PURPOSE: To provide a method for the optimal selection of sampling points for myocardial T1 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 T1 , 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 T1 mapping sequence, comparing the proposed point selection method to a uniform distribution of sampling points along the recovery curve for various T1 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 T1 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 T1 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 T1 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.
PURPOSE: To provide a method for the optimal selection of sampling points for myocardial T1 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 T1 , 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 T1 mapping sequence, comparing the proposed point selection method to a uniform distribution of sampling points along the recovery curve for various T1 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 T1 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 T1 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 T1 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.
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