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Cellular Lipid Extraction for Targeted Stable Isotope Dilution Liquid Chromatography-Mass Spectrometry Analysis
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Maximum-likelihood estimation for indicator dilution analysis.

Maarten P J Kuenen, Ingeborg H F Herold, Hendrikus H M Korsten

    IEEE Transactions on Bio-Medical Engineering
    |November 19, 2013
    PubMed
    Summary
    This summary is machine-generated.

    A new maximum-likelihood algorithm improves parameter estimation accuracy for indicator dilution curves. This method enhances medical imaging analysis and shows promise for detecting prostate cancer.

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    Area of Science:

    • Medical Imaging
    • Biophysics
    • Computational Biology

    Background:

    • Indicator-dilution methods are crucial for estimating hemodynamic parameters in various medical imaging techniques.
    • Current methods often rely on fitting mathematical models to indicator dilution curves, which can limit accuracy.
    • Accurate parameter estimation is vital for effective diagnosis and treatment monitoring.

    Purpose of the Study:

    • To introduce a novel maximum-likelihood algorithm for parameter estimation from indicator dilution curves.
    • To enhance the accuracy of hemodynamic parameter estimation compared to existing methods.
    • To evaluate the algorithm's efficacy in differentiating between healthy and cancerous tissues using dynamic contrast-enhanced ultrasound imaging.

    Main Methods:

    • Developed a new maximum-likelihood algorithm treating indicator dilution curves as transit-time distribution histograms.
    • Provided semianalytical solutions for three established indicator dilution models.
    • Adapted the algorithm to handle indicator recirculation, a common challenge in physiological measurements.
    • Validated the algorithm using simulations and experimental data from dynamic contrast-enhanced ultrasound imaging.

    Main Results:

    • The proposed maximum-likelihood algorithm demonstrated superior parameter estimation accuracy over nonlinear least-squares regression in both simulations and experimental data.
    • The algorithm's adaptation for indicator recirculation proved effective.
    • In prostate cancer detection using dynamic contrast-enhanced ultrasound, the algorithm showed an improved ability to differentiate between healthy and cancerous tissues, with an increase in receiver-operating characteristic curve area up to 0.13.

    Conclusions:

    • The new maximum-likelihood algorithm offers a significant advancement in parameter estimation for indicator dilution methods.
    • The algorithm's enhanced accuracy and ability to handle recirculation make it a valuable tool for medical imaging analysis.
    • The demonstrated improvement in prostate cancer detection highlights the clinical potential of this novel approach for in vivo applications.