| Cardiac magnetic resonance imaging is an excellent tool for assessing global and regional function of the heart. It is now regarded as the gold standard for the analysis and prediction of abnormality of heart function. In this research, we introduce new imaging methods and analysis techniques to noninvasively study the function of the heart. The main goal was to provide a set of tools that can be helpful in the diagnosis of heart function. First, we develop mathematical frameworks to some of the already existing methods in cardiac MRI in order to explore their promises and limitations. Second, we investigate new techniques and strategies that help in improving these methods. Finally, we introduce some new methods that have the potential of improving cardiac imaging in terms of speed, image quality, and functional quantifications. Successful development of these techniques will provide a set of tools that help in improving cardiac imaging and heart diagnosis.;In the first part, we develop a mathematical framework for strain encoding (SENC) imaging to optimize the parameters selection, address the noise effect on strain quantifications, and study the effect of slice following on strain quantifications. In the second part, we develop a pulse sequence that improves the signal-to-noise ratio (SNR) in SENC images using Steady-State Free Precession (SSFP) acquisition. Then, based on the same pulse sequence, we develop a black blood SSFP pulse sequence that have the advantages of both SSFP and conventional black-blood (BB) pulse sequences. We also propose an optimization algorithm for the gradient spoiler angles in tagging images in order to obtain uniform signal intensity through the cardiac phases. Finally, we develop a new technique for strain quantification that has the potential of obtaining strain images with higher spatial resolution and better SNR. We follow that by proposing a fast imaging pulse sequence that can be used for free-breathing planning in cardiac imaging. |
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