ArticleMagnetic resonance in medicine2015
Algorithm for fast monoexponential fitting based on Auto-Regression on Linear Operations (ARLO) of data.
Article in Magnetic resonance in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 58 papers.
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58 citing papers in PubMed.
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- Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.Magnetic resonance in medicine · 2026Article
- Addressing fatty tissue in quantitative susceptibility mapping of human knee cartilage.Magma (New York, N.Y.) · 2025Article
- MRI R2* and quantitative susceptibility mapping in brain tissue with extreme iron overload.European radiology experimental · 2025Article
- Quantitative susceptibility mapping of the human carotid artery: Assessing sensitivity to elastin and collagen ex vivo.Magnetic resonance in medicine · 2025Article
- The statistical impact of ROI referencing on quantitative susceptibility mapping.Magma (New York, N.Y.) · 2025Article
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Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
purposeTo 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.
methodsARLO, 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).
resultsIn 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.
conclusionDue to comparable accuracy and significantly higher speed, ARLO can be considered as a valid alternative to the conventional LM algorithm for online T2 * mapping.
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