Abstract
Diffusion measurements in the human central nervous system are complex to characterize and a broad spectrum of methods have been proposed. In this study, a comprehensive diffusion encoding and analysis approach, hybrid diffusion imaging (HYDI), is described. The HYDI encoding scheme is composed of multiple concentric "shells" of constant diffusion weighting, which may be used to characterize the signal behavior with low, moderate and high diffusion weighting. HYDI facilitates the application of multiple data analyses strategies including diffusion tensor imaging (DTI), multi-exponential diffusion measurements, diffusion spectrum imaging (DSI) and q-ball imaging (QBI). These different analysis strategies may provide complementary information. DTI measures (mean diffusivity and fractional anisotropy) may be estimated from either data in the inner shells or the entire HYDI data. Fast and slow diffusivities were estimated using a nonlinear least squares bi-exponential fit on geometric means of the HYDI shells. DSI measurements from the entire HYDI data yield empirical model-independent diffusion information and are well-suited for characterizing tissue regions with complex diffusion behavior. DSI measurements were characterized using the zero displacement probability and the mean-squared displacement. The outermost HYDI shell was analyzed using QBI analysis to estimate the orientation distribution function (ODF), which is useful for characterizing the directions of multiple fiber groups within a voxel. In this study, an HYDI encoding scheme with 102 diffusion-weighted measurements was obtained over most of the human cerebrum in under 30 min.