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.

中文翻译：

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使用生成对抗网络对预波束形成的光声数据进行去噪

我们训练了生成对抗网络 (GAN) 来模拟时间平均和奇异值分解 (SVD) 去噪的效果。这有效地消除了噪声和采集伪影，并提高了射频 (RF) 数据和相应光声重建中的信噪比 (SNR)。该方法允许单帧采集而不是平均多帧，显着减少了扫描时间和总激光剂量。我们已经在实验数据上测试了这种方法，并量化了对 RF 数据和重建图像分别使用 SVD 去噪或帧平均的改进。我们实现了 0.05% 的均方误差 (MSE)、0.78 的结构相似性指数度量 (SSIM) 和 0 的特征相似性指数度量 (FSIM)。85 与我们的真实射频结果相比。在使用去噪数据的后续重建中，与地面实况重建相比，我们实现了 0.05% 的 MSE、0.80 的 SSIM 和 0.80 的 FSIM。