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This purpose of this study is to correlate a new shear-wave elastography (SWE) parameter, mass characteristic frequency (fmass) and other elasticity measure with the prognostic histological factors and immunohistochemical (IHC) biomarkers for the evaluation of heterogeneous breast carcinomas. The new parameter, fmass, first introduced in this paper, is defined as the ratio of the averaged minimum shear wave speed taken spatially within regions of interest to the largest mass dimension. 264 biopsy-proven breast cancerous masses were included in this study. Mean (Emean), maximum (Emax), minimum (Emin) shear wave elasticity and standard deviation (Esd) of shear wave elasticity were found significantly correlated with tumor size, axillary lymph node (ALN) status, histological subtypes and IHC subtypes. The areas under the curve for the ALN prediction are 0.73 (95% confidence interval [CI]: 0.67-0.80) and 0.75 (95% CI: 0.69-0.81) for the combination of Emean with Breast Imaging Reporting and Data System (BI-RADS) score and Emax with BI-RADS score, respectively. fmass was significantly correlated with the presence of calcifications, ALN status, histological grade, the expressions of IHC biomarkers and IHC subtypes. To conclude, poor prognostic factors were associated with high shear wave elasticity values and low mass characteristic frequency value. Therefore, SWE provides valuable information that may help with prediction of breast cancer invasiveness.

Background Early prediction of tumor response to neoadjuvant chemotherapy (NACT) is crucial for optimal treatment and improved outcome in breast cancer patients. The purpose of this study is to investigate the role of shear wave elastography (SWE) for early assessment of response to NACT in patients with invasive breast cancer. Methods In a prospective study, 62 patients with biopsy-proven invasive breast cancer were enrolled. Three SWE studies were conducted on each patient: before, at mid-course, and after NACT but before surgery. A new parameter, mass characteristic frequency (fmass), along with SWE measurements and mass size was obtained from each SWE study visit. The clinical biomarkers were acquired from the pre-NACT core-needle biopsy. The efficacy of different models, generated with the leave-one-out cross-validation, in predicting response to NACT was shown by the area under the receiver operating characteristic curve and the corresponding sensitivity and specificity. Results A significant difference was found for SWE parameters measured before, at mid-course, and after NACT between the responders and non-responders. The combination of Emean2 and mass size (s2) gave an AUC of 0.75 (0.95 CI 0.62–0.88). For the ER+ tumors, the combination of Emean_ratio1, s1, and Ki-67 index gave an improved AUC of 0.84 (0.95 CI 0.65–0.96). For responders, fmass was significantly higher during the third visit. Conclusions Our study findings highlight the value of SWE estimation in the mid-course of NACT for the early prediction of treatment response. For ER+ tumors, the addition of Ki-67improves the predictive power of SWE. Moreover, fmass is presented as a new marker in predicting the endpoint of NACT in responders.

BACKGROUND Shear wave elastography (SWE) shows promise in peripheral neuropathy evaluation but has potential limitations due to tissue size and heterogeneity. We tested SWE sensitivity to elasticity change and the effect of probe position in a median nerve cadaver model. METHODS Ten specimens were used to measure median nerve elasticity under increasing loads using SWE and indentation. Measurements were compared using repeated-measures analysis of variance. RESULTS Indentation and SWE-based longitudinal nerve elasticity increased with tensile loading (P < .01), showing a similar relationship. Acquisition in a transverse plane showed lower values compared with longitudinal measurements, mostly under higher loads (P = .03), as did postdissection elasticity (P = .02). Elasticity did not change when measured proximal to the carpal tunnel. CONCLUSIONS Longitudinal SWE is sensitive to changes in median nerve elasticity. Measuring elasticity of peripheral nerves noninvasively could elucidate intra-neural pathology related to compression neuropathies, and proof to be of added value as a diagnostic or prognostic tool.

