MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting

TL;DR

MRF-ZOOM is a novel, efficient dictionary searching algorithm for magnetic resonance fingerprinting that significantly reduces computation time and space requirements, enabling practical high-resolution imaging.

Contribution

This paper introduces MRF ZOOM, a parameter separable, multi-resolution DGS method that accelerates MRF dictionary searching by hundreds to thousands of times compared to brute-force approaches.

Findings

MRF ZOOM is hundreds to thousands of times faster than brute-force methods.

It enables high-resolution MRF imaging in approximately 117 seconds for a 64x64 slice.

Spatial constraints further enhance the speed of MRF ZOOM.

Abstract

Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously quantifying multiple MR parameters using one temporally resolved MR scan. But its brute-force dictionary generating and searching (DGS) process causes a huge disk space demand and computational burden, prohibiting it from a practical multiple slice high-definition imaging. The purpose of this paper was to provide a fast and space efficient DGS algorithm for MRF. Based on an empirical analysis of properties of the distance function of the acquired MRF signal and the pre-defined MRF dictionary entries, we proposed a parameter separable MRF DGS method, which breaks the multiplicative computation complexity into an additive one and enabling a resolution scalable multi-resolution DGS process, which was dubbed as MRF ZOOM. The evaluation results showed that MRF ZOOM was hundreds or thousands of times faster than the original brute-force DGS method. The acceleration was even higher when considering the time difference for generating the dictionary. Using a high precision quantification, MRF can find the right parameter values for a 64x64 imaging slice in 117 secs. Our data also showed that spatial constraints can be used to further speed up MRF ZOOM.