Automated Breast Volume Scanner Research Articles

The Value of an Automated Ultrasound System in the Detection of Synovitis.

Background The detection of joint swelling caused by synovitis is important for the diagnosis of inflammatory arthritis. Ultrasound (US) and MRI have proven to be more sensitive and reliable than physical examination, but they are time-consuming and expensive. The automated breast volume scanner was developed to acquire serial B-mode pictures of the female breast and these can be analyzed in all three dimensions. Objectives To analyze the value of automated B-mode ultrasound employing the ABVS system in detecting synovitis of the finger joints compared to manual ultrasound (mUS) and physical examination, using MRI as the gold standard. Methods 19 consecutive patients suffering from active rheumatoid (n=15) or psoriatic (n=4) arthritis were included. Automated and mUS were conducted with a linear array (ACUSON S2000™, 11 MHz). Multiplanar reconstruction enabled examination of the images for the presence of synovitis. Results 90% of the hand joints were assessable by automated ultrasound. Automated US detected 12.0, mUS 14.2, MRI 13.4, and clinical examination 4.1 positive joints – i. e. joints with synovitis - on average per patient. The inter-observer reliability of both assessors for automated and mUS, MRI, and physical examination, was 66.9%, 72.7%, 95.1%, and 88.9%, respectively. 84.3% of the joints classified as positive on MRI were confirmed by automated ultrasound, 85.5% on mUS, and 36.0 on physical examination. This translated into a sensitivity of 83.5%, 85.5%, and 36.0% for the three methods, respectively. Conclusion: Automated ultrasound is a promising ultrasound method for assessing small joints in patients with inflammatory arthritis.

Evaluation of an automated breast volume scanner according to the fifth edition of BI-RADS for breast ultrasound compared with hand-held ultrasound

ObjectivesTo investigate the automated breast volume scanner (ABVS) in comparison with hand-held ultrasound (HHUS) according to the fifth edition of BI-RADS ultrasound. Material and methodsA total of 831 lesions in 786 patients who underwent both HHUS and ABVS were included. Three radiologists independently evaluated the sonographic features of each lesion according to the fifth BI-RADS edition. The kappa coefficient (κ) was calculated for each BI-RADS descriptor and final assessment category. The accuracy of malignancy prediction and diagnostic performance of the BI-RADS descriptors were assessed using multivariate logistic regression and area under the receiver operator characteristic curve (AUC), respectively. ResultsABVS and HHUS showed moderate to good interobserver agreement (κ = 0.53–0.67 and 0.55–0.70, respectively) except in associated features (κ = 0.31 and 0.36, respectively) for BI-RADS lexicons. Irregular shape, a non-circumscribed margin, and posterior features (combined or shadowing) were independently associated with malignancy in both ABVS and HHUS. Calcification presence on ABVS (odds ratio [OR], 95% confidence interval [CI]: 2.09, 1.11–3.94) and non-parallel orientation on HHUS (OR, 95% CI: 2.04, 1.10–3.78) were independently associated with malignancy. There were no significant differences between ABVS and HHUS in sensitivity (84.2% vs. 84.2%), specificity (80.5% vs. 83.9%), or AUC (0.88 vs. 0.90). ConclusionsAccording to the fifth BI-RADS edition, ABVS is not statistically significantly different from HHUS with regard to interobserver variability and diagnostic performance.

Breast and Tumour Volume Measurements in Breast Cancer Patients Using 3-D Automated Breast Volume Scanner Images

