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Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images
Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306237/ https://www.ncbi.nlm.nih.gov/pubmed/34357123 http://dx.doi.org/10.3390/jpm11070656 |
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author | Chan, Si-Wa Hu, Wei-Hsuan Ouyang, Yen-Chieh Su, Hsien-Chi Lin, Chin-Yao Chang, Yung-Chieh Hsu, Chia-Chun Chen, Kuan-Wen Liu, Chia-Chen Chien, Sou-Hsin |
author_facet | Chan, Si-Wa Hu, Wei-Hsuan Ouyang, Yen-Chieh Su, Hsien-Chi Lin, Chin-Yao Chang, Yung-Chieh Hsu, Chia-Chun Chen, Kuan-Wen Liu, Chia-Chen Chien, Sou-Hsin |
author_sort | Chan, Si-Wa |
collection | PubMed |
description | Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types. |
format | Online Article Text |
id | pubmed-8306237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83062372021-07-25 Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images Chan, Si-Wa Hu, Wei-Hsuan Ouyang, Yen-Chieh Su, Hsien-Chi Lin, Chin-Yao Chang, Yung-Chieh Hsu, Chia-Chun Chen, Kuan-Wen Liu, Chia-Chen Chien, Sou-Hsin J Pers Med Article Breast magnetic resonance imaging (MRI) is currently a widely used clinical examination tool. Recently, MR diffusion-related technologies, such as intravoxel incoherent motion diffusion weighted imaging (IVIM-DWI), have been extensively studied by breast cancer researchers and gradually adopted in clinical practice. In this study, we explored automatic tumor detection by IVIM-DWI. We considered the acquired IVIM-DWI data as a hyperspectral image cube and used a well-known hyperspectral subpixel target detection technique: constrained energy minimization (CEM). Two extended CEM methods—kernel CEM (K-CEM) and iterative CEM (I-CEM)—were employed to detect breast tumors. The K-means and fuzzy C-means clustering algorithms were also evaluated. The quantitative measurement results were compared to dynamic contrast-enhanced T1-MR imaging as ground truth. All four methods were successful in detecting tumors for all the patients studied. The clustering methods were found to be faster, but the CEM methods demonstrated better performance according to both the Dice and Jaccard metrics. These unsupervised tumor detection methods have the advantage of potentially eliminating operator variability. The quantitative results can be measured by using ADC, signal attenuation slope, D*, D, and PF parameters to classify tumors of mass, non-mass, cyst, and fibroadenoma types. MDPI 2021-07-13 /pmc/articles/PMC8306237/ /pubmed/34357123 http://dx.doi.org/10.3390/jpm11070656 Text en © 2021 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 Chan, Si-Wa Hu, Wei-Hsuan Ouyang, Yen-Chieh Su, Hsien-Chi Lin, Chin-Yao Chang, Yung-Chieh Hsu, Chia-Chun Chen, Kuan-Wen Liu, Chia-Chen Chien, Sou-Hsin Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title | Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title_full | Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title_fullStr | Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title_full_unstemmed | Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title_short | Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images |
title_sort | quantitative measurement of breast tumors using intravoxel incoherent motion (ivim) mr images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306237/ https://www.ncbi.nlm.nih.gov/pubmed/34357123 http://dx.doi.org/10.3390/jpm11070656 |
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