Medical image reconstruction methods based on deep learning have recently demonstrated powerful performance in photoacoustic tomography (PAT) from limited-view and sparse data. However, because most of these methods must utilize conventional linear reconstruction methods to implement signal-to-image transformations, their performance is restricted. In this paper, we propose a novel deep learning reconstruction approach that integrates appropriate data pre-processing and training strategies. The Feature Projection Network (FPnet) presented herein is designed to learn this signal-to-image transformation through data-driven learning rather than through direct use of linear reconstruction. To further improve reconstruction results, our method integrates an image post-processing network (U-net). Experiments show that the proposed method can achieve high reconstruction quality from limited-view data with sparse measurements. When employing GPU acceleration, this method can achieve a reconstruction speed of 15 frames per second.

最近，基于深度学习的医学图像重建方法从有限的视野和稀疏数据中展示了在光声层析成像（PAT）中的强大性能。但是，由于这些方法中的大多数必须利用常规的线性重建方法来实现信号到图像的转换，因此它们的性能受到限制。在本文中，我们提出了一种新颖的深度学习重建方法，该方法整合了适当的数据预处理和训练策略。本文介绍的特征投影网络（FPnet）旨在通过数据驱动的学习而不是直接使用线性重构来学习这种信号到图像的转换。为了进一步提高重建效果，我们的方法集成了图像后处理网络（U-net）。实验表明，所提出的方法能够从稀疏测量的有限视角数据中获得较高的重建质量。当采用GPU加速时，此方法可以实现每秒15帧的重建速度。