Algorithm for fast monoexponential fitting based on Auto-Regression on Linear Operations (ARLO) of data.

PURPOSE: To develop a fast and accurate monoexponential fitting algorithm based on Auto-Regression on Linear Operations (ARLO) of data, and to validate its accuracy and computational speed by comparing it with the conventional Levenberg-Marquardt (LM) and Log-Linear (LL) algorithms.

METHODS: ARLO, LM, and LL performances for T2* mapping were evaluated in simulation and in vivo imaging of liver (n=15) and myocardial (n=1) iron overload patients and the brain (two healthy volunteers).

RESULTS: In simulations, ARLO consistently delivered accuracy similar to LM and significantly superior to LL. In in vivo mapping of T2 * values, ARLO showed excellent agreement with LM, while LL showed only limited agreements with ARLO and LM. Compared with LM and LL in the liver, ARLO was 125 and 8 times faster using our Matlab implementations, and 156 and 13 times faster using our C++ implementations. In C++ implementations, ARLO reduced the online whole-brain processing time from 9 min 15 s of LM and 35 s of LL to 2.7 s, providing T2 * maps approximately in real time.

CONCLUSION: Due to comparable accuracy and significantly higher speed, ARLO can be considered as a valid alternative to the conventional LM algorithm for online T2 * mapping.