Abstract：
BACKGROUND:The low quality of diffusion tensor image (DTI) could affect the accuracy of oncology diagnosis. METHODS:We present a novel sparse representation based denoising method for three dimensional DTI by learning adaptive dictionary with the context redundancy between neighbor slices. In this study, the context redundancy among the adjacent slices of the diffusion weighted imaging volumes is utilized to train sparsifying dictionaries. Therefore, higher redundancy could be achieved for better description of image with lower computation complexity. The optimization problem is solved efficiently using an iterative block-coordinate relaxation method. RESULTS:The effectiveness of our proposed method has been assessed on both simulated and real experimental DTI datasets. Qualitative and quantitative evaluations demonstrate the performance of the proposed method on the simulated data. The experiments on real datasets with different b-values also show the effectiveness of the proposed method for noise reduction of DTI. CONCLUSIONS:The proposed approach well removes the noise in the DTI, which has high potential to be applied for clinical oncology applications.
journal_name
Biomed Eng Onlinejournal_title
Biomedical engineering onlineauthors
Kong Y,Li Y,Wu J,Shu Hdoi
10.1186/s12938-015-0116-3subject
Has Abstractpub_date
2016-01-13 00:00:00pages
5issn
1475-925Xpii
10.1186/s12938-015-0116-3journal_volume
15pub_type
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journal_title：Biomedical engineering online
pub_type： 杂志文章,评审
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journal_title：Biomedical engineering online
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