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Analyzing Absolute Metabolite Concentrations in Brain Tissue Using Gaussian and Lorentzian Functions

This presentation discusses methods for obtaining absolute metabolite concentrations in brain tissue by utilizing Gaussian and Lorentzian functions. It covers the creation of a noise-free baseline using anchor points on the spectrum, the identification of metabolite peaks, and the application of mathematical models for peak integration. Both Gaussian and Lorentzian functions are compared in terms of their effectiveness, with results indicating that both models yield similar metabolite ratios. Attendees are encouraged to provide suggestions or feedback on the findings.

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Analyzing Absolute Metabolite Concentrations in Brain Tissue Using Gaussian and Lorentzian Functions

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  1. Absolute Metabolite Concentrations on Brain Tissue by Gaussian and Lorentzian Functions Amarjeet Bhullar Bimonthly Meeting on Dec. 5, 2008

  2. How to get absolute signal? Absolute Signal = Raw data - Noise Raw data = Real Spectrum without any manipulation Noise = Draw a Baseline using few anchor points on Spectrum Noise=Baseline is determined by interpolating anchor points on spectrums.

  3. Absolute Metabolite Concentrations • Create baseline using few anchor points on spectrum. • Find metabolite peaks. • Fit Mathematical function on metabolite peaks. • Integrate peaks between the limits to calculate absolute metabolite concentrations.

  4. Mathematical Model: Gaussian Function

  5. Integral of Gaussian Function : Error Function Error Function Numerically: Codes developed in C and Mathematica 6.0

  6. Integral of Gaussian Function : Gamma Function

  7. Mathematical Model: Lorentz Function

  8. Integral of Lorentzian Function : ArcTan

  9. Difference Between Lorentzian and Gaussian Function

  10. Metabolite ratios by Gaussian function

  11. Metabolite ratios by Lorentzian function

  12. Conclusion: Both mathematical models have produced the same ratios. Suggestions are welcome

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