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Abstract #3505

Improving Parallel Imaging by Jointly Reconstructing Multi-Contrast Data

Berkin Bilgic1, Tae Hyung Kim2, Congyu Liao1, Mary Kate Manhard1, Lawrence L Wald1, Justin P Haldar2, and Kawin Setsompop1

1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States

We propose a general joint reconstruction framework to accelerate multi-contrast acquisitions further than currently possible with conventional parallel imaging. Our joint parallel imaging techniques simultaneously exploit similarities between echoes/phase-cycles/contrasts, virtual coil concept, partial Fourier acquisition, complementary sampling across images along with limited support and smooth phase constraints. These permit highly accelerated 2D, Simultaneous MultiSlice and 3D acquisitions as well as improved calibrationless parallel imaging from multiple contrasts. Our algorithms, JVC-GRAPPA and J-LORAKS, provide over 2-fold improvement in reconstruction error compared to conventional GRAPPA, with improved mitigation of artifacts and noise amplification.

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