Correcting gradient chain induced fat quantification errors in radial multi-echo Dixon imaging using a gradient modulation transfer function

Purpose: Multi-echo Stack-of-stars (SoS) radial k-space trajectories with golden angle ordering are becoming popular for free-breathing abdominal Dixon imaging and proton density fat fraction (PDFF) mapping. Gradient chain imperfections including eddy currents and system delays are known to affect the image quality of radial imaging and to confound the estimation of PDFF mapping. This work proposes a retrospective trajectory correction method based on a simple gradient modulation transfer function (GMTF) measurement to predict and correct gradient chain induced k-space trajectory errors. Methods: The GMTF was measured using the standard hardware of a 3 Tesla scanner on a phantom using the thin slice method and was applied to a 3D radial SoS Dixon imaging sequence. The impact of the GMTF-based correction on image reconstruction and PDFF quantification was investigated using numerical simulations and validated on experimental phantom data as well as on in vivo leg and liver data of healthy volunteers. Results: Correcting the k-space trajectories with the measured GMTF during image reconstruction reduced PDFF quantification errors for phantom and in vivo acquisitions. A Bland-Altman comparison of the measured PDFF phantom and reference data confirmed that the GMTF correction narrowed down the limits of agreement (LoA) from 1.3% +/- 8.1% (uncorrected) to 1.9% +/- 5.4% (GMTF-corrected) over the full PDFF range (0%-100%) and from -0.26% +/- 2.8% (uncorrected) to 0.12% +/- 1.5% (GMTF-corrected) within the 0%-50% PDFF range. Liver PDFF estimation was improved by reducing the standard deviation of the mean liver PDFF and the bias of the mean liver PDFF for all subjects. Conclusion: The proposed GMTF-based k-space trajectory correction is a fast alternative method for avoiding PDFF quantitation errors caused by gradient-system induced k-space trajectory errors in 3D radial multi-echo gradient-echo acquisitions.