Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images

被引:10
作者
Chan, Si-Wa [1 ,2 ,3 ]
Hu, Wei-Hsuan [4 ]
Ouyang, Yen-Chieh [4 ]
Su, Hsien-Chi [4 ]
Lin, Chin-Yao [2 ,5 ,6 ]
Chang, Yung-Chieh [7 ]
Hsu, Chia-Chun [1 ,2 ]
Chen, Kuan-Wen [2 ,8 ]
Liu, Chia-Chen [1 ,2 ]
Chien, Sou-Hsin [2 ,9 ]
机构
[1] Taichung Tzu Chi Hosp, Dept Med Imaging, Buddhist Tzu Chi Med Fdn, Taichung 427, Taiwan
[2] Tzu Chi Univ, Sch Med, Hualien 970, Taiwan
[3] Cent Taiwan Univ Sci & Technol, Dept Med Imaging Radiol Sci, Taichung 427, Taiwan
[4] Natl Chung Hsing Univ, Dept Elect Engn, Taichung 402, Taiwan
[5] Taichung Tzu Chi Hosp, Buddhist Tzu Chi Med Fdn, Dept Surg, Taichung 407, Taiwan
[6] Sch Med, Taichung 407, Taiwan
[7] Taichung Vet Gen Hosp, Dept Radiol, Taichung 407, Taiwan
[8] Taichung Tzu Chi Hosp, Dept Radiat Oncol, Buddhist Tzu Chi Med Fdn, Taichung 427, Taiwan
[9] Taichung Tzu Chi Hosp, Div Plast Surg, Buddhist Tzu Chi Med Fdn, Taichung 427, Taiwan
关键词
magnetic resonance imaging; intra-voxel incoherent motion; hyperspectral image cube; iterative-constrained energy minimization; kernel-constrained energy minimization; K-means; fuzzy C-means; DIFFUSION; CANCER; PERFUSION;
D O I
10.3390/jpm11070656
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
100404 [儿少卫生与妇幼保健学];
摘要
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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页数:20
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