Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging

Calculation and sorting of the eigenvectors of diffusion sing diffusion ten sor imaging has previously been shown to be sensitive to noise levels in th e acquired data. This sensitivity manifests as random and systematic errors in the diffusion eigenvalues and derived parameters such as indices of ani sotropy. An optimized application of nonlinear smoothing techniques to diff usion data prior to calculation of the diffusion tensor is shown to reduce both random and systematic errors, while causing little blurring of anatomi cal structures. Conversely, filtering applied to calculated images of fract ional anisotropy is shown to fail in reducing systematic errors and in reco vering anatomical detail. Using both real and simulated brain data sets, it is demonstrated that this approach has the potential to allow acquisition of data that would otherwise be too noisy to be of use. J. Magn. Reson. Ima ging 2000;11:702-710. (C) 2000 Wiley-Liss, Inc.