title: Diffusion tensor imaging at low SNR: nonmonotonic behaviors of tensor contrasts.
abstract: Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in the tissue microarchitecture of the brain. DTI is gaining prominence in applications outside of the brain, where resolution, motion and short T2 values often limit the achievable signal-to-noise ratio (SNR). Consequently, it is important to revisit the topic of tensor estimation in low-SNR regimes. A theoretical framework is developed to model noise in DTI, and by using simulations based on this theory, the degree to which the noise, tensor estimation method and acquisition protocol affect tensor-derived quantities, such as fractional anisotropy and apparent diffusion coefficient, is clarified. These results are then validated against clinical data. It is shown that reliability of tensor contrasts depends on the noise level, estimation method, diffusion-weighting scheme and underlying anatomy. The propensity for bias and errors does not monotonically increase with noise. Comparative results are shown in both graphical and tabular forms, so that decisions about suitable acquisition protocols and processing methods can be made on a case-by-case basis without exhaustive experimentation.
authors.affiliation: Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. bennett@bme.jhu.edu
authors.forename: Bennett A
authors.initials: BA
authors.lastname: Landman
authors.forename: Jonathan A D
authors.initials: JA
authors.lastname: Farrell
authors.forename: Hao
authors.initials: H
authors.lastname: Huang
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical