Phosphorus magnetic resonance spectroscopy and spectroscopic imaging (31P-MRS/MRSI) non-invasively provide very important information regarding energy metabolism as they can detect high energy metabolites and membrane phospholipids in vivo. They have repeatedly proven their utility in the study of healthy and disease conditions, as many disorders are related to imbalances in bioenergetic processes. However, they are not often used in a clinic setting as there are technical challenges that lead to very long acquisition times. To address this issue, the present work focused on the implementation of two fast phosphorus magnetic resonance spectroscopic imaging (31P-MRSI) pulse sequences. The first one, "fidEPSI" uses a flyback echo planar readout trajectory calculated in real time to achieve an acceleration factor up to x10. The second, "fidepsiCS" further accelerates the acquisition by combining the flyback EPSI readout with a compressed sensing (CS) sampling scheme. For this latter approach two different data reconstruction processes were compared. Both sequences were tested in phantoms as well as in skeletal muscle and brain tissues of healthy volunteers. The results showed feasibility of the flyback Echo Planar Spectroscopic Imaging (EPSI) to acquire good quality data in a fraction of the time when compared to traditional phase encoded MRSI. Furthermore, the compressed sensing approach was used in an exercise-recovery paradigm to evaluate skeletal muscle high energy phosphate dynamics, achieving a temporal resolution of 9 seconds. Additionally, the comparison of CS reconstruction algorithms suggested that a low-rank approach is more suitable for 31P-MRSI data, compared to traditional thresholding, due to the fact that it exploits the sparsity of the NMR signal as the least number of spectral peaks rather than the fewest amount of non-zero values. Overall, this thesis presents new accelerated methods for the acquisition of 31P-MRSI, and its use in the evaluation of energy metabolism. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/24306 |
| Date | January 2019 |
| Creators | Santos Diaz, Alejandro |
| Contributors | Noseworthy, Michael D., Biomedical Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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