Computational and optical tools for enabling high-resolution in vivo functional imaging with optical coherence tomography
Abstract/Contents
- Abstract
- Optical Coherence Tomography (OCT) enables real-time imaging of living tissues at cell-scale resolution over millimeters in three dimensions. Despite these advantages, functional biological studies and clinical applications of OCT have been limited. The first limitation that we address is the lack of exogenous contrast agents for OCT. Such contrast agents can be beneficial for functional and molecular imaging, by labeling specific proteins or cells and providing a better understanding of the underlying biological processes in the tissue in addition to its structure, which is provided by conventional OCT. We tackle this limitation by developing uniquely spectral large gold nanorods (LGNRs) and custom algorithms to spectrally distinguish the LGNRs from the surrounding tissue. To verify our ability to identify the LGNRs in OCT volumes and to localize them with higher resolution, we use a method that combines dark-field microscopy with image-processing and machine-learning algorithms to detect them in tissue samples ex vivo. A second limitation of OCT is the speckle noise caused by coherent interference of multiply scattered light, which hides fine tissue structures and also hinders the detection of our contrast agent. We solve this limitation by developing a method for removing speckle noise in OCT by modulating the phase of the light illuminating the sample. By removing the speckle noise, speckle-modulating OCT (SM-OCT) reveals tissue structures in living mice and humans. Notably, the demonstrated improved image-quality of brain tissue can be beneficial for intraoperative tumor margin detection and for neurological studies of small animals. The combination of SM-OCT with our contrast agent can be used for labeling and tracking immune cells in brain tumors in vivo with high-resolution and over a wide field of view.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2018 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Liba, Orly |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | De la Zerda, Adam |
| Thesis advisor | De la Zerda, Adam |
| Thesis advisor | Bowden, Audrey, 1980- |
| Thesis advisor | Chu, Steven |
| Thesis advisor | Wetzstein, Gordon |
| Advisor | Bowden, Audrey, 1980- |
| Advisor | Chu, Steven |
| Advisor | Wetzstein, Gordon |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Orly Liba. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2018. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2018 by Orly Liba
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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