Meeting Banner
Abstract #0103

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

Stephen Cauley1,2, Kawin Setsompop1,2, Berkin Bilgic1, Himanshu Bhat3, Borjan Gagoski2,4, Thomas Witzel1,2, and Lawrence L. Wald1,2,5

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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here