Comparison of breast density measured on MR images acquired using fat‐suppressed versus nonfat‐suppressed sequences

Chang, Daniel; Chen, Jeon‐Hor; Lin, Muqing; Bahri, Shadfar; Yu, Hua; Mehta, Rita S.; Nie, Ke; Hsiang, David; Nalcioğlu, Orhan; Su, Min‐Ying

doi:10.1118/1.3646756

Cited by 30 publications

(51 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study also showed (78) that the MGD from FFDM and DBT for a single view increased with increasing breast thickness and was used to construct the plots for 50% and 14.3% fibroglandular breasts in Figure 2a. A 50% fibroglandular breast corresponds to the assumed average breast composition used in United States protocol and a 14.3% fibroglandular breast corresponds to approximately average realistic composition reported in recent studies (79)(80)(81)(82). It is relevant to note that the estimated MGD is with automatic exposure control, and for the desired image quality, the technique factors and consequently the MGD can be chosen.…”

Section: Radiation Dosementioning

confidence: 99%

Digital Breast Tomosynthesis: State of the Art

et al. 2015

View full text Add to dashboard Cite

This topical review on digital breast tomosynthesis (DBT) is provided with the intent of describing the state of the art in terms of technology, results from recent clinical studies, advanced applications, and ongoing efforts to develop multimodality imaging systems that include DBT. Particular emphasis is placed on clinical studies. The observations of increase in cancer detection rates, particularly for invasive cancers, and the reduction in false-positive rates with DBT in prospective trials indicate its benefit for breast cancer screening. Retrospective multireader multicase studies show either noninferiority or superiority of DBT compared with mammography. Methods to curtail radiation dose are of importance

show abstract

Section: Radiation Dosementioning

confidence: 99%

Digital Breast Tomosynthesis: State of the Art

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In the next step, a horizontal truncation line is delineated through the point placed 15 mm below point n , which places it on the anterior boundary of the sternum in the edge map image (Figure 4b). Based on Change's work [32] and the advice of two experienced radiologists, a separation threshold of 15 mm is selected.…”

Section: Detection Of Approximated Breast Boundarymentioning

confidence: 99%

Breast-region segmentation in MRI using chest region atlas and SVM

Fooladivanda¹,

Shokouhi²,

Ahmadinejad³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

An important step for computerized analysis of breast magnetic resonance imaging (MRI) is segmentation of the breast region. Due to the similar signal intensity of fibroglandular tissue and the chest wall, the segmentation process is difficult for breasts with fibroglandular tissue connected to the chest wall. In order to overcome this challenge, a new framework is presented that relies on a chest region atlas. The proposed method first detects the approximated breast-chest wall boundary using an intensity-based operation. A support vector machine (SVM) then determines the connectivity of fibroglandular tissue to the chest wall by the extracted features from the obtained breast-chest wall boundary. Finally, the obtained breast-chest wall boundary is accurately refined using the geometric shape of the chest region, which is obtained by an atlas-based segmentation method. The proposed method is validated using a dataset of 5964 breast MRI images from 126 women. The Dice similarity coefficient (DSC), total overlap (TO), false negative (FN), and false positive (FP) values are calculated to measure the similarity between automatic and manual segmentation results. Our method achieves DSC, TO, FN, and FP values of 96.46%, 96.41%, 3.59%, and 3.51%, respectively. The results prove the effectiveness of the presented algorithm for breasts with different sizes, shapes, and density patterns.

show abstract

“…10 Kopans 11 advocated for the use of threedimensional (3D) imaging for estimating the amount of dense tissue present in the breast. Techniques for quantitative estimation of fibroglandular tissue volume, and consequently the ratio of fibroglandular tissue volume to the total breast volume, often referred to as "volumetric glandular fraction" (VGF) using 3D imaging techniques such as breast MRI, 12,13 dedicated breast CT, 14,15 and ultrasound tomography 16 have been developed.…”

Section: Introductionmentioning

confidence: 99%

Dedicated breast CT: Fibroglandular volume measurements in a diagnostic population

et al. 2012

View full text Add to dashboard Cite

Purpose: To determine the mean and range of volumetric glandular fraction (VGF) of the breast in a diagnostic population using a high-resolution flat-panel cone-beam dedicated breast CT system. This information is important for Monte Carlo-based estimation of normalized glandular dose coefficients and for investigating the dependence of VGF on breast dimensions, race, and pathology. Methods: Image data from a clinical trial investigating the role of dedicated breast CT that enrolled 150 women were retrospectively analyzed to determine the VGF. The study was conducted in adherence to a protocol approved by the institutional human subjects review boards and written informed consent was obtained from all study participants. All participants in the study were assigned BI-RADS R 4 or 5 as per the American College of Radiology assessment categories after standard diagnostic work-up and underwent dedicated breast CT exam prior to biopsy. A Gaussian-kernel based fuzzy c-means algorithm was used to partition the breast CT images into adipose and fibroglandular tissue after segmenting the skin. Upon determination of the accuracy of the algorithm with a phantom, it was applied to 137 breast CT volumes from 136 women. VGF was determined for each breast and the mean and range were determined. Pathology results with classification as benign, malignant, and hyperplasia were available for 132 women, and were used to investigate if the distributions of VGF varied with pathology. Results: The algorithm was accurate to within ±1.9% in determining the volume of an irregular shaped phantom. The study mean (± inter-breast SD) for the VGF was 0.172 ± 0.142 (range: 0.012-0.719). VGF was found to be negatively correlated with age, breast dimensions (chest-wall to nipple length, pectoralis to nipple length, and effective diameter at chest-wall), and total breast volume, and positively correlated with fibroglandular volume. Based on pathology, pairwise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there was no significant difference in distributions of VGF without adjustment for age between malignant and nonmalignant breasts (p = 0.41). Pairwise comparisons of the distributions of VGF in increasing order of mammographic breast density indicated all comparisons were statistically significant (p < 0.002). Conclusions: This study used a different clinical prototype breast CT system than that in previous studies to image subjects from a different geographical region, and used a different algorithm for analysis of image data. The mean VGF estimated from this study is within the range reported in previous studies, indicating that the choice of 50% glandular weight fraction to represent an average breast for Monte Carlo-based estimation of normalized glandular dose coefficients in mammography needs revising. In the study, the distributions of VGF did not differ significantly with pathology.

show abstract

Comparison of breast density measured on MR images acquired using fat‐suppressed versus nonfat‐suppressed sequences

Cited by 30 publications

References 25 publications

Digital Breast Tomosynthesis: State of the Art

Digital Breast Tomosynthesis: State of the Art

Breast-region segmentation in MRI using chest region atlas and SVM

Dedicated breast CT: Fibroglandular volume measurements in a diagnostic population

Contact Info

Product

Resources

About