With the advent of large-scale, multi-site imaging studies, there is a growing need for magnetic resonance imaging (MRI) pulse sequences and matching computer algorithms that generate accurate and harmonious quantitative information descriptive of the examined population. Algorithms for quantitative MRI (qMRI) are of particular importance, as rich information related to tissue structure and composition can be derived without ionizing radiation. With this thesis work, a multiparametric (MP) qMRI image processing pipeline is applied to the brains of adolescents born extremely preterm (EP), who experience high incidence of neurologic disability, and white and gray matter (WM, GM) injuries. Harmonized MP-qMRI parameters served as biological markers of neurodevelopment, and were implemented in computational frameworks for tissue segmentation, and characterization of macromolecular and metal components of the neuroarchitecture.
This work first describes the triple turbo spin echo (Triple-TSE) MRI pulse sequence and multiple aspects of a highly automated MP-qMRI image processing pipeline. The primary MP-qMRI parameters of proton density, and longitudinal and transverse relaxation times were calculated according to the Bloch equation model of the Triple-TSE and harmonized across multiple MRI scanners. Next, the WM microstructure’s organization was studied with synthetic MRI and mapping spatial entropy (SE). The distribution of these parameters and their associations with SE density distinguished atypically versus neurotypically developing adolescents.
In the second part, this work describes a deep GM segmentation method. A two-channel dual-clustering algorithm was applied in parallel with connected component theory to separate cortical and deep gray matter. For every voxel, the similarity of the three MP-qMRI parameters to those of a predefined imaging cluster was interrogated. In this way, the deep GM can be isolated from the in toto brain without additional pulse sequences for structural MRI.
In the final part of this work, an MR relaxation theoretical framework was constructed to derive the distribution of macromolecules and metal deposits in the brain. These microstructural components follow interrelated pathways and play roles in neural signal transmission and normal brain function. Using a fast exchange relaxation model and synthetic MRI, linear associations between the concentrations of these components were identified in deep GM and WM structures. / 2023-05-23T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/44709 |
| Date | 23 May 2022 |
| Creators | McNaughton, Ryan Christopher |
| Contributors | Zhang, Xin |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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