BackgroundThe resection volume in relation to the breast volume is known to influence cosmetic outcome following breast-conserving therapy. It was hypothesised that three-dimensional ultrasonography (3-D US) could be used to preoperatively assess breast and tumour volume and show high association with histopathological measurements.MethodsBreast volume by the 3D-US was compared to the water displacement method (WDM), mastectomy specimen weight, 3-D MRI and three different calculations for breast volume on mammography. Tumour volume by the 3-D US was compared to the histopathological tumour volume and 3-D MRI. Relatedness was based on the intraclass correlation coefficient (ICC) with corresponding 95% confidence interval (95% CI). Bland–Altman plots were used to graphically display the agreement for the different assessment techniques. All measurements were performed by one observer.ResultsA total of 36 patients were included, 20 and 23 for the evaluation of breast and tumour volume (ductal invasive carcinomas), respectively. 3-D US breast volume showed ‘excellent’ association with WDM, ICC 0.92 [95% CI (0.80–0.97)]. 3-D US tumour volume showed a ‘excellent’ association with histopathological tumour volume, ICC 0.78 [95% CI (0.55–0.91)]. Bland–Altman plots showed an increased overestimation in lager tumour volumes measured by 3-D MRI compared to histopathological volume.Conclusions3-D US showed a high association with gold standard WDM for the preoperative assessment of breast volume and the histopathological measurement of tumour volume. 3-D US is an patient-friendly preoperative available technique to calculate both breast volume and tumour volume. Volume measurements are promising in outcome prediction of intended breast-conserving treatment.

The image performance of mucinous breast carcinoma on automated breast volume scanner and its pathologic features

Objective To explore the image performance of mucinous breast carcinoma(MBC) on automated breast volume scanner (ABVS) and its pathologic features, for the purpose of improving the recognition of this disease on ABVS, as well as diagnostic ability. Methods Retrospectively analyzed the sonogram and pathological data of 17 patients with 18 MBC lesions that included 9 PMBC(pure mucinous breast carcinoma) and 9 MMBC(mixed mucinous breast carcinoma), all patients were examined by ABVS before operation and confirmed by pathology. Results Luminal A was the main molecular subtype of MBC, as well as rarely lymph node metastases; The image of the MBC on ABVS mainly showed a round like or irregular mass, isoechoic or slightly lower with posterior acoustic enhancement, and clearly edge with microlobulated or angular; While on the coronal plane, it might presented unsmoothed contour, retraction phenomenon and expansive growth pattern like a spherical nodular; The image characteristics of the PMBC and MMBC lesions on ABVS had no statistical difference(P>0.05), but the PMBC often performed isoechoic mass with clearly marge, in contrast, the MMBC usually performed hypoechoic mass with indistinct and angular edge, and the malignant image features on the coronal plane. Conclusions The three vertical sections of ABVS are beneficial to observe the morphological features of MBC completely, which can help to identify it from the benign lesions, reduce misdiagnosis, and provide some guidance for MBC surgical treatment in the early stage. Key words: Ultrasonography; Breast neoplasms; Adenocarcinoma, mucinous; Automated breast volume scanner

The research of contrast on acceptance of automated breast volume scanner, digital screening mammography and handheld ultrasound

Objective To compare patients′ satisfaction and comfort associated with automated breast volume scanner (ABVS), handheld ultrasound (HHUS) and mammography (MMG). Methods Five hundred and twenty-eight women who performed ABVS examination and previously had undergone MMG and HHUS were enrolled. BMI, bra size, with or without lesion, lesion size, pain score of MMG, ABVS and HHUS were documented and analyzed. Results The pain score of MMG was significantly higher than that of ABVS [4.0 (2.0, 6.0) vs 2.0(1.0, 4.0)] (P<0.05). 92.4% of the cases were preferred for the ABVS examination, which was higher than that for the MMG (45.5%), and was similar to HHUS (94.7%). Bra size, BMI, with or without lesion and lesion size could affect the pain degree of ABVS. Conclusions Routine use of ABVS will be well tolerated by patients than MMG. Key words: Ultrasonography, mammary; Breast diseases; Automated breast volume scanner; X-rays

Can Automated Breast Volume Scanning be an Alternative Tool to Handheld Ultrasonography for Breast Cancer Screening?

BACKGROUND: To assess diagnostic value of automated breast volume scanning(ABVS) versus handheld ultrasonography (HHUS) in breast cancer screening. MATERIALS AND METHODS: All 110 participants received an ABVS examinationcompared with HHUS which was set up as a standard tool. The scans were interpretedby specialists blinded to the results of breast imaging and medical history. CohenKappa value was used to interpret agreement between diagnostic tools. RESULTS: On the basis of the Breast Imaging Reporting and Data System (BI-RADS)classification, of the 110 cases, an agreement was achieved in 102 cases (92.7%).Kappa value (K) was 0.885 representing almost perfect agreement. CONCLUSION: We concluded that ABVS is comparable to HHUS as an adjunctivetool to mammography for screening of breast cancer

