Tong Tong, Huang Wenhui, Wang Kun, He Zicong, Yin Lin, Yang Xin, Zhang Shuixing, Tian Jie
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Photoacoustics. 2020 May 21;19:100190. doi: 10.1016/j.pacs.2020.100190. eCollection 2020 Sep.
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.
基于深度学习的医学图像重建方法最近在有限视角和稀疏数据的光声断层成像（PAT）中展现出强大性能。然而，由于这些方法大多必须利用传统线性重建方法来实现信号到图像的转换，其性能受到限制。在本文中，我们提出了一种新颖的深度学习重建方法，该方法集成了适当的数据预处理和训练策略。本文提出的特征投影网络（FPnet）旨在通过数据驱动学习而非直接使用线性重建来学习这种信号到图像的转换。为了进一步提高重建结果，我们的方法集成了一个图像后处理网络（U-net）。实验表明，所提出的方法能够从具有稀疏测量的有限视角数据中实现高重建质量。在采用GPU加速时，该方法能够达到每秒15帧的重建速度。
