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.

通过使用改良版的 UNet 生成器（称为 FD-UNet++），我们提出了一种名为 DensePANet 的端到端方法，以解决从稀疏数据中重建 PAT 图像的问题。在各种体内和模拟数据集上评估的数量和质量结果显示了我们模型相对于其他普遍的深度学习技术的更好性能。