Clinical research of automated breast volume scanner and MRI in diagnosis of breast cancer

Objective To evaluate the effectiveness of automated breast volume scanner(ABVS) and magnetic resonance imaging(MRI) in the detection and diagnosis of breast cancer and to assess the value of these modalities as well as the joint use of the two. Methods In this study, a total of 50 breast lesions in 37 patients proved by surgery and pathology were included. Before operation, all patients were underwent both ABVS and dynamic contrast-enhanced (DCE-MRI) examinations, and classified into groups according to BI-RADS classification. Then the effectiveness of the two examinations were contrasted, the image features on the two techniques were analyzed, and their differential diagnosis in benign and malignant breast lesions were compared. Results The sensitivity, specificity, accuracy, positive predictive value(PPV) and negative predictive value(NPV) of ABVS and MRI for the diagnosis of breast cancer were 91.67% and 95.83%, 88.46% and 80.77%, 90.00% and 88.00%, 88.00% and 82.14%, 92.00% and 95.45%, respectively, and there was no statistical difference between ABVS and MRI(P>0.05); The sensitivity and specificity of the combination of the two were 99.65% and 97.78%, respectively. The difference of the ABVS coronal features and MRI dynamic enhancement curve types between malignant breast lesions and the benign were statistically significant (P<0.05). Conclusions Both ABVS and MRI are effective to diagnose breast cancer well, while the combination of the two can improve the diagnosis more accurately. Key words: Ultrasonography; Breast neoplasms; Automated breast volume scanner; Magnetic resonance imaging, three-dimensional

Improving three-dimensional mechanical imaging of breast lesions with principal component analysis.

Elastography has emerged as a new tool for detecting and diagnosing many types of diseases including breast cancer. To date, most clinical applications of elastography have utilized two-dimensional strain images. The goal of this paper is to present a new quasi-static elastography technique that yields shear modulus images in three dimensions. An automated breast volume scanner was used to acquire ultrasound images of the breast as it was gently compressed. Cross-correlation between successive images was used to determine the displacement within the tissue. The resulting displacement field was filtered of all but compressive motion through principal component analysis. This displacement field was used to infer spatial distribution of shear modulus by solving a 3D elastic inverse problem. Three dimensional shear modulus images of benign breast lesions for two subjects were generated using the techniques described above. It was found that the lesions were visualized more clearly in images generated using the displacement data de-noised through the use of principal components. We have presented experimental and algorithmic techniques that lead to three-dimensional imaging of shear modulus using quasi-static elastography. This work demonstrates feasibility of this approach, and lays the foundation for images of other, more informative, mechanical parameters.

Automatic breast tumor detection in ABVS images based on convolutional neural network and superpixel patterns

Breast cancer is one of the most common female malignancies, as well as the second leading cause of mortality for women. Early detection and treatment can dramatically decrease the mortality rate. Recently, automated breast volume scanner (ABVS) has become one of the most frequently used diagnose methods for breast tumor screening because of its operator-independent and reproducible advantages. However, it is a challenging job to obtain the tumors’ accurate locations and shapes by reviewing hundreds of ABVS slices. In this paper, a novel computer-aided detection (CADe) system is developed to reduce clinicians’ reading time and improve the efficiency. The CADe system mainly contains three parts: tumor candidate acquisition, false-positive reduction and tumor segmentation. Firstly, a local phase-based approach is built to obtain breast tumor candidates for further recognition. Subsequently, a convolutional neural network (CNN) is applied to reduce false positives (FPs). The introduction of CNN can help to avoid complicated feature extraction as well as elevate the accuracy and efficiency. Finally, superpixel-based segmentation is used to outline the breast tumor. Here, superpixel-based local binary pattern (SLBP) is proposed to assist the segmentation, which improves the performance. The methods were evaluated on a clinical ABVS dataset whose abnormal cases were manually labeled by an experienced radiologist. The experiment results were mainly composed of two parts. At the FP reduction stage, the proposed CNN achieved 100% and 78.12% sensitivity with FPs/case of 2.16 and 0. At the segmentation stage, our SLBP obtained 82.34% true positive, 15.79% false positive and 83.59% Dice similarity. In summary, the proposed CADe system demonstrated promising potential to detect and outline breast tumors in ABVS images.

