MRF-ZOOM for the unbalanced steady-state free precession (ubSSFP) magnetic resonance fingerprinting

Abstract

In magnetic resonance fingerprinting (MRF), tissue parameters are determined by finding the best-match to the acquired MR signal from a predefined signal dictionary. This dictionary searching (DS) process is generally performed in an exhaustive manner, which requires a large predefined dictionary and long searching time. A fast MRF DS algorithm, MRF-ZOOM, was recently proposed based on DS objective function optimization. As a proof-of-concept study, MRF-ZOOM was only tested with one of the earliest MRF sequences but not with the recently more popular unbalanced steady state free precession MRF sequence (MRF-ubSSFP, or MRF-FISP). Meanwhile noise effects on MRF and MRF-ZOOM have not been examined. The purpose of this study was to address these open questions and to verify whether MRF-ZOOM can be combined with a dictionary-compression based method to gain further speed. Numerical simulations were performed to evaluate the DS objective function properties, noise effects on MRF, and to compare MRF-ZOOM with other methods in terms of speed and accuracy. In-vivo experiments were performed as well. Evaluation results showed that premises of MRF-ZOOM held for MRF-FISP; noise did not affect MRF-ZOOM more than the conventional MRF method; when SNR ≥ 1, MRF quantification yielded accurate results. Dictionary compression introduced quantification errors more to T2 quantification. MRF-ZOOM was thousands of times faster than the conventional MRF method. Combining MRF-ZOOM with dictionary compression showed no benefit in terms of fitting speed. In conclusion, MRF-ZOOM is valid for MRF- FISP, and can remarkably save MRF dictionary generation and searching time without sacrificing matching accuracy.