Abstract
Studies on improving performance of magnetic resonance imaging (MRI) via focusing on and making use of sparsity of images under appropriate sparsifying basis. A mathematical foundation of such studies is compressed sensing, which allows us to solve underdetermined linear equations assuming sparsity of the solution. MRI image reconstruction on the basis of sparsity can be regarded as an example of the innovation in the field of medical image engineering, in which one separates data acquisition and image reconstruction. Magnetic resonance fingerprinting can be regarded as an approach which advances this idea further in the context of MRI, and a combination of magnetic resonance fingerprinting and compressed sensing is studied as well in order to further improve performance.
