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Abstract #3127

Accurate T2 Mapping with Sparisty and Linear Predictability Filtering

Xi Peng 1 , Leslie Ying 2 , Xin Liu 1 , and Dong Liang 1

1 Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong, China, 2 Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, New York, United States

Accelerating the acquisition of T2 mapping via sparse sampling has drawn considerable attention. However, due to non-ideal conditions in practical settings (i.e., insufficient sparsity/rank and coherent sampling), errors occur in the T2-weighted images and the subsequent relaxation map especially with high reduction factors and noisy measurements. We address this issue by integrating the prior information (i.e., exponential functions) on the temporal signals into the image reconstruction step. This is in contrast to the conventional wisdom where the image reconstruction and parameter mapping are performed independently. The proposed method was demonstrated with an in-vivo brain dataset and shows promising results.

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