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Diffusion Tensor Imaging: Structural adaptive smoothing

Please always quote using this URN:urn:nbn:de:0296-matheon-3993
  • Diffusion Tensor Imaging (DTI) data is characterized by a high noise level. Thus, estimation errors of quantities like anisotropy indices or the main diffusion direction used for fiber tracking are relatively large and may significantly confound the accuracy of DTI in clinical or neuroscience applications. Besides pulse sequence optimization, noise reduction by smoothing the data can be pursued as a complementary approach to increase the accuracy of DTI. Here, we suggest an anisotropic structural adaptive smoothing procedure, which is based on the Propagation-Separation method and preserves the structures seen in DTI and their different sizes and shapes. It is applied to artificial phantom data and a brain scan. We show that this method significantly improves the quality of the estimate of the diffusion tensor and hence enables one either to reduce the number of scans or to enhance the input for subsequent analysis such as fiber tracking.

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Metadaten
Author:Karsten Tabelow, Jörg Polzehl, Vladimir Spokoiny, Henning U. Voss
URN:urn:nbn:de:0296-matheon-3993
Referee:Christof Schütte
Document Type:Preprint, Research Center Matheon
Language:English
Date of first Publication:2008/03/13
Release Date:2008/01/17
Tag:
Institute:Weierstraß-Institut für Angewandte Analysis und Stochastik (WIAS)
MSC-Classification:62-XX STATISTICS / 62Gxx Nonparametric inference / 62G05 Estimation
62-XX STATISTICS / 62Gxx Nonparametric inference / 62G10 Hypothesis testing
62-XX STATISTICS / 62Pxx Applications [See also 90-XX, 91-XX, 92-XX] / 62P10 Applications to biology and medical sciences
92-XX BIOLOGY AND OTHER NATURAL SCIENCES / 92Cxx Physiological, cellular and medical topics / 92C55 Biomedical imaging and signal processing [See also 44A12, 65R10]
Preprint Number:482
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