This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 201-222). / Diffusion magnetic resonance imaging (dMRI) tractography is the only non-invasive tool for studying the connectional architecture of the brain in vivo. By measuring the diffusion of water molecules dMRI provides unique information about white matter pathways and their integrity, making it an invaluable neuroimaging tool that has improved our understanding of the human brain and how it is affected by disease. A major roadblock to its acceptance into clinical practice has been the difficulty in assessing its anatomical accuracy and reliability. In fact, obtaining a map of brain pathways is a multi-step process with numerous variables, assumptions and approximations that can influence the veracity of the generated pathways. Validation is, thus, necessary and yet challenging because there is no gold standard which dMRI can be compared to, since the configuration of human brain connections is largely unknown. Which aspects of tractography processing have the greatest effect on its performance? How do mapping methods compare? Which one is the most anatomically accurate? We tackle these questions with a multi-modal approach that capitalizes on the complementary strengths of available validation strategies to probe dMRI performance on different scales and across a wide range of acquisition and analysis parameters. The outcome is a multi-layered validation of dMRI tractography that 1) quantifies dMRI tractography accuracy both on the level of brain connections and tissue microstructure; 2) highlights the strengths and weaknesses of different modeling and tractography approaches, offering guidance on the issues that need to be resolved to achieve a more accurate mapping of the human brain. / by Giorgia Grisot. / Ph. D. / Ph.D. Harvard-MIT Program in Health Sciences and Technology
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/122194 |
| Date | January 2019 |
| Creators | Grisot, Giorgia. |
| Contributors | Anastasia Yendiki., Harvard--MIT Program in Health Sciences and Technology., Harvard--MIT Program in Health Sciences and Technology |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 222 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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