A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

Abstract

Inspired by the recently proposed magnetic resonance fingerprinting (MRF) technique, we develop a principled compressed sensing framework for quantitative MRI. The three key components are a random pulse excitation sequence following the MRF technique, a random EPI subsampling strategy, and an iterative projection algorithm that imposes consistency with the Bloch equations. We show that, theoretically, as long as the excitation sequence possesses an appropriate form of persistent excitation, we are able to accurately recover the proton density, T1, T2, and off-resonance maps simultaneously from a limited number of samples. These results are further supported through extensive simulations using a brain phantom.