Dynamic contrast enhanced MR imaging (DCE-MRI) has commonly been used to investigate disruptions in microvascular capillary permeability in pathologies such as tumours, and in brain diseases such as multiple sclerosis. This imaging technique involves intravenous injection of a contrast agent, which can modulate MR signal contrast, while frequently acquiring images (i.e. every few seconds) as the agent perfuses through the tissue of interest. Microvascular permeability, and other parameters such as blood volume and flow (perfusion) can be quantified through application of a pharmacokinetic model on the data acquired from the MRI scan. The model requires input from both the biological (e.g. pharmacokinetic rate constants) as well as physical (i.e. scanner settings) parameters. As there are a great many variables and different biophysical models (e.g. high blood flow, high permeability tissues, etc.) there needs to be considerations made for situations where the permeability may be only slightly different from normal. In the brain the blood-brain barrier (BBB) is a highly selective barrier that restricts most bulk diffusion/permeability of solutes. Changes in BBB permeability is likely only subtle in diseases such as depression or bipolar disorder, especially when compared against hypervascular-hypermemeable cancers that are void of a BBB altogether. The problem is however, to decide which model of BBB permeability is best suited for differentiating subtle changes. Thus the intention of this project was to investigate multiple pharmacokinetic models for the tracking of MRI contrast agent in regions of the brain with an intact BBB. In the brain, where there is strict regulation of molecules passing through the microvasculature into the extracellular space, and where more subtle disruptions might be of interest, different assumptions may be necessary. Four models were investigated: the Tofts model, the modified Tofts model, the two-compartment exchange model, and the uptake model. Scans of eight healthy subjects were analyzed, and permeability was quantified using each model. The accuracy of each model, quantified by the R\textsuperscript{2} value, were compared. Analysis found that the Tofts model performed significantly worse than the modified Tofts and Uptake models when fitting regions of the brain with a blood-brain barrier, with a p-value of 0.006. The analysis did not reveal any significant difference between the modified Tofts, Uptake or 2CX models, although perhaps it was obscured due to the limited number of data points. Further investigation is needed to determine any differences between the three top-performing models. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/26631 |
| Date | January 2021 |
| Creators | Gilbank, Ashley |
| Contributors | Noseworthy, Michael, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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