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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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