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Magn Reson Med. 2015 Feb;73(2):843-50. doi: 10.1002/mrm.25137. Epub 2014 Mar 24.

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

Author information

1
Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China; Yifu Inc, Jiaxing, Zhejiang Province, China.

Abstract

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.

KEYWORDS:

Levenberg-Marquardt; Log-Linear; T2* mapping; autoregression; iron overload

PMID:
24664497
PMCID:
PMC4175304
DOI:
10.1002/mrm.25137
[Indexed for MEDLINE]
Free PMC Article

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