Sonographic features associated with margins, shape, size, and volume of thyroid nodules are used to assess their risk of malignancy. Automatically segmenting nodules from normal thyroid gland would enable an automated estimation of these features. A novel multi-output convolutional neural network algorithm with dilated convolutional layers is presented to segment thyroid nodules, cystic components inside the nodules, and normal thyroid gland from clinical ultrasound B-mode scans. A prospective study was conducted, collecting data from 234 patients undergoing a thyroid ultrasound exam before biopsy. The training and validation sets encompassed 188 patients total; the testing set consisted of 48 patients. The algorithm effectively segmented thyroid anatomy into nodules, normal gland, and cystic components. The algorithm achieved a mean Dice coefficient of 0.76, a mean true positive fraction of 0.90, and a mean false positive fraction of 1.61 × 10-6. The values are on par with a conventional seeded algorithm. The proposed algorithm eliminates the need for a seed in the segmentation process, thus automatically detecting and segmenting the thyroid nodules and cystic components. The detection rate for thyroid nodules and cystic components was 82% and 44%, respectively. The inference time per image, per fold was 107ms. The mean error in volume estimation of thyroid nodules for five select cases was 7.47%. The algorithm can be used for detection, segmentation, size estimation, volume estimation, and generating thyroid maps for thyroid nodules. The algorithm has applications in point of care, mobile health monitoring, improving workflow, reducing localization time, and assisting sonographers with limited expertise.

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt, 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt, VGG-SegNet &gt, NIH &gt, SegNet. The HDL runs in &lt, 1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Background COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. Methodology Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. Results Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values

Many of the current techniques in transient elastography, such as shear wave elastography (SWE) assume a dominant planar shear wave propagating in an infinite medium. This underlying assumption, however, can be easily violated in real scenarios in vivo, leading to image artifacts and reconstruction errors. Other approaches that are not bound to planar shear wave assumption, such solutions based on the partial differential equation, can potentially overcome the shortcomings of the conventional SWE. The main objective of this paper is to demonstrate the advantages of the modified error in constitutive equations (MECE) formulation with total variation regularization (MECE + TV) over SWE in reconstructing the elastic moduli of different tissue-mimicking phantoms. Experiments were conducted on phantoms with inclusions of well-defined shapes to study the reconstruction of specific features relevant to practical applications. We compared the performances of MECE + TV and SWE in terms of quantitative metrics to estimate reconstruction accuracy, inclusion shape recovery, edge preservation and edge sharpness, inclusion size representation, and shear elasticity and contrast accuracies. The results indicate that the MECE + TV approach outperforms SWE based on several of these metrics. It is concluded that, with further development, the proposed method may offer elastography reconstructions that are superior to SWE in clinical applications.

ObjectivesTo evaluate the predictive performance of comb-push ultrasound shear elastography for the differentiation of reactive and metastatic axillary lymph nodes.MethodsFrom June 2014 through September 2018, 114 female volunteers (mean age 58.1±13.3 years; range 28-88 years) with enlarged axillary lymph nodes identified by palpation or clinical imaging were prospectively enrolled in the study. Mean, standard deviation and maximum shear wave elastography parameters from 117 lymph nodes were obtained and compared to fine needle aspiration biopsy results. Mann-Whitney U test and ROC curve analysis were performed.ResultsThe axillary lymph nodes were classified as reactive or metastatic based on the fine needle aspiration outcomes. A statistically significant difference between reactive and metastatic axillary lymph nodes was observed based on comb-push ultrasound shear elastography (CUSE) results (pConclusionsCUSE provided a quantifiable parameter that can be used for the assessment of enlarged axillary lymph nodes to differentiate between reactive and metastatic processes.

