal; Mohsin Zafar; Md Tarikul Islam; Rayyan Manwar; Dan Schonfeld; Kamran Avanaki

doi:10.1117/12.2651217

9 March 2023 E-Unet: a deep learning method for photoacoustic signal enhancement

Deepika Aggrawal, Mohsin Zafar, Md Tarikul Islam, Rayyan Manwar, Dan Schonfeld, Kamran Avanaki

Proceedings Volume 12379, Photons Plus Ultrasound: Imaging and Sensing 2023; 123790X (2023) https://doi.org/10.1117/12.2651217
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

We developed a deep learning algorithm, called enhancement Unet (E-Unet), to improve the signal-to-noise ratio (SNR) of signals acquired in a photoacoustic computed microscopy (PAM) system. We tried various combination of custom loss functions which included peak-amplitude, peak-position and mean-squared signal value with Adam optimizer for training purposes. For the testing purposes, we acquired PAM data with complicated phantoms in biological tissue. The performance of the improved signals is evaluated in terms of SNR, structural similarity index (SSIM), root mean square error (RMSE) and Pearson correlation.

Citation Download Citation

Deepika Aggrawal, Mohsin Zafar, Md Tarikul Islam, Rayyan Manwar, Dan Schonfeld, and Kamran Avanaki "E-Unet: a deep learning method for photoacoustic signal enhancement", Proc. SPIE 12379, Photons Plus Ultrasound: Imaging and Sensing 2023, 123790X (9 March 2023); https://doi.org/10.1117/12.2651217

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