Analysis of multi-exponential relaxation data with very short components using linear regularization.
Linear regularization is a common and robust technique for fitting multi-exponential relaxation decay data to obtain a distribution of relaxation times. The regularization algorithms employed by the Uniform-Penalty inversion (UPEN) and CONTIN computer programs have been compared using simulated transverse (T2) relaxation data derived from a typical bimodal distribution observed in cartilage tissue which contain a component shorter than t(0), the time of the first decay sample. We examined the reliability of detecting sub-t(0) relaxation components and the accuracy of statistical estimates of T2 distribution parameters. When the integrated area of the sub-t(0) component relative to that of the total distribution was greater than 0.25, our results indicated a signal-to-noise threshold of about 300 for detecting the presence of the sub-t(0) component with a probability of 0.9 or greater. This threshold was obtained using both the UPEN and CONTIN algorithms. In addition, when using the second-derivative-squared regularizer, UPEN solutions provided statistical estimates of T2 distribution parameters which were substantially free of the biasing effect of the regularizer observed in analagous CONTIN solutions.
Department of Physics, Oakland University, 190 Science and Engineering Building, Rochester, MI 48309-4487, USA.
Jonathan B
JB
Moody
Yang
Y
Xia
D000465:Algorithms; D002356:Cartilage / Q000033:anatomy & histology* / Q000502:physiology*; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes