Dual sided automated ultrasound system in the mammographic geometry

Journal of Ultrasound in Medicine

Paramagul

et al. 2013

Self Cite

Objective To retrospectively evaluate the effect of 3D automated ultrasound (3D-AUS) as an adjunct to digital breast tomosynthesis (DBT) on radiologists’ performance and confidence in discriminating malignant and benign breast masses. Methods Two-view DBT (CC and MLO or Lateral) and single-view 3D-AUS were acquired on 51 patients with (subsequently) biopsy-proven masses (13 malignant, 38 benign). Six experienced radiologists rated, on a 13-point scale, the likelihood of malignancy of an identified mass, first by reading the DBT alone, followed immediately by reading the DBT with automatically co-registered 3D-AUS. The diagnostic performance of each method was measured using ROC analysis. and changes in sensitivity and specificity with the McNemar test. After each reading, radiologists took a survey to rate their confidence level in using DBT alone vs combined DBT/3D-AUS as potential screening modalities. Results The six radiologists had an average area under the ROC curve of 0.92 for both modalities (range 0.89–0.97 for DBT, 0.90–0.94 for DBT/3D-AUS). With BI-RADS rating of 4 as the threshold for biopsy recommendation, the average sensitivity of the radiologists increased from 96% to 100% (p>0.08) with 3D-AUS, while the specificity decreased from 33% to 25% (p>0.28). Survey responses indicated an increased confidence in potentially using DBT for screening when 3D-AUS was added (p<0.05 for each reader). Conclusions In this initial reader study, no significant difference in ROC performance was found with the addition of 3D-AUS to DBT. However, a trend to an improved discrimination of malignancy was observed when adding 3D-AUS. Radiologists’ confidence also improved with DBT/3DAUS compared to DBT alone.

Section: Discussionmentioning

confidence: 99%

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Padilla

Journal of Ultrasound in Medicine

Paramagul

et al. 2013

Self Cite

“…Larger breasts take two, and occasionally three, sweeps for a full coverage of the breast past the nipple. The acquired volumes are spliced into a single 3D volume for interpretation of the entire breast in comparison to digital breast tomography (DBT) or mammography [50]. Overlapping areas of the sweep volumes are filtered through an arctangent function to provide smooth transitions.…”

Section: Methodsmentioning

confidence: 99%

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Lee

et al. 2016

Ultrasonics

Self Cite

Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

“…The image field of view was set to 51 mm depth. As this system includes photoacoustic tomography capabilities, we refer to it as the Breast Light and Ultrasound Combined Imaging (BLUCI) system (Carson, et al 2011, Sinha, et al 2010), Figure 1A.…”

Section: Methodsmentioning

confidence: 99%

Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging

Larson

Lee

Ultrasound in Medicine & Biology

et al. 2016

Self Cite

We report the design and performance of a mammographically configured, dual-sided, automated breast ultrasound (ABUS) 3D imaging system. Dual-sided imaging (superior and inferior) is compared with single-sided imaging to aid decisions on clinical implementation of the more complex, but potentially higher quality dual-sided imaging. Marked improvement in the image quality and coverage of the breast is found in dual-sided ultrasound over single-sided ultrasound. Among hypoechoic masses imaged, there are increases in the mean contrast-to-noise ratio (CNR) of 57% and 79%, respectively, for spliced dual sided vs. superior or inferior single-sided imaging. The fractional breast volume coverage, defined as the percentage volume in the transducer field of view that is imaged with clinically-acceptable quality, is improved from 59% in both superior and inferior single-sided imaging to 89% in dual-sided imaging. Applying acoustic coupling to the breast requires more effort or sophisticated methods in dual-sided imaging than in single-sided.