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Abstract #1570

Acceleration of Perfusion MRI using Adaptive Artificial Sparsity

Xiaoying Cai 1 , Feng Huang 2 , Kui Ying 3 , Chun Yuan 4 , and Huijun Chen 4

1 Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States, 2 Philips Healthcare, Gainesville, FL, United States, 3 Department of Engineering Physics, Tsinghua University, Beijing, China, 4 Center for Biomedical Imaging Research, Beijing, China

We proposed a new framework for reconstruction of accelerating perfusion imaging. By prediction intensity change of perfusion images using pre-contrast image and adaptive weights , the sparsity of dynamic images was strengthened, which benefits the following regular imaging reconstruction (k-t GRAPPA, k-t PCA or k-t SLR). The framework was tested with black blood vessel wall imaging data. The results showed that our new framework could result in better image reconstruction and improved kinetic parameter fitting for all tested reconstruction methods when acceleration factors were high.

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