Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging.

PURPOSE: The purpose of this study was to develop a multi-step adaptive fitting approach for liver proton density fat fraction (PDFF) and R(2)* quantification, and to perform an initial validation on a broadly available hardware platform. THEORY AND METHODS: The proposed method uses a multi-echo three-dimensional gradient echo acquisition, with initial guesses for the fat and water signal fractions based on a Dixon decomposition of two selected echoes. Based on magnitude signal equations with a multi-peak fat spectral model, a multi-step nonlinear fitting procedure is then performed to adaptively update the fat and water signal fractions and R(2)* values. The proposed method was validated using numeric phantoms as ground truth, followed by preliminary clinical validation of PDFF calculations against spectroscopy in 30 patients.
RESULTS: The results of the proposed method agreed well with the ground truth of numerical phantoms, and were relatively insensitive to changes in field strength, field homogeneity, monopolar/bipolar readout, signal to noise ratio, and echo time selections. The in vivo patient study showed excellent consistency between the PDFF values measured with the proposed approach compared with spectroscopy.
CONCLUSION: This multi-step adaptive fitting approach performed well in both simulated and initial clinical evaluation, and shows potential in the quantification of hepatic steatosis.