Three-dimensional Ultrasound Elasticity Imaging on an Automated Breast Volume Scanning System.

Ultrasound elasticity imaging has demonstrated utility in breast imaging, but it is typically performed with handheld transducers and two-dimensional imaging. Two-dimensional (2D) elastography images tissue stiffness of only a plane and hence suffers from errors due to out-of-plane motion, whereas three-dimensional (3D) data acquisition and motion tracking can be used to track out-of-plane motion that is lost in 2D elastography systems. A commercially available automated breast volume scanning system that acquires 3D ultrasound data with precisely controlled elevational movement of the 1D array ultrasound transducer was employed in this study. A hybrid guided 3D motion-tracking algorithm was developed that first estimated the displacements in one plane using a modified quality-guided search method, and then performed an elevational guided-search for displacement estimation in adjacent planes. To assess the performance of the method, 3D radiofrequency echo data were acquired with this system from a phantom and from an in vivo human breast. For both experiments, the axial displacement fields were smooth and high cross-correlation coefficients were obtained in most of the tracking region. The motion-tracking performance of the new method was compared with a correlation-based exhaustive-search method. For all motion-tracking volume pairs, the average motion-compensated cross-correlation values obtained by the guided-search motion-tracking method were equivalent to those by the exhaustive-search method, and the computation time was about a factor of 10 lesser. Therefore, the proposed 3D ultrasound elasticity imaging method was a more efficient approach to produce a high quality of 3D ultrasound strain image.

Agreement Between an Automated Volume Breast Scanner and Handheld Ultrasound for Diagnostic Breast Examinations.

To compare the agreement and interobserver variability of diagnostic handheld ultrasound (US) and a single volume on an automated breast volume scanner (ABVS) and to determine whether there was a significant difference if the ABVS was used by a sonographer or mammographic technologist. Ninety patients scheduled for diagnostic US examinations were randomized to either handheld US or the ABVS first. The AVBS was randomized between a sonographer and a mammographic technologist performing the study. The studies were blinded, randomized, and read by 2 radiologists. The lesion with the highest Breast Imaging Reporting and Data System (BI-RADS) score was used in the analysis. Final diagnoses were made by core biopsy or follow-up for 2 years. Lesions included 9 malignant and 81 benign. The 90 patients had a mean age ± SD of 53.1 ± 16.3 years. The κ value for agreement between the ABVS and handheld US was 0.831 (95% confidence interval, 0.744-0.925), whereas the global agreement for a 7-point BI-RADS score was 0.488 (0.372-0.560). The agreement between the ABVS and handheld US was nearly the same when the ABVS was used by a mammographic technologist (κ = 0.858 [0.723-0.963]) or sonographer (κ = 0.803 [0.596-1.000]; P = .47). The areas under the receiver operating characteristic curves for characterization by the ABVS were 0.91 (0.84-0.96) for reader 1 and 0.91 (0.83-0.96) for reader 2; those for handheld US were 0.91 (0.84-0.96) for reader 1 and 0.83 (0.74-0.90) for reader 2, with no statistical difference. The agreement based on pathologic images was κ = 0.831 (0.718-0.944); for handheld US, κ = 0.795 (0.623-0.967); and for the AVBS, κ = 0.869 (0.725-1.000). Performing a single-view diagnostic ABVS examination has good agreement with a handheld diagnostic US workup. There is no difference if the ABVS is used by a sonographer or mammographic technologist.

Automated breast volume scanner (ABVS) in breast cancer - a review.

The early detection of breast cancer is essential for its timely diagnosis and treatment. Many imaging modalities are continually being developed to screen and diagnose breast cancer at an early stage. Especially, ultrasound (US) is one of useful diagnostic tools to dis- tinguish benign from malignant masses of the breast. However, the US image has some limitations, such as low resolution and low con- trast, speckle noise, and blurry edges between various organs. Recent technological advancements in the area resulted in development of automated breast volume scanner (ABVS) that could increase the diagnostic confidence for a physician and provide a possible solution to improve the positive predictive value of breast biopsy. The purpose of the present review article was to explore the advantages and technical advancements of ABVS for diagnosing breast cancer patients.

