TY - GEN
T1 - Low-dose CT reconstruction based on multiscale dictionary
AU - Bai, Ti
AU - Mou, Xuanqin
AU - Xu, Qiong
AU - Zhang, Yanbo
PY - 2013
Y1 - 2013
N2 - Statistical CT reconstruction using penalized weighted least-squares(PWLS) criteria can improve image-quality in low-dose CT reconstruction. A suitable design of regularization term can benefit it very much. Recently, sparse representation based on dictionary learning has been treated as the regularization term and results in a high- quality reconstruction. In this paper, we incorporated a multiscale dictionary into statistical CT reconstruction, which can keep more details compared with the reconstruction based on singlescale dictionary. Further more, we exploited reweigted 1 norm minimization for sparse coding, which performs better than 1 norm minimization in locating the sparse solution of underdetermined linear systems of equations. To mitigate the time consuming process that computing the gradiant of regularization term, we adopted the so-called double surrogates method to accelerate ordered-subsets image reconstruction. Experiments showed that combining multiscale dictionary and reweighted 1 norm minimization can result in a reconstruction superior to that bases on singlescale dictionary and 1 norm minimization.
AB - Statistical CT reconstruction using penalized weighted least-squares(PWLS) criteria can improve image-quality in low-dose CT reconstruction. A suitable design of regularization term can benefit it very much. Recently, sparse representation based on dictionary learning has been treated as the regularization term and results in a high- quality reconstruction. In this paper, we incorporated a multiscale dictionary into statistical CT reconstruction, which can keep more details compared with the reconstruction based on singlescale dictionary. Further more, we exploited reweigted 1 norm minimization for sparse coding, which performs better than 1 norm minimization in locating the sparse solution of underdetermined linear systems of equations. To mitigate the time consuming process that computing the gradiant of regularization term, we adopted the so-called double surrogates method to accelerate ordered-subsets image reconstruction. Experiments showed that combining multiscale dictionary and reweighted 1 norm minimization can result in a reconstruction superior to that bases on singlescale dictionary and 1 norm minimization.
KW - Double surrogates
KW - Multiscale dictionary
KW - Reweighted 1 norm minimization
KW - Singlescale dictionary
KW - Sparsity
UR - http://www.scopus.com/inward/record.url?scp=84878317588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878317588&partnerID=8YFLogxK
U2 - 10.1117/12.2008140
DO - 10.1117/12.2008140
M3 - Conference contribution
AN - SCOPUS:84878317588
SN - 9780819494429
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2013
T2 - Medical Imaging 2013: Physics of Medical Imaging
Y2 - 11 February 2013 through 14 February 2013
ER -