  The recently proposed Magnetic Resonance Fingerprinting (MRF) technique can
simultaneously estimate multiple parameters through dictionary matching. It has
promising potentials in a wide range of applications. However, MRF introduces
errors due to undersampling during the data acquisition process and the limit
of dictionary resolution. In this paper, we investigate the error source of MRF
and propose the technologies of improving the quality of MRF with compressed
sensing, error prediction by decision trees, and adaptive filtering.
Experimental results support our observations and show significant improvement
of the proposed technologies.
