<div>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.</div><div><br></div><div>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. </div><div><br></div><div>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. </div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/11326919 |
| Date | 06 December 2019 |
| Creators | Dergan Lin (5929976) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Super-Resolution_Imaging_and_Characterization/11326919 |
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