Novel adaptive reconstruction schemes for accelerated myocardial perfusion magnetic resonance imaging

Coronary artery disease (CAD) is one of the leading causes of death in the world. In the United States alone, it is estimated that approximately every 25 seconds, a new CAD event will occur, and approximately every minute, someone will die of one. The detection of CAD in its early stages is very critical to reduce the mortality rates. Magnetic resonance imaging of myocardial perfusion (MR-MPI) has been receiving significant attention over the last decade due to its ability to provide a unique view of the microcirculation blood flow in the myocardial tissue through the coronary vascular network. The ability of MR-MPI to detect changes in microcirculation during early stages of ischemic events makes it a useful tool in identifying myocardial tissues that are alive but at the risk of dying. However this technique is not yet fully established in the clinic due to fundamental limitations imposed by the MRI device physics in terms of slow imaging speed. The limitations of current MRI schemes often make it challenging to simultaneously achieve high spatio-temporal resolution, sufficient spatial coverage, and good image quality in myocardial perfusion MRI. Furthermore, the acquisitions are typically set up to acquire images during breath holding. This often results in motion artifacts due to improper breath hold patterns. This also limits its applicability to a large domain of patient populations such as those with impaired respiratory function, arrhythmias, pediatrics. The overall objective of this thesis is to develop a novel dynamic imaging framework that can enable free breathing myocardial perfusion imaging with high spatiotemporal resolutions and close to whole heart volume coverage. To achieve this, this thesis deals with developing novel image reconstruction methods for the reconstruction of dynamic MRI data from highly accelerated / under-sampled Fourier measurements. It specifically focuses on novel blind or adaptive image models that represent the dynamic image data set using adaptive temporal bases (bases derived from the data at hand). This is in sharp contrast to classical models that rely on predetermined temporal bases (such as Fourier bases), which require assumptions such as temporal