Our group is interested in developing next-generation optical imaging and computational methods that will enable noninvasive (label-free), deeper, faster, and richer (more contrast) visualization of biosystems.
Coming soon 🙂
Using fiber supercontinuum and pulse shaping, we developed a multiphoton imaging platform that achieves real-time label-free single-shot functional and structural optical imaging for the first time, and enables non-perturbative, faster, and richer visualization of living systems.
Because of the nonlinear dependence on incident power and the longer excitation wavelength, multiphoton imaging achieves deeper imaging with less photobleaching artifacts compared to one-photon imaging. This opens windows to in vivo imaging of the dynamic living biosystems. Label-free multiphoton microscopy makes the technique even more attractive by the efficient excitation and detection of the intrinsic molecular contrasts.
By utilizing the intrinsic metabolic signatures of biological molecules, we were able to directly visualize and characterize extracellular vesicles in living animals and fresh human tissues with breast cancer.
The high (~30%) re-operation rate for breast conserving surgeries arise from the failure of detecting positive margins at the time of surgery. This risk can be significantly reduced by a combined framework of real-time virtual histopathology and a deep-learning-based classification algorithm.
Recent advances in microscopy, along with advances in artificial intelligence (AI), present an exciting opportunity to innovate microscopy analysis and instrumentation. Here is an example of how we use deep learning to “pseudo label” the label-free images.
Raman spectroscopy provides noninvasive fingerprints of chemical compositions at molecular level. Coherent Raman scattering makes it possible to extend this chemical profiling capacity to broader imaging applications that require high spatiotemporal resolution on top of the chemical resolution, e.g. drug tracking in living cells as shown in the figure below. Signal throughput has been a longstanding limit to further expand this technique to real-life biomedical applications. We aim to develop methods that can address this issue by efficiently acquiring and reconstructing the hyperspectral dataset.