## 1 Introduction
However, traditional PA imaging algorithms assume that the SOS is uniform in porous bone tissue, and this uniform assumption of SOS for bone tissue leads to calculation errors and a following distortion for reconstructed images [40]. In addition, due to the limitation of experimental conditions and time costs, the highly under-sampled data, which is caused by a sparse array of detector points, cannot meet the requirement of artifact-free imaging advised by [PERSON]'s sampling theory [41]. Approaches to the above problems have been discussed recently [42; 43; 44; 45]. [PERSON] et al. elaborated an iterative reconstruction method, estimating the velocity distribution to reduce the effects of acoustic heterogeneity, while the iterative method is implemented at the cost of a formidable computational burden [42]. A new method called ultrasonic transmission tomography (UTT) was proposed to correct acoustic speed variations. However, the UTT algorithm assumes that ultrasound pulses travel in straight lines through the target, it may be invalid to restore the tissue of great acoustic speed variations like bones [43]. [PERSON] et al. investigated a heuristic method for reconstructing weakly scattering object, but it is limited at a request of a complete set of PAT measurement data [44]. [PERSON] et al. utilized a matrix filtering method, successfully recovering the PA image inside a skull-like scattering layer, however, it can hardly restore the information of bone micro-structure accurately [45]. Convolutional neural networks (CNN) are a type of deep feedforward neural network with convolution computation [46]. It is one of the representative algorithms of deep learning. Based on their rich representation power, fast inference and filter sharing properties, CNNs have significantly pushed the performance of image classification [47; 48; 49; 50] and super-resolution reconstruction [51; 52; 53]. This data-driven way also helps achieve high-resolution and fast PA image reconstruction, avoidingaccurately analyzing the physical process, which has greatly improved the image quality of CT and magnetic resonance imaging (MRI) [54; 55; 56; 57; 58; 59; 60; 61]. [PERSON] et al. proposed an architecture to locate and remove the reflection artifacts of point-like objects for generating high-quality PA images [62]. The deep learning method has been reported to successfully recover the image quality from sparse photoacoustic data and be proven with data in vivo [63]. [PERSON] et al. utilized the CNN to improve contrast in low-fluence PA imaging, by mapping low fluence illumination source images to their corresponding high fluence excitation maps [64]. However, whether CNN could be used for PA bone imaging has not been studied. Overall, PA imaging owns great advantages in bone evaluation, but the complexity of bone structure makes it extremely difficult to analyze how the light and sound propagate in the tissue. This limits the development of the algorithms for PA bone imaging. The deep learning method is an efficient way to restore image details through data learning without analyzing physical process, which would be of great significance to the development of PA bone imaging. The contributions of this work are mainly in three aspects:  
1. The CNN for PA bone imaging was first analyzed in this study. 2. A modified U-Net architecture was proposed by embedding attention modules for PA bone imaging. 3. The relationship between bone structure and reconstruction quality of the CNN was analyzed in this study. The overview of the paper is organized as follows. In Section 2, we review the theory of the post-processing approach, including the physical model of PA imaging reconstruction and how to optimize low-quality images with CNN. A detailed demonstration of the proposed network architecture is also revealed in this section, with the introduction of the embedded attention module, loss function and quantitative indexes for reconstructed image evaluation. Section 3 provides details for dataset generation and simulation experiments.