## 5 Discussion
When reconstructing bone samples with various micro-structure complexities, the result in Figure 8 shows that the anti-interference ability of AB-U-Net is better than that of TR with steadier image quality. We studied the influence of attention blocks in CNN reconstruction. Depending on their capabilities to focus on important information, attention modules have improved the performance of CNNs in the field of computational versions [89]. It is reasonable to believe that the addition of this module will enhance the quality of reconstruction. In this study, we embedded CBAM into the typical U-Net and  
Figure 8: (**a**) Scatter plots of the relationship between SMI and the performance of TR reconstruction, and (**b**) performance of AB-U-Net reconstruction. The red lines are the fitting curves of linear regression, where R\({}^{2}\) is the coefficient of determination, and \(p\) is the possibility value. designed a new network: AB-U-Net. Besides, U-Net (without attention module) and attention U-Net (attention module in the decoder part) were added for comparison to investigate the effect. Results demonstrate that attention blocks embedded into feature extraction modules implicitly learn useful features and suppress feature activations in irrelevant regions. Thus, for the reconstruction task of bones with complex interior structures, it is better to employ attention blocks in the encoder part to enhance CNN's ability feature representative. One advantage of the trained CNNs for medical image reconstruction is their ability to restore relatively accurate information in a short time, which meets the need of fast imaging. Specifically, the comparisons of the computational complexities and reconstruction times of the three CNN networks are summarized in Table 2. 'Params' represents the number of model parameters. 'FLOPs' are floating point numbers, understood as the amount of computation. The computations were performed using an Intel Xeon Gold 6130 CPU and a Nvidia Quadro P4000 GPU. The training times for CNNs were calculated using the training set of 2280 image pairs (512 \(\times\) 512 resolution) for 100 epochs with the batch size of 2, and the testing times for the trained CNNs of a single PA image (reconstruction time) were given. As shown in Table 2, the computational complexity of AB-U-Net (329 GFLOPs) is higher than U-Net (184 GFLOPs) and attention U-Net (266 GFLOPs). The complex computation leads to the AB-U-Net need to reduce the batch size of training and undertake longer training time than the others. However, once the network training is completed, the reconstruction for single PA image of AB-U-Net only takes 5 s. Although the testing time of AB-U-Net is still longer than U-Net and Attention U-Net, it is acceptable at the cost of only 2 s additional to achieve better performance. A limitation of this study is that simulation data were used for training and testing the CNN framework for PA bone reconstruction. As a data-driven method, the CNN model requires a large amount of data for training and learning implicit information. It is not very effective and extremely time-consuming to measure massive in vitro or in vivo PA experimental signals from varied bone conditions. However, simulations were quite helpful and effective to generate sufficient PA data for training the CNN models. The simulation results showed that the proposed AB-U-Net significantly improved the quality of PA reconstructed bone images. To better combine the simulations with practical experiments, we will perform in vitro and in vivo experiments to validate the effect of CNN reconstruction in PA bone images. Transfer learning could be used to adopt and apply the simulation-trained CNN models to in vitro and in vivo experimental conditions [90]. With the best use of previously marked data, transfer learning can effectively shorten training time and improving model accuracy [90]. Another limitation is that as our study is an initial study, no in vivo environmental condition of PA simulation is considered.