Abstract #0103

Joint K-space Trajectory and Parallel Imaging Optimization for Auto-calibrated Image Reconstruction

Stephen Cauley^1,2, Kawin Setsompop^1,2, Berkin Bilgic¹, Himanshu Bhat³, Borjan Gagoski^2,4, Thomas Witzel^1,2, and Lawrence L. Wald^1,2,5

¹MGH/HST, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Siemens Medical Solutions Inc, Malvern, PA, United States, ⁴Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, United States, ⁵Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA, United States

Fast MRI acquisitions often rely on efficient traversal of k-space, e.g. Spiral, EPI, and Wave-CAIPI. Limitations in hardware and other physical effects cause these trajectories to deviate from the theoretical path, and additional measurements are typically used to approximate discrepancies. We propose a joint optimization to directly estimate trajectory discrepancies simultaneously with the underlying image, without need for additional characterization measurements. Model reduction schemes are introduced to make this optimization computationally efficient and ensure final image quality. We demonstrate our approach for a clinically relevant Wave-CAIPI acquisition, where we accurately optimize across >6million unknowns in 30s on standard vendor hardware.

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