Computational Methods for Temporal Super-resolution Microscopy

The goal of this proposal is to develop algorithms that will, in conjunction with hardware common in modern microscopes, allow breaking the temporal resolution limit imposed by slow fluorescence cameras and the scarcity of fluorescence photons in dim samples. It will make imaging of fast biological processes possible without compromising spatial for temporal resolution. With the ability to directly visualize dynamic processes in samples that are currently imaged fixed---rather than alive---our technique has the potential to reveal the fundamental mechanisms underlying key biological processes, both in healthy and diseased samples. Our first aim is to develop discrete algorithms for reconstructing high temporal resolution signals from multiple image series of recurring dynamic events, using temporally patterned illumination. Our second aim is to determine algorithms and illumination patterns optimized for specialized image analysis tasks including multi-cellular tracking and morphometry dynamics, two key components in quantitative biology. Our method could be directly integrated into most existing microscopes, thereby benefitting a broad range of biologists and clinicians, while also transforming the microscopy industry. In particular, our technique has direct application in the field of cardiac research, where investigations of disease and defects during heart development are hampered by the lack of fast fluorescence imaging tools. In addition, it could facilitate diagnosis and cure of certain heart diseases, which are a major cause of death and suffering in human populations.
Application Area - Health and bioengineering
Idiap Research Institute
Swiss National Science Foundation
Apr 01, 2016
Mar 30, 2019