In Vivo Deep Learning Estimation of Diffusion Coefficients of Nanoparticles

Understanding the transport of molecules in the brain in vivo is the key to learning how the brain regulates its metabolism, how brain pathologies develop, and how most of the developed brain-targeted drugs fail. Two–photon microscopy – the main tool for in vivo brain imaging - achieves sub-micrometer resolution and high image contrast when imaging cells, blood vessels, and other microscopic structures. However, images of small and fast-moving objects, e.g. nanoparticles, are ill-suited for analysis of transport with standard methods, e.g. super-localization, because of (i) low photon budgets resulting in noisy images; (ii) severe motion blur due to slow pixel-by-pixel image acquisition by two-photon microscopy; and (iii) high density of tracked objects, preventing their individual localization. Here, we developed a deep learning-based estimator of diffusion coefficients of nanoparticles directly from movies recorded with two-photon microscopy in vivo. We’ve benchmarked the method with synthetic data, model experimental data (nanoparticles in water), and in vivo data (nanoparticles in the brain). Our method robustly estimates the diffusion coefficient of nanoparticles from movies with severe motion blur and movies with high nanoparticle densities, where, in contrast to the classic algorithms, the deep learning estimator’s accuracy improves with increasing density. As a result, the deep learning-based estimator facilitates the estimation of diffusion coefficients of nanoparticles in the brain in vivo, where the existing estimators fail.