期刊
MEDICAL IMAGE ANALYSIS
卷 16, 期 6, 页码 1142-1155出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.media.2012.05.007
关键词
Diffusion weighted magnetic resonance imaging; POAS; Structural adaptive smoothing; Special Euclidean motion group; Lie groups
类别
资金
- DFG Research Center MATHEON
- Stiftung der Deutschen Wirtschaft (SDW)
- Nancy M. and Samuel C. Fleming Research Scholar Award, Cornell University
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. (c) 2012 Elsevier B.V. All rights reserved.
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