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dc.contributor.advisorPatil, Chetan Appasaheb
dc.creatorDumont, Alexander Pierre
dc.date.accessioned2020-11-03T16:23:53Z
dc.date.available2020-11-03T16:23:53Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/20.500.12613/2806
dc.description.abstractComputational modeling of light-matter interactions is a valuable approach for simulating photon paths in highly scattering media such as biological tissues. Monte Carlo (MC) models are considered to be the gold standard of implementation and can offer insights into light flux, absorption, and emission through tissues. Monte Carlo modeling is a computationally intensive approach, but this burden has been alleviated in recent years due to the parallelizable nature of the algorithm and the recent implementation of graphics processing unit (GPU) acceleration. Despite impressive translational applications, the relatively recent emergence of GPU-based acceleration of MC models can still be utilized to address some pressing challenges in biomedical optics beyond DOT and PDT. The overarching goal of the current dissertation is to advance the applications and abilities of GPU accelerated MC models to include low-cost devices and model Raman scattering phenomena as they relate to clinical diagnoses. The massive increase in computational capacity afforded by GPU acceleration dramatically reduces the time necessary to model and optimize optical detection systems over a wide range of real-world scenarios. Specifically, the development of simplified optical devices to meet diagnostic challenges in low-resource settings is an emerging area of interest in which the use of MC modeling to better inform device design has not yet been widely reported. In this dissertation, GPU accelerated MC modeling is utilized to guide the development of a mobile phone-based approach for diagnosing neonatal jaundice. Increased computational capacity makes the incorporation of less common optical phenomena such as Raman scattering feasible in realistic time frames. Previous Raman scattering MC models were simplistic by necessity. As a result, it was either challenging or impractical to adequately include model parameters relevant to guiding clinical translation. This dissertation develops a Raman scattering MC model and validates it in biological tissues. The high computational capacity of a GPU-accelerated model can be used to dramatically decrease the model’s grid size and potentially provide an understanding of measured signals in Raman spectroscopy that span multiple orders of magnitude in spatial scale. In this dissertation, a GPU-accelerated Raman scattering MC model is used to inform clinical measurements of millimeter-scale bulk tissue specimens based on Raman microscopy images. The current study further develops the MC model as a tool for designing diffuse detection systems and expands the ability to use the MC model in Raman scattering in biological tissues.
dc.format.extent196 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectBioengineering
dc.subjectBiophysics
dc.subjectEngineering, Biomedical
dc.subjectDiffuse Reflectance
dc.subjectFluorescence Spectroscopy
dc.subjectGpu Acceleration
dc.subjectModeling
dc.subjectMonte-carlo
dc.subjectRaman Spectroscopy
dc.titleMONTE CARLO MODELING OF DIFFUSE REFLECTANCE AND RAMAN SPECTROSCOPY IN BIOMEDICAL DIAGNOSTICS
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberPleshko, Nancy
dc.contributor.committeememberTuzel, Erkan
dc.contributor.committeememberFang, Qianqian
dc.contributor.committeememberWright, William Geoffrey
dc.description.departmentBioengineering
dc.relation.doihttp://dx.doi.org/10.34944/dspace/2788
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
refterms.dateFOA2020-11-03T16:23:53Z


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