论文标题
深度学习通过单个数据采集渠道启用了实时光声断层扫描系统
Deep Learning Enabled Real-Time Photoacoustic Tomography System via Single Data Acquisition Channel
论文作者
论文摘要
光声计算机断层扫描（PACT）结合了光学成像的光学对比度和超声检查的渗透性。在这项工作中，我们开发了一种新的PACT系统来提供实时成像，这是通过仅使用单个数据采集（DAQ）通道的120元素超声阵列实现的。为了减少DAQ的通道数，我们将附近的30个通道的信号叠加在一起，并在模拟域中缩小到4个数据通道（120/30 = 4）。此外，在输入单渠道DAQ之前，设计了一个四对一的延迟线模块，将这四个通道的数据组合到一个通道中，然后在数据采集后解耦信号。为了重建来自四个叠加的30个通道信号的图像，我们训练一个专门的深度学习模型来重建最终的PA映像。在本文中，我们介绍了幻影和体内实验的初步结果，这些结果表现出了其强大的实时成像性能。这个新颖的协议系统的意义在于，它大大降低了多通道DAQ模块的成本（从120个通道到1通道），铺平了通往便携式，低成本和实时契约系统的方式。
Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels' signals together in the analog domain, and shrinking to 4 channels of data (120/30=4). Furthermore, a four-to-one delay-line module is designed to combine these four channels' data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels'PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.
