Photoacoustic imaging (PAI) is a noninvasive emerging imaging method based on the photoacoustic effect, which provides necessary assistance for medical diagnosis. It has the characteristics of large imaging depth and high contrast. However, limited by the equipment cost and reconstruction time requirements, the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed. In this paper, a triple-path feature transform network (TFT-Net) for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data. Specifically, the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data, and takes the photoacoustic physical model as a prior information to guide the reconstruction process. In addition, to enhance the ability of extracting signal features, the residual block and squeeze and excitation block are introduced into the TFT-Net. For further efficient reconstruction, the final output of photoacoustic signals uses ‘filter-then-upsample’ operation with a pixel-shuffle multiplexer and a max out module. Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly, reduce background noise, and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

Radiation-induced acoustic computed tomography (RACT) is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues. Traditional back-projection (BP) reconstructions carry noisy and limited-view artifacts. Model-based algorithms have been demonstrated to overcome the drawbacks of BPs. However, model-based algorithms are relatively more complex to develop and computationally demanding. Furthermore, while a plethora of novel algorithms has been developed over the past decade, most of these algorithms are either not accessible, readily available, or hard to implement for researchers who are not well versed in programming. We developed a user-friendly MATLAB-based graphical user interface (GUI; RACT2D) that facilitates back-projection and model-based image reconstructions for two-dimensional RACT problems. We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI. The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer, thus further accelerating the reconstruction speed. We also share the MATLAB-based codes for evaluating RACT reconstructions, which users with MATLAB programming expertise can further modify to suit their needs. The shared GUI and codes can be of interest to researchers across the globe and assist them in efficient evaluation of improved RACT reconstructions.Radiation-induced acoustic computed tomography (RACT) is an evolving biomedical imaging modality that aims to reconstruct the radiation energy deposition in tissues. Traditional back-projection (BP) reconstructions carry noisy and limited-view artifacts. Model-based algorithms have been demonstrated to overcome the drawbacks of BPs. However, model-based algorithms are relatively more complex to develop and computationally demanding. Furthermore, while a plethora of novel algorithms has been developed over the past decade, most of these algorithms are either not accessible, readily available, or hard to implement for researchers who are not well versed in programming. We developed a user-friendly MATLAB-based graphical user interface (GUI; RACT2D) that facilitates back-projection and model-based image reconstructions for two-dimensional RACT problems. We included numerical and experimental X-ray-induced acoustic datasets to demonstrate the capabilities of the GUI. The developed algorithms support parallel computing for evaluating reconstructions using the cores of the computer, thus further accelerating the reconstruction speed. We also share the MATLAB-based codes for evaluating RACT reconstructions, which users with MATLAB programming expertise can further modify to suit their needs. The shared GUI and codes can be of interest to researchers across the globe and assist them in efficient evaluation of improved RACT reconstructions.

为了实现对飞秒光丝内部空间结构特征的精细化描述，通过对光丝诱导形成超声信号的正向传播过程进行精细化模拟，然后采用通用反投影算法（UBP）、延迟叠加算法（DAS）和超优光声非负重构算法（SPANNER）等多种光声层析图像重建算法进行反向重建，理论验证了利用多元线性阵列探测的方式重建飞秒单丝和多丝轴向r-z截面图像的可行性。结果表明，当探测距离为3 mm时，单丝和多丝诱导形成的超声信号最大频率约为5 MHz；光声层析法能够较为准确地实现对单丝位置、r-z截面轮廓等信息的反演，但是不同图像重建算法重建效果差异较大。UBP重建算法对单丝的重建存在较为明显的伪影现象；DAS重建算法由于受到“有限孔径效应”的影响，高估了光丝的直径；SPANNER重建算法由于使用最优理论来改进非线性共轭梯度算子，实现了非负性和各向异性总变分正则化，可有效避免噪声干扰，因而对多丝图像的重建效果最好。该研究结果对于揭示光丝结构特征和促进基于光丝的大气应用研究具有一定的参考价值。

光声层析成像是一种非侵入式的医学成像技术，与其他成像方法相比具备诸多优势，可以为肿瘤早期诊断提供新的成像思路。对光声信号的分析与去噪能提高成像系统的信噪比（SNR）和成像质量。为此，提出了一种针对光声信号的智能去噪算法。首先，利用自适应白噪声完备集合经验模态分解完成光声信号的分解；其次，采用小波阈值去噪方法完成对特定模态光声信号的高频去噪；最后，利用K奇异值分解对预处理后的光声信号进行稀疏重构，实现光声信号的智能去噪。仿真和实验结果表明，所提算法在SNR和均方根误差（RMSE）等方面相比于其他去噪算法均有改善，可以有效去除三维肿瘤仿体光声重建图像中的噪点与伪影，并保留图像的边缘信息。所提智能去噪算法能根据含噪光声信号的特征自适应地去噪，达到更好的去噪效果，可以作为一种成像前的辅助手段应用于光声成像领域。

光声计算断层成像(PACT)是近年来迅速发展的一种无损生物医学成像技术，在生物医学领域有着较高的应用价值。为了获得高质量的光声图像，成像系统的信号采集装置需要配备高密度的阵列探测器。但在实际应用中，由于经济成本、制造工艺及成像时间等因素的限制，探测器的排布往往较为稀疏，难以实现稳定重建，导致重建图像中出现条纹伪影。为了解决这一问题，本文提出一种基于双域神经网络的PACT图像重建算法。该算法主要包含三个模块：数据域网络、反投影层和图像域网络，其中数据域网络和图像域网络可分别对光声数据和光声图像进行增强，以提升图像质量。为了对网络进行训练和测试，构建了一个血管仿真数据集和一个小鼠活体试验数据集。研究结果表明，所提算法可以有效地抑制条纹伪影，提升图像质量，并且重建性能优于其他重建算法。

在光声层析成像重建方法中,当考虑超声换能器特别是大晶面超声换能器的尺寸影响时,经典的光声模型重建算法需将晶面离散化,通过分别计算每个离散点的正向模型并求和来建立最终的正向模型矩阵。这种基于模型的重建方法已被证明比传统的延迟叠加和反投影重建方法具有更高的精确性,但同时也会导致巨大的时间消耗和计算/存储空间。本文提出了一种基于虚拟平行投影的光声模型重建方法,该方法用直接建立虚拟平行投影的模型矩阵来代替晶面离散点模型矩阵之和,适用于使用大晶面柱形聚焦或平面超声换能器的光声层析成像系统。数值模拟、仿体实验及活体实验结果表明,所提方法与基于离散化晶面的光声模型重建方法具有相似的图像重建性能,但其重建过程的时间消耗和计算/存储空间需求显著降低。