Diagnostic Performance of Automated Breast Volume Scanning (ABVS) Compared to Handheld Ultrasonography With Breast MRI as the Gold Standard

This study aimed to compare the diagnostic value of automated breast volume scanning (ABVS) to that of handheld ultrasonography (HHUS) using breast magnetic resonance imaging (MRI) as the gold standard. Twenty-eight patients with 39 examined breasts with at least one lesion visible in breast MRI underwent HHUS and ABVS. Detection rate, localization, maximum diameter, and Breast Imaging Reporting and Data System classification were compared. Sensitivity, specificity, diagnostic accuracy, positive predictive value, and negative predictive value were calculated for HHUS and ABVS. Lesion localization and maximum diameters based on HHUS and ABVS were compared to size measurement in MRI. Breast Imaging Reporting and Data System categories based on each method were compared to the MRI diagnosis (malignant or benign) or, if available (21 cases), with the histologic diagnosis. MRI detected 72 lesions, ABVS 59 lesions, and HHUS 54 lesions. Malignancy was proven histopathologically in 15 cases. There was no significant difference between ABVS and HHUS in terms of sensitivity (93.3% vs. 100%), specificity (83.3% vs. 83.3%), diagnostic accuracy (87.2% vs. 89.7%), positive predictive value (77.8% vs. 78.9%), and negative predictive value (95.2% vs. 100%). Agreement regarding lesion localization (same quadrant) was 94.3% for ABVS and MRI and 91.2% for HHUS and MRI. Lesion size compared to MRI lesion size was assessed correctly (+/- 3 mm) in 79.4% (HHUS) and 80% (ABVS). The correlation of size measurement was slightly higher for ABVS-MRI (r = 0.89) than for HHUS-MRI (r = 0.82) with P < .001. ABVS can be used as an alternative to HHUS. ABVS has the advantage of operator independence and better reproducibility although it is limited in evaluating axillary lymph nodes and lacks Doppler or elastrography capabilities, which sometimes provide important supplementary information in HHUS.

Comparison between automated breast volume scanner (ABVS) versus hand-held ultrasound as a second look procedure after magnetic resonance imaging.

To evaluate the agreement between automated breast volume scanner (ABVS) and conventional ultrasound (US) as a second-look (SL) tool for assessing additional findings found on MRI. Over a 7-month period, we prospectively assigned to SL-US and SL-ABVS all patients undergoing 1.5T breast MRI in whom additional findings were found. Five experienced breast radiologists independently interpreted SL-US and SL-ABVS in blinded sessions to evaluate the detection rate of MRI findings and assign them to BI-RADS categories. We calculated the agreement between the two methods in assessing MRI findings as significant (BI-RADS 3-5) versus not significant (BI-RADS 1-2), as well as their cancer detection rate. In a population of 131 patients, SL-ABVS and SL-US showed a comparable detection rate of MRI findings (69.3 vs. 71.5%) (p > 0.05; McNemar test), with an almost perfect agreement in assessing them as significant or not (k = 0.94). This translated into a comparably high cancer detection rate (83.8% for SL-ABVS vs. 87.0% for SL-US). Only 1/31 cancers was missed by SL-ABVS. SL-ABVS and SL-US are nearly equivalent in assessing the significance of MRI findings, leading to a comparable cancer detection rate. SL-ABVS has the potential to replace SL-US in the SL scenario. • SL-ABVS shows almost perfect agreement with SL-US in assessing MRI findings. • SL-ABVS shows a comparably high cancer detection rate with respect to SL-US. • SL-ABVS has the potential to replace SL-US in evaluating additional MRI findings.

Clinical Applicationo of the Automated Breast Volume Scanner (ABVS) in the Diagnosis of Breast Lesions: Single Center Experience

Clinical Applicationo of the Automated Breast Volume Scanner (ABVS) in the Diagnosis of Breast Lesions: Single Center Experience

Bi-Rads 3 Lesions in Screening Automated Breast Volume Scanner: Comparison with 6-Months Follow-Up Hand-Held Breast Us

Bi-Rads 3 Lesions in Screening Automated Breast Volume Scanner: Comparison with 6-Months Follow-Up Hand-Held Breast Us

The Characteristics of Breast Cancer Detected in Screening Automated Breast Volume Scanner

