Myocardial T2∗ mapping: Influence of noise on accuracy and precision

Background: pixel-wise,parametric t2∗ mapping is emerging as a means of automatic measurement of iron content in tissues. it enables quick,intuitive interpretation and provides the potential benefit of spatial context between tissues. however,pixel-wise mapping uses much lower snr data to estimate t2∗ when compared to region-based mapping thereby decreasing both its accuracy and precision. in this study,the effects that noise has on the precision and accuracy of pixel-wise t2∗ mapping were investigated and techniques to mitigate those effects are proposed. methods: to study precision across t2∗ mapping techniques,a pipeline to estimate the pixel-wise standard deviation (sd) of the t2∗ based on the fit residuals is proposed. for validation,a monte-carlo analysis was performed in which t2∗ phantoms were scanned n = 64 times,the true sd was measured and compared to the estimated sd. to improve accuracy and precision,the automatic truncation method for mitigating noise bias was extended to pixel-wise fitting by using an snr scaled image reconstruction and truncating low snr measurements. finally,the precision and accuracy of non-linear regression with and without automatic truncation,were investigated using monte-carlo simulations. results: measured and estimated sd's were >99.9% correlated for non-linear regression with and without truncation. non-linear regression with automatic truncation was shown to be the best mapping technique for improving accuracy and precision in low t2∗ and low snr measurements. conclusions: a method for applying an automatic truncation method to pixel-wise t2∗ mapping that reduces t2∗ overestimation due to noise bias was proposed. a formulation for estimating pixel-wise standard deviation (sd) maps for t2∗ that can serve as a quality map for interpreting images and for comparison of imaging protocols was also proposed and validated. © 2015 sandino et al.