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Abstract #4245

Data Decoupling for Efficient Auto-Calibrating Parallel Imaging for Arbitrary Cartesian K-Space Sampling: Application to Highly-Accelerated 3D Cardiac Cine MRI

Peng Lai1, Anja C.S Brau1

1Global Applied Science Laboratory, GE Healthcare, Menlo Park, CA, United States

This work develops a novel source data decoupling method for computationally efficient auto-calibrating parallel imaging for arbitrary Cartesian sampling and validates its performance on 3D cardiac cine MRI with different acceleration factors and different coils. Our computation analysis shows that the proposed method can dramatically reduce calibration time compared to conventional autocalibrating parallel imaging methods, especially with large numbers of coil channels and synthesis patterns. Our invivo results on cardiac cine MRI also show the decoupling method preserves image quality of conventional kt GRAPPA and provides more accurate reconstruction and higher SNR than GRAPPA operator based methods due to its optimal combination of syntheses from different source data groups. The proposed data decoupling method is promising for online reconstruction for 3D cardiac cine MRI and other static 3D MRI applications.