Quantitative magnetic resonance image analysis via the EM algorithm with stochastic variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual's response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying pathological/physiological changes via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov random field to model the spatial dependence in the data and develop a maximum likelihood approach via the Expectation--Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. More importantly, we focus on the expected changes rather than image segmentation. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS157 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

CARBON QUANTUM DOTS: BRIDGING THE GAP BETWEEN CHEMICAL STRUCTURE AND MATERIAL PROPERTIES

Carbon quantum dots (CQDs) are the latest generation of carbon nanomaterials in applications where fullerenes, carbon nanotubes, and graphene are abundantly used. With several attractive properties such as tunable optical property, edge-functionalization, and defect-rich chemical structure, CQDs have the potential to revolutionize optoelectronics, electro- and photocatalysis, and biomedical applications. Chemical modifications through the addition of heteroatoms, chemical reduction, and surface passivation are found to alter the band gap, spectral position, and emission pathways of CQDs. Despite extensive studies, fundamental understanding of structure-property relationship remains unclear due to the inhomogeneity in chemical structure and a complex emission mechanism for CQDs. This dissertation outlines a series of works that investigate the structure-property relationship of CQDs and its impact in a variety of applications. First, this relationship was explored by modifying specific chemical functionalities of CQDs and relating them to differences observed in optical, catalytic, and pharmacological performance. While a number of scientific articles reported that top-down or bottom-up synthesized CQDs yielded similar properties, the results herein present dissimilar chemical structures as well as photoluminescent and metal sensing properties. Second, the role of nitrogen heteroatoms in top-down synthesized CQD was studied. The effect of nitrogen atoms on spectral position and fluorescence quantum yield was considerably studied in past reports; however, thorough investigation to differentiate various nitrogen related chemical states was rarely reported. By finely tuning both the quantity of nitrogen doping and the distribution of nitrogen-related chemical states, we found that primary amine and pyridine induce a red-shift in emission while pyrrolic and graphitic nitrogen produced a blue-shift in emission. The investigation of nitrogen chemical states was extended to bottom-up synthesized CQDs with similar results. Finally, top-down, bottom-up, nitrogen-doped and chemically reduced CQDs were separately tested for their ability to act as photodynamic anti-cancer agents. This series of experiments uncovered the distribution of reactive oxygen species produced during light exposure which elucidated the photodynamic mechanisms of cancer cytotoxicity. The results presented in this dissertation provide key insight into engineering finely-tailored CQDs as the ideal nanomaterial for a broad range of applications

Single-Calf Heifer System Profitability Compared to Other North Dakota Beef Production Systems

Production Economics, Productivity Analysis,

Differentiating the Impact of Nitrogen Chemical States on Optical Properties of Nitrogen-Doped Graphene Quantum Dots

The optical properties of top-down synthesized oxidized graphene quantum dots (ox-GQDs) and nitrogen-incorporating graphene quantum dots (N-GQDs) along a range of hydrothermal treatment temperatures were observed. By controlling the hydrothermal treatment temperature, different chemical states of nitrogen atoms were incorporated into GQDs. Below 150 °C, edge-terminating amines and amides dominated the nitrogen content of N-GQDs. Above 150 °C, nitrogen was primarily present in the forms of pyridinic, pyrrolic and quaternary N. In addition to the absorbance and emission profiles of ox-GQDs and N-GQDs, pH-dependent emission spectra were collected to probe chemical states of nitrogen atoms and investigate the relationship between nitrogen location and photoluminescence

Quantitative Magnetic Resonance Image Analysis via the EM Algorithm with Stochastic Variation

Quantitative Magnetic Resonance Imaging (qMRI) provides researchers insight into pathological and physiological alterations of living tissue, with the help of which, researchers hope to predict (local) therapeutic efficacy early and determine optimal treatment schedule. However, the analysis of qMRI has been limited to ad-hoc heuristic methods. Our research provides a powerful statistical framework for image analysis and sheds light on future localized adaptive treatment regimes tailored to the individual’s response. We assume in an imperfect world we only observe a blurred and noisy version of the underlying “true” scene via qMRI, due to measurement errors or unpredictable influences. We use a hidden Markov Random Field to model the unobserved “true” scene and develop a maximum likelihood approach via the Expectation-Maximization algorithm with stochastic variation. An important improvement over previous work is the assessment of variability in parameter estimation, which is the valid basis for statistical inference. Moreover, we focus on recovering the “true” scene rather than segmenting the image. Our research has shown that the approach is powerful in both simulation studies and on a real dataset, while quite robust in the presence of some model assumption violations

Longitudinal image analysis of tumour–healthy brain change in contrast uptake induced by radiation

The work is motivated by a quantitative magnetic resonance imaging study of the differential tumour–healthy tissue change in contrast uptake induced by radiation. The goal is to determine the time in which there is maximal contrast uptake (a surrogate for permeability) in the tumour relative to healthy tissue. A notable feature of the data is its spatial heterogeneity. Zhang and co-workers have discussed two parallel approaches to ‘denoise’ a single image of change in contrast uptake from baseline to one follow-up visit of interest. In this work we extend the image model to explore the longitudinal profile of the tumour–healthy tissue contrast uptake in multiple images over time. We fit a two-stage model. First, we propose a longitudinal image model for each subject. This model simultaneously accounts for the spatial and temporal correlation and denoises the observed images by borrowing strength both across neighbouring pixels and over time. We propose to use the Mann–Whitney U -statistic to summarize the tumour contrast uptake relative to healthy tissue. In the second stage, we fit a population model to the U -statistic and estimate when it achieves its maximum. Our initial findings suggest that the maximal contrast uptake of the tumour core relative to healthy tissue peaks around 3 weeks after initiation of radiotherapy, though this warrants further investigation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79255/1/j.1467-9876.2010.00718.x.pd