Mario Zeller1, Alexander Mller1, Marcel Gutberlet2, Andreas J. Bartsch3, 4, Daniel Stb1, Dietbert Hahn1, Herbert Kstler1
1Institute of Radiology, University of Wrzburg, Wrzburg, Germany; 2Institute for Interventional and Diagnostic Radiology, Hannover Medical School, Hannover, Germany; 3Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany; 4FMRIB Centre, Oxford University, Oxford, United Kingdom
In Cartesian imaging, optimal SNR can be achieved by filtering the k-space proportional to the signal (SNR matched filter). This however leads to Gibbs artifact amplification. In contrast, Gibbs artifacts are reduced by filters that apodize the k-space periphery, leading to non-optimal SNR. K-space density weighting allows combining both approaches. The application of an SNR matched filter ensures optimal SNR, while a non-Cartesian k-space sampling allows achieving a prospectively defined point spread function. In this work, k-space density weighting was applied to echo planar imaging. The results indicate significant SNR advantages of density weighting over Cartesian imaging with retrospective filtering.