使用多个随机起始值和参数边界设置对体内肝脏1H磁共振光谱数据进行有效voigt模型估计

H. Ratiney, A. Bucur, M. Sdika, O. Beuf, F. Pilleul, S. Cavassila
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引用次数: 11

摘要

通过复杂Voigt函数建模的体内肝脏1H线形是减少系统误差和获得准确拟合的理想选择。然而,当峰值共振重叠且高斯阻尼与洛伦兹阻尼的比例先验未知时,优化过程变得具有挑战性。在这种情况下,通常在磁共振波谱定量中使用的非线性最小二乘算法对起始值和参数边界高度敏感。为了减轻这种敏感性,使用多个随机起始值和参数边界设置来生成候选解。然后从中选择满足成本函数和阻尼因子最终值要求的“最佳拟合”。蒙特卡罗研究和体内肝脏1H信号量化证明了所提出策略的相关性。
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Effective voigt model estimation using multiple random starting values and parameter bounds settings for in vivo hepatic 1H magnetic resonance spectroscopic data
In vivo hepatic 1H lineshapes modeled by the complex Voigt function are desirable to reduce systematic error and obtain accurate fits. However, the optimization procedure becomes challenging when the peak resonances overlap and the proportion of Gaussian to Lorentzian dampings is a priori unknown. In this context, nonlinear least-squares algorithms generally invoked in Magnetic Resonance Spectroscopy quantification are highly sensitive to the starting values and parameter bounds. To alleviate this sensitivity, multiple random starting values and parameter bounds settings are used to generate candidate solutions. The "best fit" fulfilling requirements on the cost function and damping factor final values is then selected among them. Monte Carlo studies and an in vivo hepatic 1H signal quantification demonstrated the relevance of the proposed strategy.
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