HDN：用于人脑经颅光声成像的混合深度学习与非视距重建框架
HDN: Hybrid Deep-Learning and Non-Line-of-Sight Reconstruction Framework for Transcranial Photoacoustic Imaging of Human Brain
AI总结：
针对经颅光声成像中超声波经颅骨导致信号畸变的问题，提出融合深度学习与非视距成像的HDN框架，通过信号提取与差异利用两部分实现重建，在模拟数据集上结构相似性显著优于传统及现有深度学习方法。
摘要：
光声成像结合了光学成像的高对比度与超声成像的深穿透深度，在脑血管疾病检测中展现出巨大潜力。然而，超声波穿过颅骨组织时会发生强衰减和多重散射，导致采集到的光声信号发生畸变。本文受深度学习与非视距成像原理的启发，提出了一种名为HDN（混合深度学习与非视距）的图像重建框架，该框架由信号提取部分和差异利用部分组成。信号提取部分用于校正畸变信号并重建初始图像。差异利用部分用于进一步利用畸变信号与校正信号之间的信号差异，重建初始图像与目标图像之间的残差图像。在光声数字脑模拟数据集上的测试结果表明，与传统方法（延迟求和法）和基于深度学习的方法（UNet）相比，HDN在信号校正和图像重建两方面均取得了更优的性能。具体到结构相似性指数，HDN的成像结果达到0.661，而延迟求和法为0.157，基于深度学习的方法为0.305。
英文摘要：
Photoacoustic imaging combines the high contrast of optical imaging with the deep penetration depth of ultrasonic imaging, showing great potential in cerebrovascular disease detection. However, the ultrasonic wave suffers strong attenuation and multi-scattering when it passes through the skull tissue, resulting in the distortion of the collected photoacoustic signal. In this paper, inspired by the principles of deep learning and non-line-of-sight imaging, we propose an image reconstruction framework named HDN (Hybrid Deep-learning and Non-line-of-sight), which consists of the signal extraction part and difference utilization part. The signal extraction part is used to correct the distorted signal and reconstruct an initial image. The difference utilization part is used to make further use of the signal difference between the distorted signal and corrected signal, reconstructing the residual image between the initial image and the target image. The test results on a photoacoustic digital brain simulation dataset show that compared with the traditional method (delay-and-sum) and deep-learning-based method (UNet), the HDN achieved superior performance in both signal correction and image reconstruction. Specifically for the structural similarity index, the HDN reached 0.661 in imaging results, compared to 0.157 for the delay-and-sum method and 0.305 for the deep-learning-based method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
