Course Description
Magnetic Resonance Imaging (MRI) is a non- invasive tomographic imaging technique with many applications in medicine and biomedical research. Driven by demanding applications, the development of improved image reconstruction algorithms is an important research area with connections to inverse problems, approximation theory, optimization, and signal processing. State-of- the-art iterative methods can process non- uniformly acquired samples (non-Cartesian MRI), exploit information from multiple receive coils (parallel imaging), use sparsity or low-rank priors (compressed sensing), and utilize specific knowledge about physical effects during signal generation (model- based reconstruction). This course will discuss these basic concepts and explain theory and implementation of selected algorithms.Time and Location
Instructor
Prerequisites
Projects and Grading
Tentative Syllabus