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.