JIR-Net:用于光声层析图像重建的联合迭代重建网络  

JIR-Net:a joint iterative reconstruction network forphotoacoustic tomography image reconstruction

作　　者：候英飒 孙正[1,2] 孙美晨 Hou Yingsa;Sun Zheng;Sun Meichen(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding 071003,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学电子与通信工程系,保定071003 [2]华北电力大学河北省电力物联网技术重点实验室,保定071003

出　　处：《中国图象图形学报》2024年第3期823-838,共16页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62071181)。

摘　　要：目的 高质量的图像重建是光声层析成像(photoacoustic tomography,PAT)技术的关键,有限角度稀疏测量和组织非均匀的声学特性都会影响重建图像质量。采用迭代重建技术可在一定程度上提高图像质量,但是其结果依赖于有关成像目标的先验假设模型。而且在迭代优化过程中需要反复计算前向成像算子及其伴随算子,因此计算成本较高,需要合理选择正则化方法及其参数。为了解决该问题,提出一种根据不完备光声测量信号联合重建光吸收能量分布图和声速分布图的深度学习方法。方法 设计并搭建基于学习迭代策略的联合迭代重建网络(joint iterative reconstruction network,JIR-Net),网络由4个结构单元组成,每个单元包括特征提取、特征融合和重建3个模块。网络的输入是探测器在成像平面中采集的不完备光声信号和预设的常数声速,输出是重建的光吸收能量分布图和声速分布图。分别构建仿真、仿体和在体数据集,用于训练、验证和测试网络。在训练网络的过程中,将光吸收能量密度和声速的梯度下降信息整合到网络训练中,并利用反向传播梯度下降法求解非线性最小二乘问题。结果 数值仿真、仿体和在体实验结果表明:与交替优化法、U-Net后处理法和深度梯度下降法相比,JIR-Net重建的光吸收能量分布图的结构相似度可分别提高约39.5%、26.4%和7.6%,峰值信噪比可分别提高约95.6%、71.4%和15.5%。与交替优化法相比,JIR-Net重建的声速分布图的结构相似度和峰值信噪比可分别提高约34.4%和22.6%。结论 JIR-Net解决了由于有限角度稀疏测量和组织声速分布不均匀所致的光声图像质量下降问题,实现了从光声信号到高质量光吸收能量分布图和声速分布图的映射。Objective Photoacoustic tomography(PAT)is a hybrid functional imaging modality developed rapidly in recent years.PAT is physically based on the photoacoustic effect,where biological tissues are irradiated by short laser pulses,inducing broadband(~MHz)ultrasonic waves(i.e.,photoacoustic waves)due to optical absorption and thermoelastic expansion.Ultrasonic transducers deployed around the imaging target collect photoacoustic waves from which images are reconstructed to show the morphological structure and functional properties of tissues.High-quality image reconstruction is essential for PAT,which suffers from incomplete measurements and heterogeneous acoustic properties of tissues.Tradi⁃tional image reconstruction methods include back projection,time reversal,Fourier transform-based reconstruction,and delay and sum.For simplicity,these methods are usually based on ideal assumptions about the imaging scenario,such as fixed speed of sound,a lossless acoustic media without attenuation,a point-like ultrasonic detector with sufficient band⁃width,and complete measurement.However,in real-world applications,these ideal scenarios often do not occur,leading to the degradation of the quality of images reconstructed using these methods.The model-based iterative reconstruction scheme is commonly used to improve image quality,where the inversion of a forward imaging model describing the genera⁃tion of photoacoustic signal is iteratively solved.However,its real-time applications are limited by its high computational cost because the forward imaging operator and its adjoint operator need to be calculated repeatedly in the iterative process.Regularization tools with properly defined parameters are necessary to obtain stable optimization.In addition,the recon⁃struction quality highly depends on the prior assumptions of the imaging object.In recent years,deep learning has shown great potential in reconstructing high-quality images from photoacoustic measurements.This work aims to solve the problem of image quality degradatio

关 键 词：图像重建技术 光声层析成像(PAT) 深度学习 光吸收能量密度 声速(SoS) 联合重建 梯度下降 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]