Document Detail


A VARIATIONAL MODEL FOR DENOISING HIGH ANGULAR RESOLUTION DIFFUSION IMAGING.
MedLine Citation:
PMID:  22902985     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The presence of noise in High Angular Resolution Diffusion Imaging (HARDI) data of the brain can limit the accuracy with which fiber pathways of the brain can be extracted. In this work, we present a variational model to denoise HARDI data corrupted by Rician noise. Numerical experiments are performed on three types of data: 2D synthetic data, 3D diffusion-weighted Magnetic Resonance Imaging (DW-MRI) data of a hardware phantom containing synthetic fibers, and 3D real HARDI brain data. Experiments show that our model is effective for denoising HARDI-type data while preserving important aspects of the fiber pathways such as fractional anisotropy and the orientation distribution functions.
Authors:
M Tong; Y Kim; L Zhan; G Sapiro; C Lenglet; B A Mueller; P M Thompson; L A Vese
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Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging     Volume:  -     ISSN:  1945-8452     ISO Abbreviation:  Proc IEEE Int Symp Biomed Imaging     Publication Date:  2012  
Date Detail:
Created Date:  2012-8-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101492570     Medline TA:  Proc IEEE Int Symp Biomed Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  530-533     Citation Subset:  -    
Affiliation:
Dept. of Mathematics, University of California, Los Angeles.
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