I am a postdoctoral scholar at the Caltech Optical Imaging Laboratory, under the supervision of Prof. Lihong V. Wang. My research is interdisciplinary and focused on developing new types of optical imaging techniques that could advance the work of other researchers and medical personnel in a wide variety of fields. My research interests include photoacoustic microscopy, photoacoustic computed tomography, compressed ultrafast photography, nonlinear microscopy, multiphoton microscopy, fluorescence lifetime imaging microscopy, super-resolution microscopy, high-speed imaging, deep tissue imaging, adaptive optics, computational imaging, deep learning.
Ph.D. in Electrical Engineering, 2019
University of Notre Dame
M.S. in Electrical Engineering, 2017
University of Notre Dame
B.Eng. in Automation, 2014
Huazhong University of Science and Technology
Traditional fluorescence microscopy is blind to molecular microenvironment information that is present in a fluorescence lifetime, which can be measured by fluorescence lifetime imaging microscopy (FLIM). However, most existing FLIM techniques are slow to acquire and process lifetime images, difficult to implement, and expensive. Here we present instant FLIM, an analog signal processing method that allows real-time streaming of fluorescence intensity, lifetime, and phasor imaging data through simultaneous image acquisition and instantaneous data processing. Instant FLIM can be easily implemented by upgrading an existing two-photon microscope using cost-effective components and our open-source software. We further improve the functionality, penetration depth, and resolution of instant FLIM using phasor segmentation, adaptive optics, and super-resolution techniques. We demonstrate through-skull intravital 3D FLIM of mouse brains to depths of 300 µm and present the first in vivo 4D FLIM of microglial dynamics in intact and injured zebrafish and mouse brains for up to 12 h.
Fluorescence lifetime imaging microscopy (FLIM) provides additional contrast for fluorophores with overlapping emission spectra. The phasor approach to FLIM greatly reduces the complexity of FLIM analysis and enables a useful image segmentation technique by selecting adjacent phasor points and labeling their corresponding pixels with different colors. This phasor labeling process, however, is empirical and could lead to biased results. In this Letter, we present a novel and unbiased approach to automate the phasor labeling process using an unsupervised machine learning technique, i.e., K-means clustering. In addition, we provide an open-source, user-friendly program that enables users to easily employ the proposed approach. We demonstrate successful image segmentation on 2D and 3D FLIM images of fixed cells and living animals acquired with two different FLIM systems. Finally, we evaluate how different parameters affect the segmentation result and provide a guideline for users to achieve optimal performance.
Fluorescence microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, fluorescence microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean fluorescence microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise fluorescence microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.
Super-resolution microscopy is broadening our in-depth understanding of cellular structure. However, super-resolution approaches are limited, for numerous reasons, from utilization in longer-term intravital imaging. We devised a combinatorial imaging technique that combines deconvolution with stepwise optical saturation microscopy (DeSOS) to circumvent this issue and image cells in their native physiological environment. Other than a traditional confocal or two-photon microscope, this approach requires no additional hardware. Here, we provide an open-access application to obtain DeSOS images from conventional microscope images obtained at low excitation powers. We show that DeSOS can be used in time-lapse imaging to generate super-resolution movies in zebrafish. DeSOS was also validated in live mice. These movies uncover that actin structures dynamically remodel to produce a single pioneer axon in a ‘top-down’ scaffolding event. Further, we identify an F-actin population – stable base clusters – that orchestrate that scaffolding event. We then identify that activation of Rac1 in pioneer axons destabilizes stable base clusters and disrupts pioneer axon formation. The ease of acquisition and processing with this approach provides a universal technique for biologists to answer questions in living animals.
We present a novel super-resolution fluorescence lifetime microscopy technique called generalized stepwise optical saturation (GSOS) that generalizes and extends the concept of the recently demonstrated stepwise optical saturation (SOS) super-resolution microscopy [Biomed. Opt. Express 9, 1613 (2018)]. The theoretical basis of GSOS is developed based on exploring the dynamics of a two-level fluorophore model and using perturbation theory. We show that although both SOS and GSOS utilize the linear combination of M raw images to increase the imaging resolution by a factor of √M, SOS is a special and the simplest case of GSOS. The super-resolution capability is demonstrated with theoretical analysis and numerical simulations for GSOS with sinusoidal and pulse-train modulations. Using GSOS with pulse-train modulation, super-resolution and fluorescence lifetime imaging microscopy (FLIM) images can be obtained simultaneously. The super-resolution FLIM capability is experimentally demonstrated with a cell sample on a custom-built two-photon frequency-domain (FD) FLIM system based on radio frequency analog signal processing. To our knowledge, this is the first implementation of super-resolution imaging in FD-FLIM.