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 degradation caused by incomplete measurement and heterogeneously distributed speed of sound.Method A deep learning method is proposed to reconstruct jointly images representing the distributions of optical absorption and speed of sound within the imaging domain from incomplete photoacoustic measurements.A convolutional neural network,named joint iterative reconstruction network(JIR-Net),is constructed based on an iterative learning strategy.Incomplete photoacoustic measurements are fed into the network,and images representing absorbed optical energy density and speed of sound distributions are output.The network consists of four structural units,and each unit is composed of three modules:feature extraction,feature fusion,and reconstruction.The feature extraction module extracts features from four inputs via convolution.The feature fusion module combines the features extracted from the input.Finally,the reconstruction module recreates the distributions of absorbed optical energy density and speed of sound.The network is trained using simulation,phantom,and in vivo datasets,where the gradient descent information of the absorbed optical energy density and the speed of sound distributions is incorporated into the network training.The nonlinear least square problem is solved by using the back propagation gradient descent.The validity of the method is demonstrated by simulation,phantom,and in vivo stud-ies.Compared with traditional nonlearning methods,non-iterative learning method,and learning iterative method based on depth gradient descent,JIR-Net is superior in reconstructing high-quality images from sparse data measured in acoustically heterogeneous media.Result Numerical simulation,phantom,and in vivo experiment results show the trained JIR-Net is robust to data sparsity and insensitive to the initial iterative plan.Moreover,the superiority of JIR-Net in complex structure reconstruction is proven in vivo.Compared with the depth gradient descent method,the U-Net post-processing method,and the alternate optimization method,the structural similarity of the reconstructed images representing absorbed optical energy density distribution can be improved by 7.6％,26.4％,and 39.5％,respectively,and the peak signal-to-noise ratio can be improved by 15.5％,71.4％,and 95.6％,respectively.Compared with the alternate optimization method,the structural similarity and peak signal-to-noise ratio of the JIR-Net reconstructed speed of sound images are increased by 34.4％and 22.6％,respectively.Conclusion JIR-Net achieves the mapping from incomplete photoacoustic measurements to high-quality images representing the distributions of absorbed optical energy density and speed of sound.The method can be used for image reconstruction from limited-view sparse photoacoustic signals collected with any measuring geometry in acoustic media with inhomogeneously distributed speed of sound.The method eliminates the need for prior knowledge of the characteristics of the imaging target and reduces the need for scanning and detection equipment,enabling constructing more compact imaging systems.
image reconstruction techniquesphotoacoustic tomography(PAT)deep learningabsorbed optical energy densityspeed of sound(SoS)joint reconstructiongradient descent
NETL
NSTL
万方数据
