  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.
