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dc.contributor.advisorCheng, Ji-Xinen_US
dc.contributor.authorLin, Haonanen_US
dc.date.accessioned2021-09-28T12:51:00Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/2144/43080
dc.description.abstractStimulated Raman scattering (SRS) imaging is a chemical imaging scheme that can visualize cellular content based on intrinsic chemical bond vibrations. To resolve chemicals with overlapping Raman bands, spectroscopic SRS platforms have been developed. To date, endeavors on high-speed instrumentation have achieved spectral acquisition at the microsecond level, enabling in vivo imaging of cells and tissues. Nevertheless, due to the extremely small Raman cross-sections, the current performance of SRS is bounded by a design space that trades off speed, signal fidelity, and spectral bandwidth. The lack of tailored data mining algorithms further limits the chemical information one can extract from the spectroscopic images. My thesis work focuses on developing computational SRS imaging approaches to break the physical tradeoffs and novel data analytical tools to decipher essential chemical information from stimulated Raman spectroscopic images. Utilizing data redundancy of spectroscopic images, we developed two compressive sensing schemes to improve the imaging speed by one order of magnitude without information loss. To break the sensitivity limit, we proposed an ultrafast spectroscopic SRS system and further integrated it with a deep neural network to synergistically achieve microsecond level imaging in the fingerprint region. To improve the chemical specificity and content levels, we implemented a sparsity-regularized spectral unmixing algorithm, realizing multiplexed imaging of up to six major metabolites in a cell. Finally, enabled by advances in low-exposure imaging and spectral unmixing, longitudinal imaging of biofuel synthesis in live cells with sophisticated chemical information is demonstrated.en_US
dc.language.isoen_US
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBiomedical engineeringen_US
dc.subjectBiomedical imagingen_US
dc.subjectComputational imagingen_US
dc.subjectNonlinear opticsen_US
dc.titleStimulated Raman spectroscopic imaging: data science driven innovations & applicationsen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2021-09-25T02:10:38Z
dc.description.embargo2022-09-24T00:00:00Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineBiomedical Engineeringen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.orcid0000-0003-0437-5902


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International