## 5 Discussions and Conclusions  
An improved breast ultrasound tomography algorithm based on adaptive TGC for a specific system with cylindrical motion of a PMUT linear array was realized. In order to verify the effectiveness of the algorithm, Field II was used for simulation analysis; the known attenuation coefficient of the phantom was evaluated automatically, and the attenuated image was compensated. Furthermore, the breast model was analyzed using the experimental platform. Circular scanning was performed to acquire a horizontal slice data, and the presented algorithm processed the data and obtained the reconstructed image. Through comparative analysis of the horizontal ultrasound tomography images and the quantitative results, it was inferred that the algorithm compensated for the attenuation of the ultrasonic signal and improved the ability to detect the mass in the model.  
The main components of the experimental setup are installed in a test bed, and the examinee lies prone on it; then, the breast tomography image can be obtained automatically by using the improved algorithm. In the clinical application, the non-invasive ultrasound detection does not rely on the operator's experience and gives patients a certain degree of comfort and safety [29; 30]. Besides, the surrounding tissue, shape, size, number, margin contour, and lesion boundary can be detected intuitively by using this setup. Ultrasound elastography provides additional information on breast lesions on the basis of duplex sonography. Elastography imaging is a qualitative and quantitative technique that involves tissue stiffness or hardness rather than anatomy [31; 32]. A combination of our algorithm and elastography is a promising approach to achieve effective, accurate, and comprehensive detection of pathological tissue information [33]. Besides, the adaptive TGC technology realized by software can reduce the complexity of hardware and effectively compensate the attenuation signal, which lays a foundation for real-time diagnosis and is beneficial to the development of portable medical equipment [34]. The process and its output are conducive to the accurate classification of the state of the mass and the differentiation between benign and malignant tissues in clinical applications, improving specificity and reducing biopsy rates.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ultrasound Tomography** & **PSNR** & **RMSE** \\ \hline Without TGC & 32.54 & 39.18 \\ With adaptive TGC & 43.79 & 20.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Peak signal to noise ratio (PSNR) and root-mean-square error (RMSE) run for the profiles.  
Figure 14: Cross-sections of the horizontal ultrasound tomography image at y = 9 cm: (**a**) without TGC, (**b**) with adaptive TGC.  
**Author Contributions:** [PERSON] designed the ultrasound tomography algorithm, organized the experiments, performed the data collection and analysis, and drafted the manuscript. [PERSON] and [PERSON] supervised the process. [PERSON] and [PERSON] modified the manuscript and provided the funding support. [PERSON] studied the simulation procedure.  
**Funding:** This research was funded by the National Natural Science Foundation of China as National Major Scientific Instruments Development Project, grant number 61727806. This research was funded by the National Key Research and Development Project, grant number 2016 YFC0105004 and 2016 YFC0101900. Sponsored by the Fund for Shanxi '1331 Project' Key Subject Construction.  
**Conflicts of Interest:** The authors declare no conflict of interest.