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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

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6 Author(s)
H. Ratiney ; CREATIS-LRMN, CNRS UMR 5220, Inserm U630, Université de Lyon, Université Lyon 1, INSA-Lyon, Villeurbanne, France ; A. Bucur ; M. Sdika ; O. Beuf
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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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2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

14-17 May 2008