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http://hdl.handle.net/11375/23440
Title: | Echo Planar Magnetic Resonance Imaging of Skeletal Muscle Following Exercise |
Authors: | Davis, Andrew |
Advisor: | Noseworthy, Michael |
Department: | Medical Physics |
Keywords: | skeletal muscle;magnetic resonance imaging;MRI;echo planar imaging;BOLD;exercise;motion correction;distortion correction;independent component analysis;image processing |
Publication Date: | 2018 |
Abstract: | In recent years, researchers have increasingly used magnetic resonance imaging (MRI) to study temporal skeletal muscle changes using gradient echo (GRE) echo planar imaging (EPI). These studies, typically involving exercise or ischemic challenges, have differentiated healthy subjects from athletic or unhealthy populations, such as those with peripheral vascular disease. However, the analysis methodologies have been lacking. In this thesis, two sessions of post-exercise GRE EPI data were collected from six subjects' lower legs using a 3 Tesla MRI scanner and a custom built ergometer. Past studies used common medical imaging software for motion correction. This work shows that such tools degrade leg image data by introducing motion, increasing root mean squared error in rest data by 22%. A new approach decreased it by 12%. EPI distortion correction in muscle images was also achieved, with the correlation ratio of functional and structural images increasing by up to 8%. In addition, a brief but intense artifact in GRE EPI muscle images results from muscle tissue moving in and out of the imaged volume. This through-plane artifact was successfully modelled as a mono-exponential decay for regression analysis, increasing the utility of the residual signal. The regression parameters were also leveraged to produce muscle displacement maps, identifying 44% of voxels as displaced. The maps were validated in a motion phantom and in-vivo using ultrasound. Finally, independent component analysis (ICA) was applied to post-exercise GRE EPI images to detect features in a data-driven, multivariate way and improve on conventional ROI selection methods. ICA produced parametric maps that were spatially correlated to working muscles from every trial (most with |R| > 0.4). The components were also separated from the susceptibility, motion, and blood vessel signals, and temporally reliable within individuals. These methodological advances represent increased rigour in the analysis of muscle GRE EPI images. |
URI: | http://hdl.handle.net/11375/23440 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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davis_andrew_d_201801_phd.pdf | Andrew Davis PhD Thesis | 48.6 MB | Adobe PDF | View/Open |
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