Meeting Banner
Abstract #0449

Navigator-free EPI ghost correction using low-rank matrix modeling: Theoretical insights and practical improvements

Rodrigo A. Lobos1, Tae Hyung Kim1, W. Scott Hoge2,3, 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

While the formation of ghost-free images from EPI data can be a difficult problem, recent low-rank matrix modeling methods have demonstrated promising results. In this abstract, we provide new theoretical insight into these approaches, and show that the low-rank ghost correction optimization problem has infinitely many solutions without using additional constraints. However, we also show that SENSE-like or GRAPPA-like constraints can be successfully used to make the problem well-posed, even for single-channel data. Additionally, we show that substantial performance gains can be achieved over previous low-rank ghost correction implementations by using nonconvex low-rank regularization instead of previous convex approaches.

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

Join Here