Abstract #2667

On the Impact of Regularization & Kernel Type on SHARP-Corrected GRE Phase Images

Ferdinand Schweser^1,2, Karsten Sommer^1,3, Marie Atterbury^1,4, Andreas Deistung¹, Berengar Wendel Lehr¹, Jrgen R. Reichenbach¹

¹Medical Physics Group, Dept. of Diagnostic & Interventional Radiology 1, Jena University Hospital, Jena, Germany; ²School of Medicine, Friedrich Schiller University of Jena, Jena, Germany; ³School of Physics & Astronomy, Friedrich Schiller University of Jena, Jena, Germany; ⁴Dept. of Physics, Brown University, Providence, RI, United States

In this study we investigated the impact of regularization and kernel type used with the SHARP method based on a numerical brain model. Furthermore, we present the smallest possible kernel, which allows overcoming one of the major pitfalls of SHARP, i.e. the missing values at the edges of the brain.