Rationale and Objectives Low specificity of traditional ultrasound in differentiating benign from malignant thyroid nodules leads to a great number of unnecessary (ie, benign) fine-needle aspiration biopsies that causes a significant financial and physical burden to the patients. Ultrasound shear wave elastography is a technology capable of providing additional information related to the stiffness of tissues. In this study, quantitative stiffness values acquired by ultrasound shear wave elastography in two different imaging planes were evaluated for the prediction of malignancy in thyroid nodules. In addition, the association of elasticity measurements with sonographic characteristics of thyroid gland and nodules is presented. Materials and Methods A total number of 155 patients (106 female and 49 male) (average age 57.48 ± 14.44 years) with 173 thyroid nodules (average size 24.89 ± 15.41 mm, range 5–68 mm) scheduled for fine-needle aspiration biopsy were recruited from March 2015 to May 2017. Comb-push shear elastography imaging was performed at longitudinal and transverse anatomic planes. Mean (Emean) and maximum (Emax) elasticity values were obtained. Results Measurements at longitudinal view were statistically significantly higher than measurements at transverse view. Nodules with calcifications were associated with increased elasticity, and nodules with a vascular component or within an enlarged thyroid gland (goiter) were associated with a lower elasticity value. Receiver operating characteristic curve analysis was performed for Emean and Emax at each imaging plane and for the average of both planes. Sensitivity of 95.45%, specificity of 86.61%, 0.58 positive predictive value, and 0.99 negative predictive value were achieved by the average of the two planes for each Emean and Emax parameters, with area under the curve of 92% and 93%, and a cutoff value of 49.09 kPa and 105.61 kPa, respectively. Conclusions The elastic properties of thyroid nodules showed promise to be a good discriminator between malignant and benign nodules (P

To prospectively evaluate the diagnostic performance of 3-dimensional (3D) shear wave elastography (SWE) for assessing thyroid nodules.A total of 176 surgically or cytologically confirmed thyroid nodules (63 malignant and 113 benign) in 176 patients who had undergone conventional ultrasound (US), 2-dimensional (2D) SWE, and 3D SWE examinations were included in this study. Quantitative elasticity values (mean elasticity, maximum elasticity, and standard deviation of elasticity of a large region of interest and mean elasticity of a 2-mm region of interest) were measured on 2D and 3D SWE. Diagnostic performances of conventional US, 2D SWE, and 3D SWE were assessed. The role of 2D and 3D SWE in reducing unnecessary fine-needle aspiration (FNA) for nodules with low suspicion was also evaluated.The diagnostic performances in terms of the area under the receiver operating characteristic curve were 0.612 for conventional US, 0.836 for 2D SWE (P .001 in comparison with conventional US), and 0.839 for 3D SWE (P .001 in comparison with conventional US). The mean elasticity achieved the highest diagnostic performance in 2D SWE, whereas the standard deviation of elasticity achieved the highest performance in 3D SWE, although no significant difference was found between them (P .05). Three-dimensional SWE increased the specificity in comparison with 2D SWE (88.5% versus 82.3%; P = .039). For the 37 nodules with low suspicion on conventional US imaging, 2D SWE was able to avoid unnecessary FNA in 77.1% (27 of 35) of benign nodules, and 3D SWE further increased the number to 88.6% (31 of 35).Three-dimensional SWE is a useful tool for predicting thyroid nodule malignancy and reducing unnecessary FNA procedures in thyroid nodules with low suspicion of malignancy.

