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On the selection of sampling points for myocardial T 1 mapping
Author(s) -
Akçakaya Mehmet,
Weingärtner Sebastian,
Roujol Sébastien,
Nezafat Reza
Publication year - 2015
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25285
Subject(s) - sampling (signal processing) , imaging phantom , estimator , noise (video) , algorithm , selection (genetic algorithm) , computer science , mathematics , statistics , artificial intelligence , computer vision , physics , filter (signal processing) , optics , image (mathematics)
Purpose To provide a method for the optimal selection of sampling points for myocardial T 1 mapping, and to evaluate how this selection affects the precision. Theory The Cramér–Rao lower bound on the variance of the unbiased estimator was derived for the sampling of the longitudinal magnetization curve, as a function of T 1 , signal‐to‐noise ratio, and noise mean. The bound was then minimized numerically over a search space of possible sampling points to find the optimal selection of sampling points. Methods Numerical simulations were carried out for a saturation recovery‐based T 1 mapping sequence, comparing the proposed point selection method to a uniform distribution of sampling points along the recovery curve for various T 1 ranges of interest, as well as number of sampling points. Phantom imaging was performed to replicate the scenarios in numerical simulations. In vivo imaging for myocardial T 1 mapping was also performed in healthy subjects. Results Numerical simulations show that the precision can be improved by 13–25% by selecting the sampling points according to the target T 1 values of interest. Results of the phantom imaging were not significantly different than the theoretical predictions for different sampling strategies, signal‐to‐noise ratio and number of sampling points. In vivo imaging showed precision can be improved in myocardial T 1 mapping using the proposed point selection method as predicted by theory. Conclusion The framework presented can be used to select the sampling points to improve the precision without penalties on accuracy or scan time. Magn Reson Med 73:1741–1753, 2015. © 2014 Wiley Periodicals, Inc.