Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis
<b>Background</b>: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. <b>Objective</b>: In th...
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MDPI AG
2022-01-01
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| Online Access: | https://www.mdpi.com/1099-4300/24/2/155 |
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| author | Zahra Amini Farsani Volker J. Schmid |
| author_facet | Zahra Amini Farsani Volker J. Schmid |
| author_sort | Zahra Amini Farsani |
| collection | DOAJ |
| description | <b>Background</b>: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. <b>Objective</b>: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. <b>Materials and Methods</b>: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. <b>Results and Conclusions</b>: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters. |
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| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T22:02:14Z |
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| spelling | doaj.art-0fb6f82be18e4dd2a3ed4abaa738daa02023-11-23T19:46:53ZengMDPI AGEntropy1099-43002022-01-0124215510.3390/e24020155Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data AnalysisZahra Amini Farsani0Volker J. Schmid1Statistics Department, School of Science, Lorestan University, Khorramabad 68151-44316, IranBayesian Imaging and Spatial Statistics Group, Institute of Statistics, Ludwig-Maximilians-Universität München, Ludwigstraße 33, 80539 Munich, Germany<b>Background</b>: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. <b>Objective</b>: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. <b>Materials and Methods</b>: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. <b>Results and Conclusions</b>: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.https://www.mdpi.com/1099-4300/24/2/155kinetic modelmodified maximum entropy methodarterial input functionoptimization method |
| spellingShingle | Zahra Amini Farsani Volker J. Schmid Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis Entropy kinetic model modified maximum entropy method arterial input function optimization method |
| title | Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis |
| title_full | Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis |
| title_fullStr | Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis |
| title_full_unstemmed | Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis |
| title_short | Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis |
| title_sort | modified maximum entropy method and estimating the aif via dce mri data analysis |
| topic | kinetic model modified maximum entropy method arterial input function optimization method |
| url | https://www.mdpi.com/1099-4300/24/2/155 |
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