论文标题
深度学习改善了低氟光声成像中的对比度
Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging
论文作者
论文摘要
低通量照明来源可以促进光声成像的临床过渡，因为它们是坚固，便携，负担得起和安全的。但是，这些来源还由于其低频而降低了图像质量。在这里，我们提出了一种使用多级小波横向横向神经网络的非固定方法，以将低通量照明源图像映射到其相应的高频激发图。定量和定性结果表明，消除背景噪声并保留目标结构的重要潜力。分别观察到PSNR，SSIM和CNR指标的实质性改进至2.20、2.25和4.3倍。我们还观察到使用我们建议的方法在体内应用中增强了对比度（最多1.76倍）。我们建议该工具可以提高光声成像中此类源的价值。
Low fluence illumination sources can facilitate clinical transition of photoacoustic imaging because they are rugged, portable, affordable, and safe. However, these sources also decrease image quality due to their low fluence. Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map. Quantitative and qualitative results show a significant potential to remove the background noise and preserve the structures of target. Substantial improvements up to 2.20, 2.25, and 4.3-fold for PSNR, SSIM, and CNR metrics were observed, respectively. We also observed enhanced contrast (up to 1.76-fold) in an in vivo application using our proposed methods. We suggest that this tool can improve the value of such sources in photoacoustic imaging.
