Imaging of Flow Patterns with Fluorescent Molecular Rotors

Molecular rotors are a group of fluorescent molecules that form twisted intramolecular charge transfer states (TICT) upon photoexcitation. Some classes of molecular rotors, among them those that are built on the benzylidene malononitrile motif, return to the ground state either by nonradiative intramolecular rotation or by fluorescence emission. In low-viscosity solvents, intramolecular rotation dominates, and the fluorescence quantum yield is low. Higher solvent viscosities reduce the intramolecular rotation rate, thus increasing the quantum yield. We recently described a different mechanism whereby the fluorescence quantum yield of the molecular rotor also depends on the shear stress of the solvent. In this study, we examined a possible application for shear-sensitive molecular rotors for imaging flow patterns in fluidic chambers. Flow chambers with different geometries were constructed from polycarbonate or acrylic. Solutions of molecular rotors in ethylene glycol were injected into the chamber under controlled flow rates. LED-induced fluorescence (LIF) images of the flow chambers were taken with a digital camera, and the intensity difference between flow and no-flow images was visualized and compared to computed fluid dynamics (CFD) simulations. Intensity differences were detectable with average flow rates as low as 0.1 mm/s, and an exponential association between flow rate and intensity increase was found. Furthermore, a good qualitative match to computed fluid dynamics simulations was seen. On the other hand, prolonged exposure to light reduced the emission intensity. With its high sensitivity and high spatial and temporal resolution, imaging of flow patterns with molecular rotors may become a useful tool in microfluidics, flow measurement, and control

Robust near real-time estimation of physiological parameters from megapixel multispectral images with inverse Monte Carlo and random forest regression

PURPOSE: Multispectral imaging can provide reflectance measurements at multiple spectral bands for each image pixel. These measurements can be used for estimation of important physiological parameters, such as oxygenation, which can provide indicators for the success of surgical treatment or the presence of abnormal tissue. The goal of this work was to develop a method to estimate physiological parameters in an accurate and rapid manner suited for modern high-resolution laparoscopic images. METHODS: While previous methods for oxygenation estimation are based on either simple linear methods or complex model-based approaches exclusively suited for off-line processing, we propose a new approach that combines the high accuracy of model-based approaches with the speed and robustness of modern machine learning methods. Our concept is based on training random forest regressors using reflectance spectra generated with Monte Carlo simulations. RESULTS: According to extensive in silico and in vivo experiments, the method features higher accuracy and robustness than state-of-the-art online methods and is orders of magnitude faster than other nonlinear regression based methods. CONCLUSION: Our current implementation allows for near real-time oxygenation estimation from megapixel multispectral images and is thus well suited for online tissue analysis