2012年Science上最新的关于光声成像的综述，非常不错

去噪声代码matlab 有限数据光声层析成像的Singram超分辨率和去噪卷积神经网络（SRCN） Python代码：（需要使用Tensorflow作为后端的keras） #Python拟议的深层神经网络的实现以及训练例程：SRCNTrain.py＃用于重新训练网络现有模型的Python代码：SRCNRetrain.py＃用于测试模型的Python代码：SRCNTest.py #MATLAB函数可将噪声添加到PAT正弦图中：addNoise.m #MATLAB代码制作正弦图补丁以创建训练和测试数据集：makePatches.m #MATLAB函数内插singoram（最近内插）：generalTwiceInterpolate.m 此Python和MATLAB代码用作以下工作的一部分：Navchetan Awasthi，Rohit Pardasani，Sandeep Kumar Kalva，Manojit Pramanik和Phaneendra K. Yalavarthy“ Sinogram超分辨率和降噪卷积神经网络（SRCN），用于有限的数据光声断层扫描” 该代码没有任何保证，可以随意

Photoacoustic tomography (PAT) is an emerging biomedical imaging modality that combines the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging in a single modality. By listening to light, PAT detects tissue-absorbed photons ultrasonically through the photoacoustic effect. Since ultrasonic scattering is two to three orders of magnitude weaker than optical scattering in tissue, PAT breaks the 2–4 mm spatial resolution limit associated with pure optical tomography such as diffuse optical tomography (DOT) for deep tissue imaging, or the ~1 mm depth limit associated with confocal and multiphoton microscopy and optical coherent tomography (OCT). In PAT, tissue is excited with a short (typically a few nanoseconds) laser pulse (focused or unfocused); the subsequent laser-induced transient photoacoustic waves in the range of 1–100 MHz, due to the transient thermoelastic expansion of light-absorbing components in tissue, are detected by wideband unfocused or focused ultrasound transducer(s). Unique advantages of PAT are that functional or biochemical parameters such as deoxy-hemoglobin (HbR), oxy-hemoglobin (HbO2), water (H2O), lipids, and so forth along with vasculature and blood flow can be imaged in high resolution. In addition, highly specific molecular PAT can be realized through the use of molecular contrast agents. Finally, PAT can be made portable for bedside applications, is economical, and uses non-ionization radiation.