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. / Thesis / Doctor of Philosophy (PhD) / Adequate blood circulation to muscles is important for good health. Researchers have used magnetic resonance imaging (MRI) techniques to assess blood and oxygen supply to muscles. The work in this thesis improves upon the analysis methods in prior work, especially in the areas of motion correction of the images and selection of individual muscle regions for analysis.
Previous techniques could sometimes make motion in muscle images worse. This work provides valuable motion and distortion correction for muscle imaging, ensuring that measurements truly reflect muscle physiology. It also describes a method to remove an unwanted signal from post-exercise muscle data, and create a map of the internal muscle motion that occurred.
Finally, an advanced mathematical technique was used to extract signals of interest and important spatial features from muscle image data automatically. The technique produced reliable results within and among subjects.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23440 |
| Date | January 2018 |
| Creators | Davis, Andrew |
| Contributors | Noseworthy, Michael, Medical Physics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0383 seconds