Radio-frequency non-uniformity in cardiac magnetic resonance imaging

UnrestrictedCardiovascular magnetic resonance imaging (MRI) is now routinely used for evaluating cardiac function, and myocardial viability. Due to its unique exibility, MRI has the potential to assess many other aspects of heart disease in the same examination, including the evaluation of coronary arteries, functions of the heart valves, and perfusion of heart muscle. However, the development of cardiac MRI is, in many ways, limited by signal-to-noise ratio (SNR).; Compared to 1.5 Tesla, 3 Tesla MRI systems are able to achieve higher signal-to-noise ratio due to increased polarization, which allows for improvements in spatial and/or temporal resolution. Cardiac MRI at 3T, however, is significantly different from imaging at 1.5T because of a variety of artifacts that result from inhomogeneities of the static magnetic field (B0) and radio-frequency (RF) transmit field (B1^+).; In this thesis, I present new methods to measure and mitigate one of these effects, B1^+ inhomogeneity. First, I present a variation of the saturated double-angle method for measuring B1^+ non-uniformity across the heart. Measured B1^+ profiles are then analyzed to determine the amount of variation and dominant patterns of variation across the left ventricle.; Secondly, I present tailored 2DRF excitation pulse designs that compensate for in-plane B1^+ non-uniformity in 2D imaging. Excitation pulse profiles are designed to approximate the reciprocal of the measured B1^+ variation where the variation over the left ventricle was approximated as unidirectional.; Thirdly, I present a novel saturation pulse design that improves the performance of saturation. The B0 and B1^+ variation across the left ventricle has been previously measured and a train of weighted hard pulses is optimally chosen to saturate proton spins over the expected region in B0-B1^+ space.; Finally, I present a method to accurately predict the myocardial signal during balanced steady-state free precession (SSFP) imaging. I included the effects of non-ideal slice profile, off-resonance, and B1^+ field variation in a model that accurately predicts myocardial signal behavior