# Ultrasound-Guided Detection and Segmentation of Photoacoustic Signals from Bone Tissue In Vivo
## 4 Discussion and Conclusions  
We highlighted a PA-based method, which is naturally co-registered with the transmission/reflection US methods, for segmenting the PA signal acquired from human calcaneus bone. This method is more accurate for determining the signal generated by the trabecular bone from other parts (overlying soft tissue and cortical bone), which is critical in PA signal analysis. Based on the US signal, the PA signal can be divided into two parts: (a) signals from the trabecular bone and (b) signals from the overlying soft tissue and cortical bone, which is more important for bone assessments. The obtained results indicate that US can provide structural information at a minimal additional cost. The setup used in this study may serve as a practical prototype for future trials on humans. The presented study also had some limitations. First, in this study, we only tested one subject. In the future, additional in vivo data should be collected and analyzed on large group of subjects. Second, the numerical simulation was conducted on 2D models, which would be improved to 3D models in future work. Third, this work did not provide the quantified assessment based on the PA signal of trabecular bone. Since it contains rich information with bone health, we will compare the PA signal of trabecular bone at different wavelengths and frequencies for the bone with different bone mineral densities (BMDs) in future work. Despite these limitations, this study successfully proved the feasibility of using the US-guided PA techniques to segment the PA signal acquired from bone tissue in vivo. For a clinical study, photoacoustic imaging is a potential way to implement the US-guided photoacoustic bone assessment method. For the photoacoustic imaging of bone, two of the most important problems are (1) the segmentation of the PA signal from the different layers and (2) the compensation for the large attenuation of the PA signal in the bone. We have proved the segmentation information can be obtained by the US method. Meanwhile, it can also provide the ultrasound parameters, such as the speed of sound and broadband ultrasound attenuation, which can be used for the compensation of the PA signal. Besides photoacoustic imaging, photoacoustic sensing has a greater potential to be a quicker way for clinical application and translation, as shown in our published work recently [32]. However, in the published work, we did not take the different sizes of bone into account for each subject in the PA signal segmentation. By using the segmentation based on the US-guided method proposed in this study, the segmentation can be done automatically. Unlike the established osteoporosis detection technologies based on X-ray or QUS, which focus mainly on the minerals in the bone, the PA technique described in this study has the potential to provide both physical and chemical bone information. However, segmentation of the PA signal, which is critical in PA signal analysis based on the time of flight without any guidance information, is not sufficiently accurate because of the structural complexity of the bone. Hence, the localization of the PA signal may require another modality. US measurement is usually performed in the transmission/reflection modes, targeting the human heel. US has unique advantages: it is non-ionizing and non-invasive, and it exhibits good penetration in the bone. Besides, it can provide many parameters that can be used for PA compensation. Furthermore, it is also highly compatible with the PA technique, and it can be used for its guidance. In the future, a PA-based bone assessment platform, in combination with QUS, must be developed to provide both microstructural and metabolic information of the bone, which is highly valuable for diagnosis and grading as well as treatment monitoring of osteoporosis and other bone diseases.