Smoothing that does not blur: Effects of the anisotropic approach for evaluating diffusion tensor imaging data in the clinic

Purpose: To compare the effects of anisotropic and Gaussian smoothing on the outcomes of diffusion tensor imaging (DTI) voxel-based (VB) analyses in the clinic, in terms of signal-to-noise ratio (SNR) enhancement and directional information and boundary structures preservation. Materials and Methods: DTI data of 30 Alzheimer's disease (AD) patients and 30 matched control subjects were obtained at 3T. Fractional anisotropy (FA) maps with variable degrees and quality (Gaussian and anisotropic) of smoothing were created and compared with an unsmoothed dataset. The two smoothing approaches were evaluated in terms of SNR improvements, capability to separate differential effects between patients and controls by a standard VB analysis, and level of artifacts introduced by the preprocessing. Results: Gaussian smoothing regionally biased the FA values and introduced a high variability of results in clinical analysis, greatly dependent on the kernel size. On the contrary, anisotropic smoothing proved itself capable of enhancing the SNR of images and maintaining boundary structures, with only moderate dependence of results on smoothing parameters. Conclusion: Our study suggests that anisotropic smoothing is more suitable in DTI studies; however, regardless of technique, a moderate level of smoothing seems to be preferable considering the artifacts introduced by this manipulation.