Event Date
Event Date
Two-photon fluorescence microscopy is a powerful technique for imaging neuronal activity, but its speed is traditionally constrained by the point-by-point scanning mechanism. To overcome this limitation, we present a novel approach that combines short-line illumination to boost imaging speed with computational techniques to recover spatial resolution. Our system excites the sample using a short-line point spread function and employs a single-pixel detector to capture the signal from the entire line simultaneously. A deep neural network then reconstructs the high-resolution images from the line-sampled data. This method substantially accelerates imaging by reducing the number of scanning rows required, while maintaining cellular resolution. We validate the fidelity of the reconstructed videos using simulated datasets. This approach offers a promising pathway toward high-throughput, high-speed neural activity imaging.
Presenter
Yifei Sun
Univ. of California, Davis (United States)
Yifei Sun received his B.S. and M.S., both in Electrical Engineering, from ShanghaiTech University, Shanghai, China, in 2019 and 2022 respectively. He is currently a PhD student in the Department of Electrical and Computer Engineering at University of California, Davis, USA. His research interests include computational imaging and biomedical image processing.