Abstract #0748
Integrating Principal Component Analysis and Dictionary Learning with Coherence Constraint for Fast T 1 Mapping
Yanjie Zhu 1 , Qiegen Liu 2 , Qinwei Zhang 3 , Jing Yuan 3 , and Dong Liang 1
1
Paul C. Lauterbur Research Centre for
Biomedical Imaging, Shenzhen Institutes of Advanced
Technology, Chinese Academy of Sciences, Shenzhen,
Guangdong, China,
2
Department
of Electronic Information Engineering, Nanchang
University, Nanchang, Jiangxi, China,
3
Department
of Imaging and Interventional Radiology, The Chinese
University of Hong Kong, Shatin, Hong Kong
Long scanning time hinders the widespread application of
T1 in clinics. A new approach utilizing the advantages
of both fixed and adaptive transform is proposed to
accelerate T1 imaging under the framework of
compressed sensing. Specifically, PCA is applied first
along the parameter direction, and the dictionary
learning technique is used to reconstruct the PC
coefficients. Additionally, a coherence constraint is
introduced to guarantee the sparse representation
ability of learned dictionary. Experimental results
demonstrate that the proposed method can improve the
accuracy of estimated T1 map compared with the one
without coherence constraint and conventional dictionary
learning based method.
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