Multicontrast MRI of Atherosclerotic Plaques: Acquisition, Characterization and Reconstruction
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Cardiovascular Disease (CVD) continues to be the leading cause of death in western countries according to the statistics update by the American Heart Association. Atherosclerosis is estimated to be responsible for a large portion of CVD and affects 60 million people in the United States. Accurate diagnosis is crucial for proper treatment planning. Currently, the clinical standard screening technique for diagnosing atherosclerosis is x-ray angiography, which reveals the residual lumen size. X-ray angiographic images possess good resolution and contrast, however, lumen size is not always a proper criterion given the positive remodeling nature of atherosclerotic plaques. In the past decade, it has been shown that most plaques responsible for a fatal or nonfatal myocardial infarction are less than 70% stenosed. Clinical data support the idea that plaques producing non-flow-limiting stenoses account for more cases of plaque rupture and thrombosis than plaques producing a more severe stenosis. Due to this fact, plaque itself must be imaged in order to assess its vulnerability. A wealth of literature suggests that multicontrast MRI has the potential of characterizing plaque constituents, and thus is a promising technique for plaque imaging. Because of the technical difficulties associated with in-vivo plaque imaging and the fact that our research was aimed at developing new methodologies, our approaches was to image excised coronary arteries under simulated in-vivo conditions in a tissue culture chamber. It is shown by this research that automatic plaque characterization techniques developed under ex-vivo conditions still apply for in-vivo studies. Based on this finding, an automatic plaque characterization technique using multicontrast MRI was developed. Furthermore, "shared k-space" reconstruction techniques were interrogated to assess their feasibility in accelerating multicontrast MRI acquisition. Results show that these techniques are promising in accelerating multicontrast MRI acquisitions.