基于密集网络的质子热声信号走时提取

针对临床上由质子热声信号脉宽和信噪比的不确定性引起的走时提取困难问题,提出了一种基于密集网络的走时提取算法.该算法使用密集块代替传统卷积块,融合了具有不同感受野的特征,并引入了深度监督和网络剪枝机制,利用标记好的质子束热声信号数据进行学习,以提取所需的走时信息.实验结果表明,相比其他算法,该算法对质子热声信号走时的提取具有较高的准确率和鲁棒性,同时展现了实时提取的可行性.

Proton Thermoacoustic Signal Travel Time Extraction Based on Dense Network

Aiming at the difficulty in extracting travel time caused by the uncertainty of the pulse width and signal-to-noise ratio of proton thermoacoustic signals in clinic, a travel time extraction algorithm based on dense network is proposed. The algorithm uses dense blocks instead of traditional convolutional blocks, combines features with different receptive fields, and introduces deep supervision and network pruning mechanisms. It uses labeled proton beam thermoacoustic signal data for learning to extract the required time information. The experimental results show that, compared with other algorithms, the proposed algorithm has higher accuracy and robustness for the extraction of proton thermoacoustic signal travel time, and shows the feasibility of real-time extraction.