scholarly journals Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma

2013 ◽  
Vol 40 (4) ◽  
pp. 813-823 ◽  
Author(s):  
Louisa Bokacheva ◽  
Jennifer B. Kaplan ◽  
Dilip D. Giri ◽  
Sujata Patil ◽  
Merlin Gnanasigamani ◽  
...  
Keyword(s):  
Intravoxel Incoherent Motion ◽  
Breast Lesions ◽  
Diffusion Weighted Mri ◽  
Benign Lesions ◽  
Breast Parenchyma ◽  
Diffusion Weighted ◽  
Malignant Breast ◽  
3.0 T

scholarly journals The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ya-Nan Jin ◽  
Yan Zhang ◽  
Jing-Liang Cheng ◽  
Xiao-Pan Zhang ◽  
Ying Hu ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Diffusion Weighted Imaging ◽  
Region Of Interest ◽  
Maximum Level ◽  
Histogram Analysis ◽  
Breast Lesions ◽  
Benign Lesions ◽  
Diffusion Weighted ◽  
Malignant Breast

Abstract Background The present study aims to investigate the role of histogram analysis of intravoxel incoherent motion (IVIM) in the differential diagnosis of benign and malignant breast lesions. Methods The magnetic resonance imaging and clinical data of 55 patients (63 lesions) were retrospectively analyzed. The multi-b-valued diffusion-weighted imaging image was processed using the MADC software to obtain the gray-scaled maps of apparent diffusion coefficient (ADC)-slow, ADC-fast and f. The MaZda software was used to extract the histogram metrics of these maps. Combined with the conventional sequence images, the region of interest (ROI) was manually drawn along the edge of the lesion at the maximum level of the gray-scale image, and the difference of the data was analyzed between the benign and malignant breast lesions. Results There were 29 patients with 37 benign lesions, which included 23 fibroadenomas, 6 adenosis, 1 breast cysts, 4 intraductal papillomas, and 3 inflammations of breast. Furthermore, 26 malignant lesions in 26 patients, which included 20 non-specific invasive ductal carcinomas, 5 intraductal carcinomas and 1 patient with squamous cell carcinoma. The ADC-slow (mean and the 50th percentile) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile) of these malignant breast lesions were significantly lower than those of benign lesions (P < 0.05), while ADC-fast (kurtosis) and f (variance, skewness) of these malignant breast lesions were significantly higher than those of benign lesions (P < 0.05). Conclusion The histogram analysis of ADC-slow (mean and the 50th percentile), ADC-fast (kurtosis) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile. Variance, skewness) can provide a more objective and accurate basis for the differential diagnosis of benign and malignant breast lesions.

scholarly journals Differentiation between Benign and Malignant Breast Lesions Using ADC on Diffusion-Weighted Imaging at 3.0 T

2016 ◽  
Vol 06 (01) ◽  
pp. 1-9 ◽  
Author(s):  
Vikash Kr Gupta ◽  
Wanhua Liu ◽  
Rui Wang ◽  
Yuanyuan Ye ◽  
Jing Jiang
Keyword(s):  
Diffusion Weighted Imaging ◽  
Breast Lesions ◽  
Diffusion Weighted ◽  
Malignant Breast ◽  
3.0 T

scholarly journals Importance of Superb Microvascular Imaging in Differentiation of Malignant Breast Lesions From Benign Lesions

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Fatma Zeynep Arslan ◽  
Ayşegül Altunkeser ◽  
Kazım Körez ◽  
Nergis Aksoy ◽  
Zeynep Bayramoğlu ◽  
...  
Keyword(s):  
Breast Lesions ◽  
Benign Lesions ◽  
Malignant Breast ◽  
Superb Microvascular Imaging ◽  
Microvascular Imaging

scholarly journals Deep learning how to fit an intravoxel incoherent motion model to diffusion‐weighted MRI

2019 ◽  
Vol 83 (1) ◽  
pp. 312-321 ◽  
Author(s):  
Sebastiano Barbieri ◽  
Oliver J. Gurney‐Champion ◽  
Remy Klaassen ◽  
Harriet C. Thoeny
Keyword(s):  
Deep Learning ◽  
Intravoxel Incoherent Motion ◽  
Motion Model ◽  
Diffusion Weighted Mri ◽  
Diffusion Weighted
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