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Abstract #4417

Variable Rates Undersampling Scheme for Fast brain T1ρ mapping

Yuanyuan Liu1, Yanjie Zhu1, Jing Cheng1, Xin Liu1, and Dong Liang1,2

1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Research center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

T mapping requires several 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 work, we developed a variable acceleration rates undersampling strategy to reduce the scan time. A signal compensation with low-rank plus sparse model was used to reconstruct the T-weighted images. Specifically, a feature descriptor was used to pick up useful features from the residual images. Preliminary results show that the proposed method achieves a 5.76-fold acceleration and obtain more accurate T maps than the existing methods.

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