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Abstract #2638

Augmented JSENSE: Faster Convergence and Less Sensitive to Regularization Parameter

Meng Liu1, Yunmei Chen1, Yuyuan Ouyang1, Xiaojing Ye2, Xiaodong Ma3, Feng Huang4

1Department of Mathematics, University of Florida, Gainesville, FL, United States; 2School of Mathematics, Georgia Institute of Technology, Atlanta, GA, United States; 3Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China; 4Philips Healthcare, Shanghai, China

Partially parallel imaging has been used routinely for many MR applications. SENSE is one of the most commonly used methods, theoretically resulted in the optimal signal-to-noise ratio. However, SENSE reconstruction is highly depending on the accuracy of coil sensitivity maps. Several iterative methods were proposed to jointly reconstruct image and estimate sensitivity maps, have demonstrated the improved accuracy of coil sensitivity maps and the SENSE reconstruction quality. However, they suffer two numerical problems. One is the sensitivity to choosing regularization parameters; the other is the high computational cost. The target of this work is to tackle these two existing issues.