Robust image processing for cryo-electron tomography using sparse priors
Abstract/Contents
- Abstract
- Cryo-electron tomography(CET) is the only imaging modality that can image 3D density maps of cells and viruses at their native state. It covers the resolution range of 4 - 8nm, and can reach resolutions as low as 2nm and under by using subtomogram averaging. As such, it bridges the gap between high resolution techniques such as the X-ray crystallography, and low resolution ones such as the light microscopy. Thanks to this unique property, CET has been extensively used to reveal the molecular organization of cellular structures in bacterial cells and viruses. Although CET can provide a higher resolution reconstruction of macromolecules than other imaging modalities can, there are several challenges to overcome to achieve high-quality 3D reconstructions of these structures. First, raw CET projections and tomograms have very low SNR typically less than 1. Second, due to the limitations of the arrangement of the sample holder and the transmission electron microscope, it is not possible to obtain informative tomographic projections from all angles, which distorts the 3D reconstructions. Due to these difficulties, conventional image processing techniques often fail to achieve their goals. To make the image processing pipeline for CET more robust, utilizing the prior information known about tomographic projections and reconstructions of interest is crucial. In this thesis, two such examples are presented. One is an image in-painting algorithm using l1 norm minimization to remove interferences in CET. This particular example exploits the fact that CET projections are sparse in the discrete cosine domain (DCT). The other example is subtomogram averaging via nuclear norm minimization where we exploited the observation that aligned structures span a very low dimensional space. Both examples deliver promising results even when original density maps are heavily distorted and covered with significant noise.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Song, Ka Hye |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering. |
| Primary advisor | Horowitz, Mark |
| Thesis advisor | Horowitz, Mark |
| Thesis advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Thesis advisor | Moussavi, Farshid, 1965- |
| Advisor | Candès, Emmanuel J. (Emmanuel Jean) |
| Advisor | Moussavi, Farshid, 1965- |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Ka Hye Song. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Ka Hye Song
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
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