Cargando…

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Background: 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. Objective: In the current study, we estimate the AIF r...

Descripción completa

Detalles Bibliográficos
Autores principales: Amini Farsani, Zahra, Schmid, Volker J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871336/
https://www.ncbi.nlm.nih.gov/pubmed/35205451
http://dx.doi.org/10.3390/e24020155
_version_ 1784656972450103296
author Amini Farsani, Zahra
Schmid, Volker J.
author_facet Amini Farsani, Zahra
Schmid, Volker J.
author_sort Amini Farsani, Zahra
collection PubMed
description Background: 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. Objective: 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. Materials and Methods: 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. Results and Conclusions: 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.
format Online
Article
Text
id pubmed-8871336
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-88713362022-02-25 Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis Amini Farsani, Zahra Schmid, Volker J. Entropy (Basel) Article Background: 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. Objective: 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. Materials and Methods: 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. Results and Conclusions: 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. MDPI 2022-01-20 /pmc/articles/PMC8871336/ /pubmed/35205451 http://dx.doi.org/10.3390/e24020155 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Amini Farsani, Zahra
Schmid, Volker J.
Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis
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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871336/
https://www.ncbi.nlm.nih.gov/pubmed/35205451
http://dx.doi.org/10.3390/e24020155
work_keys_str_mv AT aminifarsanizahra modifiedmaximumentropymethodandestimatingtheaifviadcemridataanalysis
AT schmidvolkerj modifiedmaximumentropymethodandestimatingtheaifviadcemridataanalysis