Self‐adapting multi‐peak water‐fat reconstruction for the removal of lipid artifacts in chemical exchange saturation transfer (CEST) imaging

Abstract

Artifacts caused by strong lipid signals pose challenges in body chemical exchange saturation transfer (CEST) imaging. This study aimed to develop an accurate water-fat reconstruction method based on the multi-echo Dixon technique to remove lipid artifacts in CEST imaging. It is well known that fat has multiple spectral peaks. Furthermore, RF pulses in CEST preparation saturate each fat peak at different levels, complicating fat modeling. Therefore, a self-adapting multi-peak model (SMPM) is proposed to update relative amplitudes of fat peaks using numerical calculation. With the SMPM-based updating, nonlinear least-squares fitting combined with IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation) algorithms was used for water-fat reconstruction and B0 mapping. The proposed method was compared with the reported 3-point Dixon method and the fixed multi-peak model in a phantom study using a fat-free Z-spectrum obtained from MR spectroscopy acquisition as the ground truth. This method was also validated by in vivo experiments on human breast. In the phantom experiments, the Z-spectrum from the SMPM-based method agreed well with the fat-free Z-spectrum from CEST-PRESS (point-resolved spectroscopy), validating the effective removal of lipid artifacts, while a decrease or a rise that appeared at -3.5 ppm was observed in the Z-spectrum from the 3-point method and the FMPM-based method, respectively. In the in vivo experiments, no lipid artifacts were observed in the Z-spectrum or the amide CEST map from the SMPM-based method in the fibro-glandular region of the breast with high fat fractions. The SMPM-based method successfully removes lipid artifacts and significantly improves the accuracy of CEST contrast.