Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)

We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both voxel space and diffusion-gradient space. The method, called POAS, does not refer to a specific model for the data, like the diffusion tensor or higher order models. It works by embedding the measurement space into a space with defined metric, in this case the Lie group of three-dimensional Euclidean motion SE(3). Subsequently, pairwise comparisons of the values of the diffusion weighted signal are used for adaptation. POAS preserves the edges of the observed fine and anisotropic structures. It is designed to reduce noise directly in the diffusion weighted images and consequently also to reduce bias and variability of quantities derived from the data for specific models. We evaluate the algorithm on simulated and experimental data and demonstrate that it can be used to reduce the number of applied diffusion gradients and hence acquisition time while achieving a similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.

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► Structure preserving smoothing algorithm for diffusion weighted imaging data.
► Method is based on the geometry of the measurement space.
► Demonstrates a significant improvement of image quality for experimental data.
► Offers potential for a significant reduction of acquisition time.
► Implemented within a freely available package DTI (GPL 2) for the R Language and Environment for Statistical Computing.