Melanoma is known as a malignant tumor of melanocytes, which usually appear in the blood circulation at the metastasis stage of cancer. Thus the detection of circulating melanoma cells is useful for early diagnosis and therapy of cancer. Here we have developed an in vivo photoacoustic flow cytometry (PAFC) based on the photoacoustic effect to detect melanoma cells. However, the raw signals we obtain from the target cells contain noises such as environmental sonic noises and electronic noises. Therefore we apply correlation comparison and feature separation methods to the detection and verification of the in vivo signals. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we are able to provide a method for separating photoacoustic signals generated by target cells from background noises. The method introduced here has proved to optimize the signal acquisition and signal processing, which can improve the detection accuracy in PAFC.
Qiyan Wang, Quanyu Zhou, Ping Yang, Xiaoling Wang, Zhenyu Niu, Yuanzhen Suo, Hao He, Wenyuan Gao, Shuo Tang, and Xunbin Wei, "Optimized signal detection and analysis methods for in vivo photoacoustic flow cytometry," Proc. SPIE 10065, Biophotonics and Immune Responses XII, 100650C (Presented at SPIE BiOS: January 30, 2017; Published: 20 February 2017); https://doi.org/10.1117/12.2253495.
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