Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging.
JOURNAL OF MAGNETIC RESONANCE IMAGING
702 - 710.
Calculation and sorting of the eigenvectors of diffusion sing diffusion tensor imaging has previously been shown to be sensitive to noise levels in the acquired data. This sensitivity manifests as random and systematic errors in the diffusion eigenvalues and derived parameters such as indices of anisotropy. An optimized application of nonlinear smoothing techniques to diffusion data prior to calculation of the diffusion tensor is shown to reduce both random and systematic errors, while causing little blurring of anatomical structures. Conversely, filtering applied to calculated images of fractional anisotropy is shown to fail in reducing systematic errors and in recovering anatomical detail. Using both real and simulated brain data sets, it is demonstrated that this approach has the potential to allow acquisition of data that would otherwise be too noisy to be of use. J. Magn. Reson. Imaging 2000;11:702-710. (C) 2000 Wiley-Liss, Inc.
|Title:||Nonlinear smoothing for reduction of systematic and random errors in diffusion tensor imaging|
|Keywords:||diffusion tensor imaging, noise reduction, systematic errors, signal-to-noise ratio, EDGE-DETECTION, ANISOTROPY|
|UCL classification:||UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Brain Repair and Rehabilitation
UCL > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Neurology > Clinical and Experimental Epilepsy
UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
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