Quantitative T₂ analysis: The effects of noise, regularization, and multivoxel approaches

Typical quantitative T₂ (qT₂) analysis involves creating T₂ distributions using a regularized algorithm from region-of-interest averaged decay data. This study uses qT₂ analysis of simulated and experimental decay signals to determine how (a) noise-type, (b) regularization, and (c) region-of-interest versus multivoxel analyses affect T₂ distributions. Our simulations indicate that regularization causes myelin water fraction and intra/extracellular water geometric mean T ₂ underestimation that worsens as the signal-to-noise ratio decreases. The underestimation was greater for intra/extracellular water geometric mean T₂ measures using Rician noise. Simulations showed significant differences between myelin water fractions determined using region-of-interest and multivoxel approaches compared to the true value. The nonregularized voxel-based approach gave the most accurate measure of myelin water fraction and intra/extracellular water geometric mean T₂ for a given signal-to-noise ratio and noise type. Additionally, multivoxel analysis provides important information about the variability of the analysis. Results obtained from in vivo rat data were similar to our simulation results. In each case, a nonregularized, multivoxel analysis provided myelin water fractions significantly different from the regularized approaches and obtained the largest myelin water fraction. We conclude that quantitative T₂ analysis is best performed using a nonregularized, multivoxel approach.