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Rician Noise Removal in Diffusion Tensor MRI

Institution:
University of Utah, School of Computing, Salt Lake City, UT, USA.
Publisher:
Med Image Comput Comput Assist Interv. MICCAI 2006
Publication Date:
Oct-2006
Volume Number:
9
Issue Number:
Pt 1
Pages:
117-25
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2006 Oct;9(Pt 1):117-25.
PubMed ID:
17354881
Keywords:
DTI Volumetric White Matter Connectivity, DTI Rician Noise Removal, Projects:DTIProcessingTools, Projects:DTIVolumetricWhiteMatterConnectivity
Appears in Collections:
NA-MIC
Sponsors:
P41 RR012553/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Basu S., Fletcher P.T., Whitaker R.T. Rician Noise Removal in Diffusion Tensor MRI. Int Conf Med Image Comput Comput Assist Interv. 2006 Oct;9(Pt 1):117-25. PMID: 17354881.
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Rician noise introduces a bias into MRI measurements that can have a significant impact on the shapes and orientations of tensors in diffusion tensor magnetic resonance images. This is less of a problem in structural MRI, because this bias is signal dependent and it does not seriously impair tissue identification or clinical diagnoses. However, diffusion imaging is used extensively for quantitative evaluations, and the tensors used in those evaluations are biased in ways that depend on orientation and signal levels. This paper presents a strategy for filtering diffusion tensor magnetic resonance images that addresses these issues. The method is a maximum a posteriori estimation technique that operates directly on the diffusion weighted images and accounts for the biases introduced by Rician noise. We account for Rician noise through a data likelihood term that is combined with a spatial smoothing prior. The method compares favorably with several other approaches from the literature, including methods that filter diffusion weighted imagery and those that operate directly on the diffusion tensors.

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BasuDTIFilteringMICCAI2006-fig5.jpg (591.369kB)