The influence of the analysis technique on myocardial T2 estimation using cardiac magnetic resonance imaging (CMR)
Cardiovascular disease is the main cause of death worldwide. Magnetic resonance imaging (MRI) has been considered as a noninvasive technique for characterizing myocardial tissues. Specifically, T2 mapping technique has been demonstrated to be an excellent tool to detect myocardial edema by estimation of the transverse relaxation time constant (T2) of myocardial tissue. However, there are several factors that could influence the analysis technique and affect the estimating T2 value. These factors include the type of exponential fitting model, which either be single exponential or exponential plus constant fitting model, the signal intensity estimation method, which either be Average, Median or Mapping method and signal-to-noise ratio (SNR) level of the T2-weighted images. In this paper, we discuss the effect of these factors on T2 estimation using a numerical phantom, T2 calibrated phantoms with different T2 values, and human subjects. The results of numerical phantom showed that the average percentage error for T2 measurement when using the single exponential fitting model reached 0.6 %, 0.4 %, and 5.9 % for the Average, Median, and Mapping methods, respectively. However, the exponential plus constant fitting model resulted in high average percentage error among the three signal intensity estimation methods (above 26 %). The experiments of calibrated phantom resulted in high correlation (R2 > 0.99) between the estimated and reference T2 values when using the single exponential fitting model compared to low correlation (R2 < 0.73) when using the exponential plus constant fitting model at high SNR levels. Finally, the results of human subjects were in agreement with both numerical and calibrated phantoms. Based on the results of this paper, the single exponential fitting model with the median estimation method is the preferable analysis technique for T2 measurement as it results in a lower error for T2 measurements at low SNR levels and higher correlation values at high SNR levels compared to the other analysis techniques.