Open AccessDenoising of pre-beamformed photoacoustic data using generative adversarial networks
Author(s) -
Amir Refaee,
Corey J. Kelly,
Hamid Moradi,
Septimiu E. Salcudean
Publication year - 2021
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.431997
Subject(s) - ground truth , noise reduction , artificial intelligence , singular value decomposition , mean squared error , computer science , similarity (geometry) , pattern recognition (psychology) , noise (video) , frame (networking) , signal to noise ratio (imaging) , computer vision , mathematics , image (mathematics) , statistics , telecommunications
We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.