Photoacoustic (PA) techniques provide optical absorption contrast and spatial information at an ultrasound resolution in deep biological tissues. Among the greatest challenges encountered in the PA examination of bone is the analysis of trabecular bone, which holds key chemical and physical information required for bone health assessments. Ultrasound detection is naturally registered with PA detection; therefore, in this study, we propose ultrasound guidance for the PA detection of trabecular bone. We perform both numerical simulations and an in vivo experiment on a human subject to investigate the possibility of ultrasound-guided detection and segmentation of photoacoustic signals from bone tissue in vivo in a non-invasive manner. The results obtained from the simulation and in vivo experiment suggest that the ultrasound-guided PA method can distinguish PA signals from trabecular and cortical bones as well as from the overlying soft tissue. Considering that the PA technique is non-ionizing and non-invasive, it holds potential for clinical bone health assessment.
Photoacoustic (PA) imaging can provide both chemical and micro-architectural information for biological tissues. However, photoacoustic imaging for bone tissue remains a challenging topic due to complicated ultrasonic propagations in the porous bone. In this paper, we proposed a post-processing method based on the convolution neural network (CNN) to improve the image quality of PA bone imaging in a numerical model. To be more adaptive for imaging bone samples with complex structure, an attention block U-net (AB-U-Net) network was designed from the standard U-net by integrating the attention blocks in the feature extraction part. The k-wave toolbox was used for the simulation of photoacoustic wave fields, and then the direct reconstruction algorithm—time reversal was adopted for generating a dataset of deep learning. The performance of the proposed AB-U-Net network on the reconstruction of photoacoustic bone imaging was analyzed. The results show that the AB-U-Net based deep learning method can obtain the image presented as a clear bone micro-structure. Compared with the traditional photoacoustic reconstruction method, the AB-U-Net-based reconstruction algorithm can achieve better performance, which greatly improves image quality on test set with peak signal to noise ratio (PSNR) and structural similarity increased (SSIM) by 3.83 dB and 0.17, respectively. The deep learning method holds great potential in enhancing PA imaging technology for bone disease detection.
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