Diffusion weighting along more than one direction at a time (tensor-valued encoding) can be used to probe features of the microstructure that are not accessible by conventional encoding. For example, it enables diffusional variance decomposition (DIVIDE) which can separate the effects of microscopic anisotropy, orientation dispersion, and heterogeneous isotropic diffusivity. Tensor-valued encoding is usually demanding with respect to gradient performance, limiting its applicability to high-performance MRI systems. However, a recent method for optimized encoding significantly reduced the demand on gradient performance, which warrants an investigation of the applicability of such encoding on a wider range of MRI hardware configurations. In this study, we demonstrate whole-brain diffusional variance decomposition (DIVIDE) in less than 8 minutes at a wide range of clinical MRI systems with different hardware configurations.
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