Poster: http://www.physics.uwo.ca/colloquia/Colloquia%20Notices/2011_2012/LEVESQUE%20Research%20Talk.pdf
Western University
DEPARTMENT OF PHYSICS AND ASTRONOMY
PHYSICS & ASTRONOMY COLLOQUIUM
Date: Monday, 5th March 2012
Time: 1:30 p.m.
Location: Physics & Astronomy Seminar Room 22
Dr. Ives Levesque
Magnetic Resonance Systems Research Laboratory
Department of Electrical Engineering
Stanford University
“Quantitative mapping of brain tissue properties in vivo by magnetic resonance”
ABSTRACT
Magnetic resonance imaging (MRI) provides exquisite sensitivity to subtle variations in biological tissue structure, molecular composition, and physiological processes. Imaging data can be analyzed with biophysical models, resulting in a wide range of quantitative techniques to map fundamental parameters that relate, directly or indirectly, to tissue properties. Examples include relaxation times, water diffusion, and metabolic information. Quantitative MRI (QMRI) measurements can be validated directly in the laboratory, and facilitate inter-site comparisons and longitudinal studies. Validated metrics can become biomarkers, providing increased specificity for research and clinical use.
QMRI faces a number of challenges, notably the reproducibility and accuracy of methods, exam duration, and interpretation of observations. Acquisition sequences must be optimized for the desired measurement. Signal modeling must be realistic and reliable, and should seek a straightforward interpretation of the observations. Examples will be drawn from the study of multiple sclerosis (MS)—a neurological disease characterized by inflammation and neurodegeneration in the brain and spine—where methods based on magnetization transfer and T2 relaxation have been proposed to characterize myelin content and disease progression. Compensation of static and transmission field inhomogeneity and parallel transmit technology improve data quality and measurement accuracy, in particular for high field imaging (3 Tesla and up). Recent advances in image reconstruction, such as parallel imaging and compressed sensing, open new possibilities for faster data acquisition. These technological advances provide new avenues for the development and application of QMRI.