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Abstract #4562

TV Regularization for High-Pass GRAPPA with Higher Net Acceleration Factor

Xiaojing Ye1, Yunmei Chen1, Feng Huang2

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

High-pass GRAPPA (hp-GRAPPA) suppresses the central calibration signal to reduce image support. When the number of ACS lines is limited, this suppression results in insufficient calibration signal which causes residual aliasing artifacts. We propose a total variation regularized GRAPPA technique to calculate supplemental calibration signal for hp-GRAPPA. The experimental results, with comparisons with conventional GRAPPA and hp-GRAPPA, show that the proposed method can generate images with lower noise/artifact level when only 32 ACS lines are used with reduction factor 4. This work enables hp-GRAPPA with limited ACS lines, and hence increases the net acceleration factor while preserving the image quality.