Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enha...

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Autores principales: Murtha, Nathan, Mason, Allister, Bowen, Chris, Clarke, Sharon, Rioux, James, Beyea, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Grapho Publications, LLC 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744192/
https://www.ncbi.nlm.nih.gov/pubmed/33364426
http://dx.doi.org/10.18383/j.tom.2020.00045
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author Murtha, Nathan
Mason, Allister
Bowen, Chris
Clarke, Sharon
Rioux, James
Beyea, Steven
author_facet Murtha, Nathan
Mason, Allister
Bowen, Chris
Clarke, Sharon
Rioux, James
Beyea, Steven
author_sort Murtha, Nathan
collection PubMed
description We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.
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spelling pubmed-77441922020-12-23 Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study Murtha, Nathan Mason, Allister Bowen, Chris Clarke, Sharon Rioux, James Beyea, Steven Tomography Research Article We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications. Grapho Publications, LLC 2020-12 /pmc/articles/PMC7744192/ /pubmed/33364426 http://dx.doi.org/10.18383/j.tom.2020.00045 Text en © 2020 The Authors. Published by Grapho Publications, LLC http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Murtha, Nathan
Mason, Allister
Bowen, Chris
Clarke, Sharon
Rioux, James
Beyea, Steven
Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title_full Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title_fullStr Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title_full_unstemmed Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title_short Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study
title_sort evaluation of golden-angle-sampled dynamic contrast-enhanced mri reconstruction using objective image quality measures: a simulated phantom study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744192/
https://www.ncbi.nlm.nih.gov/pubmed/33364426
http://dx.doi.org/10.18383/j.tom.2020.00045
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