Development, testing, and application of quantitative oxygenation imaging from magnetic susceptibility by MRI
Author(s)Fan, Audrey Peiwen
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
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The healthy brain consumes 20% of total oxygen used by the body under normal conditions. Continuous oxygen delivery to neural tissue is needed to maintain normal brain function and viability. Reliable measurements of brain oxygenation can provide critical information to diagnose and manage diseases in which this oxygen supply is disturbed, including stroke and tumor. In acute stroke, for instance, metabolic biomarkers such as local oxygen extraction fraction (OEF) have been shown to identify tissue at risk of infarction by positron emission tomography. This knowledge can then be used to identify patients who are candidates for reperfusion therapies or to avoid thrombolytic therapy in futile situations. Unfortunately, there is currently no clinically feasible method for radiologists to assess brain oxygenation in patients. My thesis aims to address this need through development of a clinically viable tool to examine regional OEF in the brain with magnetic resonance imaging (MRI). We have designed a novel imaging and analysis method to quantify oxygenation in cerebral veins. MRI phase images are sensitive to local, oxygenation-dependent magnetic field variations in brain vessels, due to the presence of paramagnetic deoxyhemoglobin molecules in venous blood. Our method was developed on a 3 Tesla MRI scanner and tested in 10 healthy volunteers during hypercapnia, i.e. breathing of low levels of CO₂. This respiratory challenge changes the baseline oxygenation state of the brain, enabling us to test whether our MRI method can detect different levels of OEF in vivo. We also show that OEF is reduced in 23 patients with multiple sclerosis, an autoimmune disease of the central nervous disease, and relates to their performance on cognitive tasks.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 101-133).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.