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Author: Tabelow, Karsten × End date: 2019 × Start date: 2011 × Subject: Diffusion weighted magnetic resonance imaging ×

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    Adaptive smoothing of multi-shell diffusion-weighted magnetic resonance data by msPOAS
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Becker, Saskia; Tabelow, Karsten; Mohammadi, Siawoosh; Weiskopf, Nikolaus; Polzehl, Jörg
    In this article we present a noise reduction method (msPOAS) for multi-shell diffusionweighted magnetic resonance data. To our knowledge, this is the first smoothing method which allows simultaneous smoothing of all q-shells. It is applied directly to the diffusion weighted data and consequently allows subsequent analysis by any model. Due to its adaptivity, the procedure avoids blurring of the inherent structures and preserves discontinuities. MsPOAS extends the recently developed positionorientation adaptive smoothing (POAS) procedure to multi-shell experiments. At the same time it considerably simplifies and accelerates the calculations. The behavior of the algorithm msPOAS is evaluated on diffusion-weighted data measured on a single shell and on multiple shells.
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    Position-orientation adaptive smoothing of diffusion weighted magnetic resonance data (POAS)
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2011) Becker, Saskia; Tabelow, Karsten; Voss, Henning U.; Anwander, Alfred; Heidemann, Robin M.; Polzehl, Jörg
    We introduce an algorithm for diffusion weighted magnetic resonance imaging data enhancement based on structural adaptive smoothing in both space and diffusion direction. 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 and group operations, 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. The position-orientation adaptive smoothing preserves the edges of the observed fine and anisotropic structures. The POAS-algorithm 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 similar quality of data, or to improve the quality of data acquired in a clinically feasible scan time setting.