Super-Resolution Imaging and Characterization
thesisposted on 06.12.2019, 15:32 by Dergan Lin
Light in heavily scattering media such as tissue can be modeled with a diffusion equation. A diffusion equation forward model in a computational imaging framework can be used to form images of deep tissue, an approach called diffuse optical tomography, which is important for biomedical studies. However, severe attenuation of high-spatial-frequency information occurs as light propagates through scattering media, and this limits image resolution. Here, we introduce a super-resolution approach based on a point emitter localization method that enables an improvement in spatial resolution of over two orders of magnitude. We demonstrate this experimentally by localizing a small fluorescent inhomogeneity in a highly scattering slab and characterize the localization uncertainty. The approach allows imaging in deep tissue with a spatial resolution of tens of microns, enabling cells to be resolved.
We also propose a localization-based method that relies on separation in time of the temporal responses of fluorescent signals, as would occur with biological reporters. By localizing each emitter individually, a high-resolution spatial image can be achieved. We develop a statistical detection method for localization based on temporal switching and characterization of multiple fluorescent emitters in a tissue-like domain. By scaling the spatial dimensions of the problem, the scope of applications is widened beyond tissue imaging to other scattering domains.
Finally, we demonstrate that motion of an object in structured illumination and intensity-based measurements provide sensitivity to material and subwavelength-scale-dimension information. The approach is illustrated as retrieving unknown parameters of interest, such as the refractive index and thickness of a film on a substrate, by utilizing measured power data as a function of object position.