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Abstract #4655

TV Regularization for Segmented GRAPPA with Higher Net Acceleration Factor

Yunmei Chen1, Xiaojing Ye1, Haili Zhang1, Jiangli Shi1, Feng Huang2

1Department of Mathematics, University of Florida, Gainesville, FL, USA; 2Invivo Corporation, Gainesville, FL, USA

Segmented GRAPPA is superior to GRAPPA but requires significant amount of ACS lines. We propose a total variation regularized GRAPPA technique to produce a full calibration k-space with limited ACS lines. In the next step, the full calibration k-space data is used as calibration signal for segmented GRAPPA. The experimental results, with comparisons with GRAPPA and high-pass GRAPPA, show that the proposed method can generate images with lower artifacts/noises level when only 32 ACS lines are used with reduction factor 4. This work enables segmented GRAPPA with limited ACS lines, and hence increases the net acceleration factor while preserving the image quality.