Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging
Article first published online: 9 DEC 2013
Copyright © 2013 Wiley Periodicals, Inc.
Magnetic Resonance in Medicine
Volume 72, Issue 5, pages 1353–1365, November 2014
How to Cite
Zhong, X., Nickel, M. D., Kannengiesser, S. A.R., Dale, B. M., Kiefer, B. and Bashir, M. R. (2014), Liver fat quantification using a multi-step adaptive fitting approach with multi-echo GRE imaging. Magn Reson Med, 72: 1353–1365. doi: 10.1002/mrm.25054
- Issue published online: 13 OCT 2014
- Article first published online: 9 DEC 2013
- Manuscript Revised: 30 OCT 2013
- Manuscript Accepted: 30 OCT 2013
- Manuscript Received: 9 JUN 2013
- fat quantification;
- iron quantification;
- water fat separation;
The purpose of this study was to develop a multi-step adaptive fitting approach for liver proton density fat fraction (PDFF) and 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 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.
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.
This multi-step adaptive fitting approach performed well in both simulated and initial clinical evaluation, and shows potential in the quantification of hepatic steatosis. Magn Reson Med 72:1353–1365, 2014. © 2013 Wiley Periodicals, Inc.