The Characteristics of Breast Cancer Detected in Screening Automated Breast Volume Scanner

Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer

ObjectivesTo investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. MethodsWe examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. ResultsBy univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n=128) were retraction phenomenon (odds ratio [OR]=10.188), post-acoustic shadowing (OR=5.112), and echogenic halo (OR=3.263, P<0.001). The predictive factors of the Human-epidermal-growth-factor-receptor-2-amplified subtype (n=39) were calcifications (OR=6.210), absence of retraction phenomenon (OR=4.375), non-mass lesions (OR=4.286, P<0.001), absence of echogenic halo (OR=3.851, P=0.035), and post-acoustic enhancement (OR=3.641, P=0.008). The predictors for the Triple-Negative subtype (n=47) were absence of retraction phenomenon (OR=5.884), post-acoustic enhancement (OR=5.255, P<0.001), absence of echogenic halo (OR=4.138, P=0.002), and absence of calcifications (OR=3.363, P=0.001). Predictors for the Luminal-B subtype (n=89) had a relatively lower association (OR≤2.328). By multivariate logistic regression analysis, retraction phenomenon was the strongest independent predictor for the Luminal-A subtype (OR=9.063, P<0.001) when present and for the Triple-Negative subtype (OR=4.875, P<0.001) when absent. ConclusionsABVS imaging features, especially retraction phenomenon, have a strong correlation with the molecular subtypes, expanding the scope of ultrasound in identifying breast cancer subtypes with confidence.

Initial experience of automated breast volume scanning (ABVS) and ultrasound elastography in predicting breast cancer subtypes and staging

ObjectivesBreast cancer is a heterogeneous disease consisting of distinct histopathological subtypes with different clinical outcomes. In this article, we identified the automated breast volume scanning (ABVS) and shear wave velocity (SWV) characteristics of different pathological types of breast carcinoma. MethodsA retrospective review of both ABVS and SWV imaging of 118 consecutive breast masses was performed. The imaging features of both techniques were assessed with reference to histopathological results. ResultsEcho heterogeneity with a smooth and lobulated margin was a significant feature more frequently found in mucinous carcinoma groups (100%, P < 0.05). Between different stages of ductal carcinoma, echo homogeneity was more likely in high-grade ductal carcinomas (P < 0.05). SWV differences existed between inside tumor areas and either tumor boundary or tissues outside the tumors (P < 0.05), and values differed between different breast carcinoma stages. The central and tumor margin areas of ductal carcinomas were much harder than in tubular carcinoma and micro-carcinoma, respectively (P < 0.05). SWV ROC curve analyses yielded a cut-off value of 3.015 m/s between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma in the central part of lesions, with 83.5% sensitivity and 80% specificity for T0 vs T1-3 staging. ConclusionsSince some features were associated with different breast carcinoma types and stages, ABVS and SWV imaging has the potential to give clues about breast carcinoma differentiation in a non-invasive manner.

3-D ultrafast ultrasound strain imaging to improve breast cancer detection

The automated breast volume scanner (ABVS) is used for breast cancer detection and consists of a linear transducer translating over the breast while collecting ultrasound data to reconstruct a breast volume. Although the ABVS shows high sensitivity, specificity remains a limitation resulting in high recall-rates. To improve ABVS specificity, we verified if it is feasible to implement 3-D strain imaging to discriminate between malignant (firmly-bound and stiff) and benign lesions (loosely bound and less stiff) based on lesion-to-surrounding-tissue connectivity (shear strain) and on stiffness (axial strain). Three phantoms containing loosely- (1) and firmly-bound (2) three times stiffer lesions were scanned twice by the ABVS, pre- and post-deformation (0.5 mm). Data was collected by plane-wave imaging to reduce acquisition times below one breath-hold. Displacements were calculated by cross-correlation from which axial and octahedral-shear strains were derived. Shear strains were distributed tightly and globally around the lesion for phantom (1) and (2), respectively. Using axial strain, the lesions could be clearly differentiated from the surrounding tissue in both phantoms (CNRe 30; SNRe 19dB). Therefore, we can conclude that it is feasible to implement 3-D strain imaging in the ABVS, and to improve its specificity by discriminating lesions by elastic properties.