Characterization of Photoacoustic Flow Cytometry Signals

Abstract

Photoacoustic flow cytometry has been utilized to clinically determine the presence of melanoma circulating tumor cells (CTCs). Further investigation was conducted into the morphology of detection signals and how they could be manipulated to allow for further classification. Novel features were extracted from waveforms that appear to have strong classification ability. Neural networks were also used to determine classification potential and the creation of feature mapping for future unsupervised classification. Detections were expanded from single waves to a time dependent multiwave event. Waveforms were also determined to be of non-parametric distribution, allowing for classification by neural network but not allowing for reduction into feature maps with techniques used in the study