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Abstract #1471

Accelerating T1Ρ mapping using patch-based low-rank tensor

Yuanyuan Liu1, Zhuo-Xu Cui1, Xin Liu1, Dong Liang1, and Yanjie Zhu1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

T mapping requires the acquisition of multiple T-weighted images with different spin lock times to obtain the T maps, resulting in a long scan time. Compressed sensing has shown good performance in fast quantitative T mapping. In this study, we used a patch-based low-rank tensor imaging method to reconstruct the T-weighted images from highly undersampled data. Preliminary results show that the proposed method achieves a 6-fold acceleration and obtains more accurate T maps than the existing methods.

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