The application of multi-combinatorial approach in sensitivity improvement of lipid photoacoustic imaging

The photoacoustic imaging of lipid is intrinsically constrained by the feeble nature of endogenous lipid signals, posing a persistent sensitivity challenge that demands innovative solutions. Although adopting high-efficiency excitation and detection elements may improve the imaging sensitivity to a certain extent, the application of the elements is inevitably subject to various limitations in practical applications, particularly during in vivo imaging and endoscopic imaging. In this study, we propose a multi-combinatorial approach to enhance the sensitivity of lipid photoacoustic imaging. The approach involves wavelet transform processing of one-dimensional A-line signals, gradient-based denoising of two-dimensional B-scan images, and finally, three-dimensional spatial weighted averaging of the data processed by the previous two steps. This method not only significantly improves the signal-to-noise ratio (SNR) in distinguished feature regions of the image by around 10 dB, but also efficiently extracts weak signals with no distinct features in the original image. After processing with this method, the images acquired under single scanning were compared with those obtained under multiple scanning. The results showed highly consistent image features, with the structural similarity index increasing from 0.2 to 0.8, confirming the accuracy and reliability of the multi-combinatorial approach.