Image reconstruction is an essential step of every medical imaging method, including Photoacoustic Tomography (PAT), which is a promising modality of imaging, that unites the benefits of both ultrasound and optical imaging methods. Reconstruction of PAT images using conventional methods results in rough artifacts, especially when applied directly to sparse PAT data. In recent years, generative adversarial networks (GANs) have shown a powerful performance in image generation as well as translation, rendering them a smart choice to be applied to reconstruction tasks. In this study, we proposed an end-to-end method called DensePANet to solve the problem of PAT image reconstruction from sparse data. The proposed model employs a novel modification of UNet in its generator, called FD-UNet++, which considerably improves the reconstruction performance. We evaluated the method on various in-vivo and simulated datasets. Quantitative and qualitative results show the better performance of our model over other prevalent deep learning techniques.

翻译：图像重建是每种医学成像方法的关键步骤，包括光声断层成像（PAT）——一种融合了超声和光学成像优势的有前景的成像模态。使用传统方法重建PAT图像会产生粗糙的伪影，尤其当直接应用于稀疏PAT数据时。近年来，生成对抗网络（GANs）在图像生成和翻译方面展现出强大性能，使其成为应用于重建任务的明智选择。在本研究中，我们提出了一种名为DensePANet的端到端方法，以解决稀疏数据的PAT图像重建问题。该模型在其生成器中采用了一种新颖的UNet改进结构——FD-UNet++，显著提升了重建性能。我们在各种体内和模拟数据集上评估了该方法。定量和定性结果表明，我们的模型优于其他主流深度学习技术。