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Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach
Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. C...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7311534/ https://www.ncbi.nlm.nih.gov/pubmed/32576843 http://dx.doi.org/10.1038/s41598-020-66985-9 |
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author | Ghodasara, Satyam Chen, Yong Pahwa, Shivani Griswold, Mark A. Seiberlich, Nicole Wright, Katherine L. Gulani, Vikas |
author_facet | Ghodasara, Satyam Chen, Yong Pahwa, Shivani Griswold, Mark A. Seiberlich, Nicole Wright, Katherine L. Gulani, Vikas |
author_sort | Ghodasara, Satyam |
collection | PubMed |
description | Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo DCE-MRI datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4–1.0% for arterial fraction, 0.5–1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1–2.1% for arterial fraction, 0.5–1.3% for distribution volume, and 0.2–1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in DCE-MRI. |
format | Online Article Text |
id | pubmed-7311534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73115342020-06-25 Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach Ghodasara, Satyam Chen, Yong Pahwa, Shivani Griswold, Mark A. Seiberlich, Nicole Wright, Katherine L. Gulani, Vikas Sci Rep Article Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo DCE-MRI datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4–1.0% for arterial fraction, 0.5–1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1–2.1% for arterial fraction, 0.5–1.3% for distribution volume, and 0.2–1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in DCE-MRI. Nature Publishing Group UK 2020-06-23 /pmc/articles/PMC7311534/ /pubmed/32576843 http://dx.doi.org/10.1038/s41598-020-66985-9 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Ghodasara, Satyam Chen, Yong Pahwa, Shivani Griswold, Mark A. Seiberlich, Nicole Wright, Katherine L. Gulani, Vikas Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title | Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title_full | Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title_fullStr | Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title_full_unstemmed | Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title_short | Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach |
title_sort | quantifying perfusion properties with dce-mri using a dictionary matching approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7311534/ https://www.ncbi.nlm.nih.gov/pubmed/32576843 http://dx.doi.org/10.1038/s41598-020-66985-9 |
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