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Abstract #0050

Self-adapting Multi-peak Water-fat Separation for Removing Lipid Artifacts in Breast Chemical Exchange Saturation Transfer (CEST) Imaging

Yu Zhao1 and Jianqi Li1

1Shanghai Key Laboratory of Magnetic Resonance, Shanghai, China

Chemical exchange saturation transfer (CEST) MRI show potential for breast lesion characterization. However, artifacts caused by strong lipid signals hinder its widespread application. To remove the artifacts, water-fat separation based on multipoint Dixon acquisition is used to obtain water-only images. Considering that RF pulses with various frequency offset in CEST preparation saturate each fat peak at different level, relative amplitudes of fat peaks are updated for building fat signal model by the numerical simulation. Based on this self-adapting multi-peak model (SMPM), a method combining nonlinear least-squares fitting and R2 *-IDEAL is used to perform the water-fat reconstruction. Phantom and in vivo breast experiments demonstrate that the proposed method successfully removes lipid artifacts.

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