scholarly journals Fully automatic quantification of fibroglandular tissue and background parenchymal enhancement with accurate implementation for axial and sagittal breast MRI protocols

2020 ◽  
Author(s):  
Dong Wei ◽  
Nariman Jahani ◽  
Eric Cohen ◽  
Susan Weinstein ◽  
Meng‐Kang Hsieh ◽  
...  
Keyword(s):  
Breast Mri ◽  
Automatic Quantification ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
Fully Automatic ◽  
Parenchymal Enhancement

Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI

2012 ◽  
Vol 22 (12) ◽  
pp. 2641-2647 ◽  
Author(s):  
Valencia King ◽  
Yajia Gu ◽  
Jennifer B. Kaplan ◽  
Jennifer D. Brooks ◽  
Malcolm C. Pike ◽  
...  
Keyword(s):  
Menopausal Status ◽  
Breast Mri ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
Parenchymal Enhancement

scholarly journals Motion artifacts, lesion type, and parenchymal enhancement in breast MRI: what does really influence diagnostic accuracy?

2018 ◽  
Vol 60 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Paola Clauser ◽  
Matthias Dietzel ◽  
Michael Weber ◽  
Clemens G Kaiser ◽  
Pascal AT Baltzer
Keyword(s):  
Breast Mri ◽  
Lesion Size ◽  
Motion Artifacts ◽  
Lower Sensitivity ◽  
Fibroglandular Tissue ◽  
Factors Influencing ◽  
Background Parenchymal Enhancement ◽  
Lesion Type ◽  
Sensitivity Specificity ◽  
Parenchymal Enhancement

Background Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates. Purpose To evaluate factors that potentially influence readers’ diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type. Material and Methods This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy. Results Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P < 0.001), and small lesions ≤ 10 mm ( P < 0.021). No further factors (e.g. BPE, FGT) significantly influenced diagnostic estimates. At multivariate analysis, lesion type and size were retained as independent factors influencing the diagnostic performance ( P < 0.033). Conclusion Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

The Impact of Bilateral Salpingo-Oophorectomy on Breast MRI Background Parenchymal Enhancement and Fibroglandular Tissue

2013 ◽  
Vol 24 (1) ◽  
pp. 162-168 ◽  
Author(s):  
E. R. Price ◽  
J. D. Brooks ◽  
E. J. Watson ◽  
S. B. Brennan ◽  
E. A. Comen ◽  
...  
Keyword(s):  
Breast Mri ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
The Impact ◽  
Parenchymal Enhancement

scholarly journals Comparison of Radiologist-Assigned Categories and Quantitative Measures of Background Parenchymal Enhancement on Breast MRI

2020 ◽  
Author(s):  
Bethany L Niell ◽  
Mahmoud Abdalah ◽  
Olya Stringfield ◽  
Malesa Pereira ◽  
Dana Ataya ◽  
...  
Keyword(s):  
Active Contours ◽  
Breast Mri ◽  
Automated Segmentation ◽  
Post Contrast ◽  
Fibroglandular Tissue ◽  
Background Parenchymal Enhancement ◽  
Otsu’S Method ◽  
Risk Patients ◽  
Student’S T ◽  
Parenchymal Enhancement

AbstractBackground parenchymal enhancement (BPE) on multi-parametric breast magnetic resonance imaging (mpMRI) reflects the volume and intensity of gadolinium uptake. We developed a semi-automated segmentation algorithm to extract and quantify measures of BPE and investigated the agreement of computed measures of BPE with radiologist-assigned categories. We retrospectively identified 123 high risk patients with breast mpMRI performed for screening indications. Pre- and post-gadolinium T1-weighted series with and without fat suppression were co-registered. Using Otsu’s method and an active contours method, the breast tissue was segmented from the chest wall and non-fat voxels were clustered to identify the amount of fibroglandular tissue. Median and inter-quartile ranges for the absolute volume of BPE (BPE cm3) and the percentage of BPE (BPE%) using a threshold PE of 30% were computed within the segmented FGT on the first post-contrast phase. Student’s t-test was used to evaluate BPE volume and BPE% by radiologist-assigned categories. Both BPE volume and BPE% differed significantly between minimal/mild and moderate/marked radiologist assigned categories (p=0.030 and 0.004, respectively). Using our newly developed semi-automated segmentation pipeline, we quantified fibroglandular tissue and BPE. The overlapping ranges of quantitative BPE corresponding to the radiologist-assigned BPE categories showcases the inter-reader variability with visual estimation of BPE.

scholarly journals Fully automatic classification of breast MRI background parenchymal enhancement using a transfer learning approach

Medicine ◽  
2020 ◽  
Vol 99 (29) ◽  
pp. e21243
Author(s):  
Karol Borkowski ◽  
Cristina Rossi ◽  
Alexander Ciritsis ◽  
Magda Marcon ◽  
Patryk Hejduk ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Breast Mri ◽  
Automatic Classification ◽  
Learning Approach ◽  
Background Parenchymal Enhancement ◽  
Fully Automatic ◽  
Parenchymal Enhancement
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