Meeting Banner
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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here