Selective Encoding Through Nutation and Fingerprinting (SENF): A Gradient-Free Quantitative RF Encoding Technique

Abstract

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous acquisition of multiple quantitative parameter maps in a single acquisition, but still requires B0 gradients for spatial encoding. These B0 gradients cause patient discomfort through auditory noise and peripheral nerve stimulation, and increase the cost of an MRI scanner. To address these problems, a new method of encoding MRI data called Selective Encoding through Nutation and Fingerprinting (SENF) was developed that combines MRF and B1+-selective excitation pulses. SENF is a B0 gradient-free RF encoding technique that simultaneously encodes quantitative and spatial information to produce multiple quantitative parameter maps. Three different SENF sequences were developed using different B1-dependent pulse effects, then compared to conventional T1 inversion recovery and T2 spin echo sequences in simulation. Hardware was developed to enable SENF implementation on a 47.5 mT scanner. The first 1D SENF reconstructions were then acquired on a 47.5 mT scanner, laying the foundation for future work of developing SENF into a clinical MRI encoding method.