Type 2 diabetes is an increasingly prevalent disease, with more than 400 million people worldwide diagnosed in 2016. As a stable and accurate biomarker, glycated hemoglobin (HbA1c) is clinically used to diagnose type 2 diabetes with a threshold of 6.5% HbA1c among total hemoglobin (Hb). Current methods such as boronate affinity chromatography or enzymatic assay involve complex processing of large-volume blood samples, which inhibits real-time measurement in clinic. Moreover, these methods cannot measure the HbA1c fraction at single red blood cell level, thus unable to separate the contribution by diabetes from other factors such as diseases related to lifetime of red blood cells. Here, we demonstrate a transient absorption imaging approach that is able to differentiate HbA1c from Hb based on the excited state dynamics measurement. HbA1c fraction inside a single red blood cell is derived quantitatively through phasor analysis. HbA1c fraction distribution for diabetic blood is found apparently different from that for healthy blood. A mathematical model is developed to derive the long-term glucose concentration in the blood. Our technology provides a new way to study heme modification and to derive clinically important information avoid of glucose fluctuation in the bloodstream.
Spectroscopic stimulated Raman scattering (SRS) is a label-free chemical imaging modality enabling visualization of molecules in living systems with high specificity. Among various spectroscopic SRS imaging methods, a convenient way is through linearly chirping two femtosecond lasers and tuning their temporal delay, which in turn corresponds to different Raman shifts. Currently, the acquisition speed using a resonant mirror is 3 seconds (80 microseconds per spectrum), which is insufficient for imaging samples with high motility. In this work, we aim to push the imaging speed using a 50-kHz polygon scanner as a delay line tuner, achieving a speed of 20 microseconds per spectrum. At such high speeds, to overcome the signal level decrease due to reduced signal integration time, we apply a U-Net deep learning framework, which first takes pairs of spectroscopic SRS images at different speeds as training samples, with high-speed, low-signal images as input and low speed, high-signal ones as output. After training, the network is capable of rapidly transforming a low-signal spectroscopic image to a high-signal version. Consequently, our design can generate ultrafast spectroscopic SRS image while maintaining the signal level comparable to the output with longer signal integration time.
A hyperspectral image corresponds to a data cube with two spatial dimensions and one spectral dimension. Through linear un-mixing, hyperspectral images can be decomposed into spectral signatures of pure components as well as their concentration maps. Due to this distinct advantage on component identification, hyperspectral imaging becomes a rapidly emerging platform for engineering better medicine and expediting scientific discovery. Among various hyperspectral imaging techniques, hyperspectral stimulated Raman scattering (HSRS) microscopy acquires data in a pixel-by-pixel scanning manner. Nevertheless, current image acquisition speed for HSRS is insufficient to capture the dynamics of freely moving subjects. Instead of reducing the pixel dwell time to achieve speed-up, which would inevitably decrease signal-to-noise ratio (SNR), we propose to reduce the total number of sampled pixels. Location of sampled pixels are carefully engineered with triangular wave Lissajous trajectory. Followed by a model-based image in-painting algorithm, the complete data is recovered for linear unmixing. Simulation results show that by careful selection of trajectory, a fill rate as low as 10% is sufficient to generate accurate linear unmixing results. The proposed framework applies to any hyperspectral beam-scanning imaging platform which demands high acquisition speed.