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Myocardial T2* mapping: influence of noise on accuracy and precision
BACKGROUND: Pixel-wise, parametric T2* mapping is emerging as a means of automatic measurement of iron content in tissues. It enables quick, intuitive interpretation and provides the potential benefit of spatial context between tissues. However, pixel-wise mapping uses much lower SNR data to estimat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4316604/ https://www.ncbi.nlm.nih.gov/pubmed/25648167 http://dx.doi.org/10.1186/s12968-015-0115-3 |
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author | Sandino, Christopher M Kellman, Peter Arai, Andrew E Hansen, Michael S Xue, Hui |
author_facet | Sandino, Christopher M Kellman, Peter Arai, Andrew E Hansen, Michael S Xue, Hui |
author_sort | Sandino, Christopher M |
collection | PubMed |
description | BACKGROUND: Pixel-wise, parametric T2* mapping is emerging as a means of automatic measurement of iron content in tissues. It enables quick, intuitive interpretation and provides the potential benefit of spatial context between tissues. However, pixel-wise mapping uses much lower SNR data to estimate T2* when compared to region-based mapping thereby decreasing both its accuracy and precision. In this study, the effects that noise has on the precision and accuracy of pixel-wise T2* mapping were investigated and techniques to mitigate those effects are proposed. METHODS: To study precision across T2* mapping techniques, a pipeline to estimate the pixel-wise standard deviation (SD) of the T2* based on the fit residuals is proposed. For validation, a Monte-Carlo analysis was performed in which T2* phantoms were scanned N = 64 times, the true SD was measured and compared to the estimated SD. To improve accuracy and precision, the automatic truncation method for mitigating noise bias was extended to pixel-wise fitting by using an SNR scaled image reconstruction and truncating low SNR measurements. Finally, the precision and accuracy of non-linear regression with and without automatic truncation, were investigated using Monte-Carlo simulations. RESULTS: Measured and estimated SD’s were >99.9% correlated for non-linear regression with and without truncation. Non-linear regression with automatic truncation was shown to be the best mapping technique for improving accuracy and precision in low T2* and low SNR measurements. CONCLUSIONS: A method for applying an automatic truncation method to pixel-wise T2* mapping that reduces T2* overestimation due to noise bias was proposed. A formulation for estimating pixel-wise standard deviation (SD) maps for T2* that can serve as a quality map for interpreting images and for comparison of imaging protocols was also proposed and validated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12968-015-0115-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4316604 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43166042015-02-05 Myocardial T2* mapping: influence of noise on accuracy and precision Sandino, Christopher M Kellman, Peter Arai, Andrew E Hansen, Michael S Xue, Hui J Cardiovasc Magn Reson Research BACKGROUND: Pixel-wise, parametric T2* mapping is emerging as a means of automatic measurement of iron content in tissues. It enables quick, intuitive interpretation and provides the potential benefit of spatial context between tissues. However, pixel-wise mapping uses much lower SNR data to estimate T2* when compared to region-based mapping thereby decreasing both its accuracy and precision. In this study, the effects that noise has on the precision and accuracy of pixel-wise T2* mapping were investigated and techniques to mitigate those effects are proposed. METHODS: To study precision across T2* mapping techniques, a pipeline to estimate the pixel-wise standard deviation (SD) of the T2* based on the fit residuals is proposed. For validation, a Monte-Carlo analysis was performed in which T2* phantoms were scanned N = 64 times, the true SD was measured and compared to the estimated SD. To improve accuracy and precision, the automatic truncation method for mitigating noise bias was extended to pixel-wise fitting by using an SNR scaled image reconstruction and truncating low SNR measurements. Finally, the precision and accuracy of non-linear regression with and without automatic truncation, were investigated using Monte-Carlo simulations. RESULTS: Measured and estimated SD’s were >99.9% correlated for non-linear regression with and without truncation. Non-linear regression with automatic truncation was shown to be the best mapping technique for improving accuracy and precision in low T2* and low SNR measurements. CONCLUSIONS: A method for applying an automatic truncation method to pixel-wise T2* mapping that reduces T2* overestimation due to noise bias was proposed. A formulation for estimating pixel-wise standard deviation (SD) maps for T2* that can serve as a quality map for interpreting images and for comparison of imaging protocols was also proposed and validated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12968-015-0115-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-02-04 /pmc/articles/PMC4316604/ /pubmed/25648167 http://dx.doi.org/10.1186/s12968-015-0115-3 Text en © Sandino et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Sandino, Christopher M Kellman, Peter Arai, Andrew E Hansen, Michael S Xue, Hui Myocardial T2* mapping: influence of noise on accuracy and precision |
title | Myocardial T2* mapping: influence of noise on accuracy and precision |
title_full | Myocardial T2* mapping: influence of noise on accuracy and precision |
title_fullStr | Myocardial T2* mapping: influence of noise on accuracy and precision |
title_full_unstemmed | Myocardial T2* mapping: influence of noise on accuracy and precision |
title_short | Myocardial T2* mapping: influence of noise on accuracy and precision |
title_sort | myocardial t2* mapping: influence of noise on accuracy and precision |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4316604/ https://www.ncbi.nlm.nih.gov/pubmed/25648167 http://dx.doi.org/10.1186/s12968-015-0115-3 |
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