Zhong Xiaodong, Nickel Marcel D, Kannengiesser Stephan A R, Dale Brian M, Kiefer Berthold, Bashir Mustafa R
MR R&D Collaborations, Siemens Healthcare, Atlanta, Georgia, USA.
Magn Reson Med. 2014 Nov;72(5):1353-65. doi: 10.1002/mrm.25054. Epub 2013 Dec 9.
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.
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.
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.
本研究的目的是开发一种用于肝脏质子密度脂肪分数（PDFF）和R(2)*定量的多步自适应拟合方法，并在广泛可用的硬件平台上进行初步验证。
所提出的方法采用多回波三维梯度回波采集，基于对两个选定回波的狄克逊分解对脂肪和水信号分数进行初始估计。然后基于具有多峰脂肪光谱模型的幅度信号方程，执行多步非线性拟合程序以自适应更新脂肪和水信号分数以及R(2)*值。所提出的方法使用数值体模作为真值进行验证，随后对30例患者的PDFF计算与光谱法进行初步临床验证。
所提出方法的结果与数值体模的真值吻合良好，并且对场强、场均匀性、单极/双极读出、信噪比和回波时间选择的变化相对不敏感。体内患者研究表明，与光谱法相比，所提出方法测量的PDFF值具有极好的一致性。
这种多步自适应拟合方法在模拟和初步临床评估中均表现良好，并且在肝脂肪变性定量方面显示出潜力。
