Application of SVD-based sparsity in compressed sensing susceptibility weighted imaging

Long scan time has hampered susceptibility weighted imaging (SWI) in routine clinical application to diagnose brain diseases related to venous vasculature. Compressed sensing (CS) was demonstrated to significantly reduce scan time of SWI by exploiting signal sparsity in wavelet domain. However the r...

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Bibliographic Details
Published in2012 5th International Conference on Biomedical Engineering and Informatics pp. 447 - 450
Main Authors Wei Chen, Zhaoyang Jin, Feng Liu, Du, Yiping P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
compressed sensing
singular value decomposition
susceptibility weighted imaging
wavelet transform
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Summary:Long scan time has hampered susceptibility weighted imaging (SWI) in routine clinical application to diagnose brain diseases related to venous vasculature. Compressed sensing (CS) was demonstrated to significantly reduce scan time of SWI by exploiting signal sparsity in wavelet domain. However the reconstruction time of CS based on wavelet sparsity is usually time consuming. In this study, the feasibility of applying CS in SWI with singular value decomposition (SVD)-based sparsity basis was investigated. It was found that CS reconstruction based on SVD sparsity basis can achieve reasonably high computing speed than that of wavelet-based sparsity basis, while still achieving accurate image reconstruction.
ISBN:9781467311830
1467311839
DOI:10.1109/BMEI.2012.6513159