
Sarah Morris
Doctor of Philosophy in Physics (PhD)
Research Topic
Development, validation and application of advanced MRI techniques to measure white matter microstructure
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No abstract available.
No abstract available.
No abstract available.
Magnetic resonance spectroscopy (MRS) measures relative signals arising from spins on different metabolites, e.g. N-Acetyl aspartate (NAA). To improve the interpretability of changes caused by disease, it is optimal to convert these relative signals to absolute concentrations e.g. by referencing it to the MR signal of water. Segmentation of high-resolution qualitative magnetic resonance images (MRI) is an accessible and easy-to-use method to estimate the properties of tissue water in the spectroscopic volume of interest (VOI), including water content, [H₂O], and relaxation properties (T₁, T₂) with pre-determined literature values. However, these tissue properties can change in disease and with age. Therefore, we proposed the use of a quantitative MRI approach to reference metabolite concentrations by measuring subject-specific T₁ and T₂ relaxation as well as water content maps. The approach was first validated by measuring a range of biologically relevant water contents and metabolite concentrations in vitro. [H₂O] was overestimated by 4.8% on average, while NAA concentrations were underestimated by 9.9%. In a study of ten healthy controls comparing the traditional segmentation quantification with the novel quantitative MRI method, we observed larger variabilities for subject-specific water properties, which did not propagate to the variability of the absolute metabolite concentrations of the neurochemicals (p > 0.37). Metabolite concentrations were lower with the quantitative MRI approach by -5.4% (p=0.002) in a white matter volume of interest (VOI) and -2.4% (p=0.002) in a grey matter VOI compared to the segmentation-based quantification.The quantitative MRI method for calculating absolute metabolite concentrations in MRS showed promising results, offering a potential alternative for the currently widely used segmentation approach.
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