<p> The motivation for this work is a vision of widespread adoption of a priori quantitative epidemiological information for clinical decision-making, and can be seen as a quantitative large-scale extension of evidence-based medicine (EBM). Medical images can be seen as a spatially encoded map of physiological measurements that can be used to predict prognosis and to drive treatment plans. This paradigm can be very powerful and is driven by the recent big data revolution in computer science as well as the increasing availability of medical imaging modalities due to decreases in manufacturing costs. In order to achieve this overarching goal, three practical requirements must be reached and correspond to the parts of this thesis: Part A: Developing IT infrastructure and technology that enables the dataset to be properly collected and organized for analysis. Part B & C: Generation of functional (Part B) and structural (Part C) medical imaging contrast that are optimized for analysis. Part D: Pattern recognition techniques (including both image processing and machine learning techniques) to mine information from the large imaging datasets generated. As part of the thesis, I discuss my contribution to IT infrastructure (Part A) by developing a Short Message Service (SMS)-based system to control the clinically used Picture Archival and Communication System (PACS) (Ch.2) as well as an imaging study tool that categorizes patient imaging data for use in retrospective studies(Ch.3). I then go on to detail my work with functional neuroimaging of obesity using functional magnetic resonance imaging (fMRI)(Ch.4) and (Ch.5). Chapters 6-9 details my efforts at studying abnormal aging versus normal aging using diffusion MRI as well as applications of diffusion MRI to surgical planning. Chapters 10 discusses my work integrating diffusion MR with FLAIR MRI to investigate the properties of white matter lesions and how it can be used in the clinical setting. Chapter 11 then moves on to talk about my work modifying standard brain parcellation techniques to allow them to work with aged brains with large infarcts. Chapters 6-11 altogether represent my efforts in structural neuroimaging using MRI (Part C). The thesis then closes with capstone work in development staging using hand x-rays using fuzzy logic (Ch. 12 & 13). To close the work with Alzheimer's Disease (AD) and aging, we used machine learning techniques to predict disease progression based on a baseline MRI scan as well as higher order analysis of our diffusion MRI dataset by integrating MRI information with other clinical information such as neuropsychological tests, cardiovascular status. This is all in an effort to computationally explore the relationship between MRI measurements and clinical presentation of disease as measured by neuropsychological scores. Similarly with the Obesity work, we related fMRI activation differences between high and low calorie foods with non-imaging information such as insulin resistance (Ch. 16).</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3610018 |
| Date | 05 March 2014 |
| Creators | Tsao, Sinchai |
| Publisher | University of Southern California |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0014 seconds