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Abstract #2599

Implicit-GRAPPA for Data-Efficient, real-time free-breathing cardiac imaging

Tianyun Zhao1, Mark Nishimura1, Daniel Abraham1, Nicole Seiberlich2, and Kawin Setsompop1,3
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, University of Michigan, Ann Arbor, MI, United States, 3Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Parallel Imaging, Image Reconstruction

Motivation: Non-Cartesian GRAPPA shows promise for dynamic imaging reconstruction but demands extensive calibration data and computation. A data-efficient implicit-GRAPPA approach could improve speed and usability without sacrificing image quality.

Goal(s): To achieve an efficient GRAPPA kernel with reduced calibration data requirements, matching or exceeding the quality of previous methods for real-time radial trajectory reconstruction.

Approach: Our method uses spatially structured source-target mappings to learn data-efficient GRAPPA weights per target point through a multilayer perceptron (MLP) model, learning implicit correlations across calibration trajectories.

Results: Our implicit-GRAPPA approach achieves high quality reconstruction with 31.73% lower RMSE than current state-of-art techniques while requiring ~15x less training data.

Impact: The proposed implicit-GRAPPA improves the image reconstruction quality and markedly reduces the calibration data requirement of non-Cartesian GRAPPA, and should prove useful for applications requiring robust real-time non-Cartesian reconstructions.

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Keywords