Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed fully dense UNet (FD-UNet) for removing artifacts from two-dimensional PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.

中文翻译：

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用于2D稀疏光声层析成像伪影的Fully Dense UNet

光声成像是一种基于光声效应的新兴成像方式。在光声层析成像（PAT）中，感应声波由一系列检测器测量，并用于重建初始压力分布的图像。PAT面临的一个共同挑战是，只能稀疏地采样所测量的声波。使用标准方法重建稀疏采样的数据会导致严重的伪影，从而模糊图像中的信息。我们提出了一种改进的卷积神经网络（CNN）架构，称为全密度UNet（FD-UNet），用于从稀疏数据重构的二维PAT图像中消除伪影，并在重构的图像质量方面将拟议的CNN与标准UNet进行比较。