# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 2 Materials and Methods
### Photoacoustic Data for Training and Testing  
Photoacoustic computed tomography is a novel medical imaging technique that utilizes the light absorption properties of tissues to generate images. The method works by identifying photoacoustic-induced initial pressure distributions in tissues. In these situations, light absorption raises local temperatures, which produces ultrasound waves and an initial pressure distribution [42]. Different tissues absorb light to different extents, and thus the initial pressure distributions vary, which affects the contrast of the PAI [43]. PAI of biological tissues mostly employs near-infrared (NIR) light, which can produce deeper penetration depth, a higher signal-to-noise ratio, and more contrast compared to those of typical visible-light biological imaging [44]. This helps to increase spatial resolution. The absorption coefficients of different tissues such as fat, muscle, and blood at different wavelengths have been extensively studied [45]. Therefore, the tissue structure and density information obtained using CT and MR scans can be used to define the initial pressure distribution in the PAI and then obtain the contrast of the photoacoustic image.  
Synthetic training and test data were created using k-Wave, a MATLAB toolbox for simulating photoacoustic wavefields [32]. The photoacoustic simulation in the k-Wave toolbox was implemented using a pseudo-spectral approach. The pseudo-spectral method is a numerical method for solving partial differential equations that improves efficiency in the spatial domain by fitting Fourier series to all the data in a global way, and is  
Figure 3: DeepLabV3+ network architecture.  
suitable for time-domain modeling of broadband or high-frequency waves in the field of acoustics [46]. For each image in the dataset, an initial photoacoustic source with a grid size of 256 \(\times\) 256 pixels is defined. The medium is assumed to be homogeneous, with a sound velocity of 1500 m/s and an attenuation coefficient of0.75 dB/(MHz-cm), similar to that of soft tissue in vivo [47; 48; 49]. The transducer arrays have 32, 64, and 128 equidistant sensors on a circle with a radius of 100 pixels for the reception of the photoacoustic waves. k-Wave's inbuilt functions are used to simulate photoacoustic pressure sampling. The image is then reconstructed from the simulated photoacoustic time-series data using the time-reversal method.  
The CHAOS dataset [50] is a comprehensive dataset for multimodal medical imaging of the liver. The dataset contains CT and MR scans from multiple healthy volunteers covering segmented annotations of abdominal organs such as liver, kidney, and spleen. The abdominal MRI image dataset with T2-Spectral Pre-Saturation Inversion Recovery (SPIR) sequence was selected for defining the initial photoacoustic stressor in k-Wave to create simulated PAI. It contains 632 T2-weighted abdominal images based on fat-suppressed pulse sequences acquisitions. Post-processing of the reconstructed sound field images, including filtering, denoising, interpolation, and other operations, was performed to create a simulated image dataset for deep-learning reconstruction of PAI.