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Abstract #0833

B-spline Parameterized Joint Optimization of Reconstruction and K-space Sampling Patterns (BJORK) for Accelerated 2D Acquisition

Guanhua Wang1, Tianrui Luo1, Jon-Fredrik Nielsen1, Jeffrey A. Fessler2, and Douglas C. Noll1
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, 2Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States

The proposed approach, BJORK, provides a robust and generalizable workflow to jointly optimize non-Cartesian sampling patterns and a physics-informed reconstruction. Several approaches, including re-parameterization of trajectories, multi-level optimization, and non-Cartesian unrolled neural networks, are introduced to improve training effect and avoid sub-optimal local minima. The in-vivo experiments show that the networks and trajectories learned on simulation dataset are transferable to the real acquisition even with different parameter-weighted MRI contrasts and noise-levels, and demonstrate improved image quality compared with previous learning-based and model-based trajectory optimization methods.

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