Table 2. Accuracy and precision of fit parameters from Monte Carlo simulations.

Model	Parameter	SNR = 78,000		SNR = 100		SNR = 40
		Accuracy (%)	Precision (%)	Accuracy (%)	Precision (%)	Accuracy (%)	Precision (%)
Mono-exponential	T2	3.36e-5	7.45e-4	-0.22	1.54	-1.13	3.75
Stretched exponential	T2,se	-8.54e-6	9.60e-4	0.12	2.27	0.06	5.89
	αse	-1.20e-4	1.37e-3	0.38	3.03	1.66	7.93
Stretched Mittag-Leffler	T2,sml	-4.57e-5	7.81e-4	-0.11	1.64	-0.34	5.12
	αsml	-3.56e-4	4.94e-4	-0.31	1.27	0.96	3.46
Biexponental	w1	2.21e-3	2.58e-2	-25.47	35.27	-38.69	35.26
	T2,1	7.14e-4	2.43e-2	-24.89	30.41	-30.29	45.25
	T2,2	2.87e-4	4.17e-3	-3.25	5.36	-4.12	7.29

Each sample condition was simulated with 200 noise realizations. SNR = 78,000 was used, representing the average experimental SNR from in vitro BNC experiments. Two more modest levels, SNR = 100 and SNR = 40, were also simulated representing typical levels for clinical imaging. The average T₂, α, and w values derived experimentally from control BNC were used as input values. Decay data for the high SNR condition were simulated using 1024 echoes with TE = 1.2 ms. Decay data using SNR = 100 and 40 were simulated using typical imaging parameters consisting of 64 echoes and TE= 8 ms. Accuracy is defined as fit parameter percent error, which is the difference between the true simulation input parameter and the average fit value over 200 noise realizations, divided by the true input simulation parameter value, all multiplied by 100. Precision is reported as the percent relative standard deviation, which is the standard deviation of fit residuals over 200 noise realizations divided by the mean fit value of the parameter, multiplied by 100.