Real-time image reconstruction for real-time magnetic resonance imaging data

Recently we developed a noniterative real-time image reconstruction algorithm for real-time magnetic resonance imagining, in which the k-space is undersampled. The developed algorithm was in the form of filtered backprojection (FBP) and was able to minimize a quadratic constrained objective function. This thesis investigates the possibility to extend this FBP-MAP (Filtered Backprojection, Maximum a Posteriori) algorithm by introducing more controlling parameters. Thus, our original FBP-MAP algorithm became a special case of the extended FBP-MAP algorithm. A cardiac patient data set is used in this thesis to evaluate the extended FBP-MAP algorithm, and the result from a well-established iterative algorithm is used as the gold standard. If the parameters are selected properly, the extended FBP-MAP algorithm can outperform the original FBP-MAP algorithm.