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Abstract #3549

Accelerating T2 Mapping Using a Self-trained Kernel PCA Model

Chaoyi Zhang1, Ukash Nakarmi1, Hongyu Li1, Yihang Zhou2, Dong Liang3, and Leslie Ying1,4

1Electrical Engineering, University at Buffalo, SUNY, Buffalo, NY, United States, 2Medical Physics and Research department, Hong Kong Sanatorium & Hospital, Happy Valley, Hong Kong, 3Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Shenzhen, China, 4Biomedical Engineering, University at Buffalo, SUNY, Buffalo, NY, United States

Kernel Principal component analysis(KPCA) model has recently been proposed to accelerate dynamic cardiac imaging. In this abstract, we study the effectiveness of KPCA for MR T2 mapping from highly under-sampled data acquired at different echo time. Different from dynamic cardiac imaging where only morphological information is needed, the quantitative values are highly important in parameter mapping. Here we use a self-trained KPCA model to guarantee the accuracy of the reconstructed T2 maps. The experimental results show that the proposed method can recover the T2 map with high fidelity at high acceleration factors.

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