Integrating Histology and Microarchitecture Modeling with Deep Learning for Diffusion-Weighted Magnetic Resonance Imaging
dc.contributor.advisor | Landman, Bennett A | |
dc.creator | Nath, Vishwesh | |
dc.date.accessioned | 2020-09-22T22:15:43Z | |
dc.date.created | 2020-05 | |
dc.date.issued | 2020-04-06 | |
dc.date.submitted | May 2020 | |
dc.identifier.uri | http://hdl.handle.net/1803/16057 | |
dc.description.abstract | The human brain is one of the most complex organs to understand in terms of anatomy and microstructural tissue properties of the brain’s white matter. There is still a lack of fundamental understanding for various neurological disorders in terms of the microstructural tissue properties of the brain. A critical imaging modality: Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) has become a key insight for probing the in-vivo tissue organization of the underlying microstructure and microarchitecture for the human brain. DW-MRI was proposed in the early 1980’s and has rapidly grown as a field; due to it being the only imaging modality that can provide information about tissue microstructural properties in-vivo. In brief, contributions are briefly summarized: 1.) We characterize the empirical reproducibility of microstructural measures; due to the existence of a large number of approaches that reconstruct the microstructural measures and white matter nerve tract reconstructions as there was a lack of intra-method reproducibility validation. 2.) The empirical validation led us to propose data-driven novel methods using rare animal study datasets. Validation of microstructural measures has traditionally been approached using phantom based studies. 3.) Inter-site harmonized reconstruction of microstructure measures was proposed by the addition of auxiliary data to the rare animal dataset so that effective statistical analysis can be conducted on inter-site data. 4.) Promising results on the prior contributions led us to the proposition of recovery of 3D microstructural measures from 2D microscopic histology slides. 5.) Data-driven modeling in general from the prior contributions (2,3 and 4) led the motivation for learning across the manifolds of single and multi-shell DW-MRI as the acquisitions vary by multiple parameters and the capabilities of the scanning hardware. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | DW-MRI | |
dc.subject | Deep Learning | |
dc.subject | Histology | |
dc.title | Integrating Histology and Microarchitecture Modeling with Deep Learning for Diffusion-Weighted Magnetic Resonance Imaging | |
dc.type | Thesis | |
dc.date.updated | 2020-09-22T22:15:43Z | |
dc.type.material | text | |
thesis.degree.name | PhD | |
thesis.degree.level | Doctoral | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
local.embargo.terms | 2020-11-01 | |
local.embargo.lift | 2020-11-01 | |
dc.creator.orcid | 0000-0002-6840-6205 |
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Electronic Theses and Dissertations
Electronic theses and dissertations of masters and doctoral students submitted to the Graduate School.