Effects of image noise in muscle diffusion tensor (DT)-MRI assessed using numerical simulations

Diffusion tensor (DT)-MRI studies of skeletal muscle provide information about muscle architecture, microstructure, and damage. However, the effects of noise, the diffusion weighting (b)-value, and partial volume artifacts on the estimation of the diffusion tensor (D) are unknown. This study investigated these issues using Monte Carlo simulations of 3 x 9 voxel regions of interest (ROIs) containing muscle, adipose tissue, and intermediate degrees of muscle volume fractions (f(M)). A total of 1000 simulations were performed for each of eight b-values and 11 SNR levels. The dependencies of the eigenvalues (lambda(1-3)), mean diffusivity (lambda), and fractional anisotropy (FA), and the angular deviation of the first eigenvector from its true value (alpha) were observed. For moderate b-values (b = 435-725 s/mm(2)) and f(M) = 1, an accuracy of 5% was obtained for lambda(1-3), lambda, and FA with an SNR of 25. An accuracy of 1% was obtained for lambda(1-3), lambda, and FA with f(M) = 1 and SNR = 50. For regions with f(M) = 8/9, 5% accuracy was obtained with SNR = 40. For alpha, SNRs of >or=25 and >or=45 were required for +/-4.5 degrees uncertainty with f(M) = 1 and f(M) = 0.5, respectively; SNR >or= 60 was required for +/-9 degrees uncertainty in single muscle voxels. These findings may influence the design and interpretation of DT-MRI studies of muscle microstructure, damage, and architecture.