Abstract
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.
Highlights
Photoacoustic imaging (PAI) is a biomedical imaging technique that leverages the advantages of both ultrasound and optical imaging to non-invasively image vasculature and other optically absorbing targets [1,2]
Different reconstruction schemes including modified delay-and-sum techniques [11], filtered back-projection [12] and iterative approaches [13,14,15] have been developed to enhance the quality of the reconstructed images, it is still common to average the raw radio-frequency (RF) ultrasound data generated from multiple consecutive laser pulses, reducing stochastic noise and improving the signal-to-noise ratio (SNR) [16,17]
In this paper we propose a deep learning based method that is capable of reducing Gaussian background noise similar to averaging multiple acquisition frames, while simultaneously replicating more sophisticated denoising, in particular the singular value decomposition (SVD) denoising method of Hill et al [18], which we have previously found to be effective for our data [19]
Summary
Photoacoustic imaging (PAI) is a biomedical imaging technique that leverages the advantages of both ultrasound and optical imaging to non-invasively image vasculature and other optically absorbing targets [1,2]. Different reconstruction schemes including modified delay-and-sum techniques [11], filtered back-projection [12] and iterative approaches [13,14,15] have been developed to enhance the quality of the reconstructed images, it is still common to average the raw radio-frequency (RF) ultrasound data generated from multiple consecutive laser pulses, reducing stochastic noise and improving the signal-to-noise ratio (SNR) [16,17]. This approach increases the scan time proportionally to the number of frames acquired at each imaging position, lowering the frame rate of the system, but it increases the total laser dose. In this paper we propose a deep learning based method that is capable of reducing Gaussian background noise similar to averaging multiple acquisition frames, while simultaneously replicating more sophisticated denoising, in particular the SVD denoising method of Hill et al [18] , which we have previously found to be effective for our data [19]
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