Abstract #3533

Robust Autocalibrated LORAKS for Improved EPI Ghost Correction with Structured Low-Rank Matrix Models

Rodrigo A Lobos¹, Ahsan Javed¹, Krishna S Nayak¹, W Scott Hoge^2,3, and Justin P Haldar¹

¹Electrical Engineering, University of Southern California, Los Angeles, CA, United States, ²Radiology, Brigham and Women's Hospital, Boston, MA, United States, ³Radiology, Harvard Medical School, Boston, MA, United States

The presence of ghost artifacts is a recurrent problem in EPI images, which has been recently addressed using structured low-rank matrix (SLM) methods. In this work we propose a new SLM ghost correction method called Robust Autocalibrated LORAKS (RAC-LORAKS). RAC-LORAKS considers autocalibrated k-space constraints (similar to GRAPPA) to deal with the ill-posedness of existing SLM EPI ghost correction methods. RAC-LORAKS additionally adapts these constraints to enable robustness to possible imperfections in the autocalibration data. We illustrate the capabilities of RAC-LORAKS in two challenging scenarios: highly accelerated EPI of the brain, and cardiac EPI with double-oblique slice orientation.

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