Deep learning is a powerful tool that became practical in 2008, harnessing the power of Graphic Processing Unites, and has developed rapidly in image, video, and natural language processing. There are ongoing developments in the application of deep learning to medical data for a variety of tasks across multiple imaging modalities. The reliability and repeatability of deep learning techniques are of utmost importance if deep learning can be considered a tool for assisting experts, including physicians, radiologists, and sonographers. Owing to the high costs of labeling data, deep learning models are often evaluated against one expert, and it is unknown if any errors fall within a clinically acceptable range. Ultrasound is a commonly used imaging modality for breast cancer screening processes and for visually estimating risk using the Breast Imaging Reporting and Data System score. This process is highly dependent on the skills and experience of the sonographers and radiologists, thereby leading to interobserver variability and interpretation. For these reasons, we propose an interobserver reliability study comparing the performance of a current top-performing deep learning segmentation model against three experts who manually segmented suspicious breast lesions in clinical ultrasound (US) images. We pretrained the model using a US thyroid segmentation dataset with 455 patients and 50,993 images, and trained the model using a US breast segmentation dataset with 733 patients and 29,884 images. We found a mean Fleiss kappa value of 0.78 for the performance of three experts in breast mass segmentation compared to a mean Fleiss kappa value of 0.79 for the performance of experts and the optimized deep learning model.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Noninvasive measurement of tissue viscoelastic properties is gaining more attention for screening and diagnostic purposes. Recently, measuring dynamic response of tissue under a constant force has been studied for estimation of tissue viscoelastic properties in terms of retardation times. The essential part of such a test is an instrument that is capable of creating a controlled axial force and is suitable for clinical applications. Such a device should be lightweight, portable, and easy to use for patient studies to capture tissue dynamics under external stress. In this paper, we present the design of an automated compression device for studying the creep response of materials with tissue-like behaviors. The device can be used to apply a ramp-and-hold force excitation for a predetermined duration of time and it houses an ultrasound probe for monitoring the creep response of the underlying tissue. To validate the performance of the device, several creep tests were performed on tissue-mimicking phantoms, and the results were compared against those from a commercial mechanical testing instrument. Using a second-order Kelvin–Voigt model and surface measurement of the forces and displacements, retardation times ${\boldsymbol{T}_1}$ and ${\boldsymbol{T}_2}$ were estimated from each test. These tests showed strong agreement between our automated compression device and the commercial mechanical testing system, with an average relative error of 2.9% and 12.4%, for ${\boldsymbol{T}_1}$ and ${\boldsymbol{T}_2}$ , respectively. Also, we present the application of compression device to measure local retardation times for four different phantoms with different size and stiffness.

C-Elastography (CE) is a new ultrasound technique that locally maps the non-linear elasticity of soft tissue using low-frequency (150–250 Hz) shear waves generated by the acoustic radiation force (ARF). CE is based on a recent finding that the magnitude of the ARF in an isotropic tissue-like solid is related linearly to a third-order modulus of elasticity, C, which is responsible for the coupling between deviatoric and volumetric constitutive behaviors. The main objective of the work described here was to examine the feasibility of using and performance of C-elastography in differentiating and characterizing soft tissue via a pilot study on ex vivo tissue and tissue-mimicking inclusions cast in a gelatin block. In this vein, the CE technique deploys a combination of ultrasound motion sensing and 3-D visco-elastodynamic simulation to estimate the non-linear modulus C. As ultrasound focusing inherently confines the ARF to a small region, CE provides the means for measuring C within O(mm3) volumes. Equipped with such data analysis, we performed in vitro CE experiments on agar-based, xenograft and normal breast tissue samples embedded in a gelatin matrix. The compound C-elastograms indicate marked (and sharp) C-contrast, with average values of 1.9 and 5.6 at push points inside the featured soft and hard inclusions, respectively.

Singular value based spatiotemporal clutter filtering (SVD-STF) can significantly improve the sensitivity of blood flow imaging in small vessels without using contrast agents. However, despite effective clutter filtering, large physiological motion in thyroid imaging can impact coherent integration of the Doppler signal and degrade the visualization of the underlying vasculature. In this study, we hypothesize that motion correction of the clutter filtered Doppler ensemble, prior to the power Doppler estimation, can considerably improve the visualization of smalls vessels in suspicious thyroid nodules. We corroborated this hypothesis by conducting in vivo experiments on 10 female patients in the age group 44–82 yrs, with at least one thyroid nodule suspicious of malignancy, with recommendation for fine needle aspiration biopsy. Ultrasound images were acquired using a clinical ultrasound scanner, implemented with compounded plane wave imaging. Axial and lateral displacements associated with the thyroid nodules were estimated using 2D normalized cross-correlation. Subsequently, the tissue clutter associated with the Doppler ensemble was suppressed using SVD-STF. Motion correction of the clutter-filtered Doppler ensemble was achieved using a spline based sub-pixel interpolation. The results demonstrated that power Doppler images of thyroid nodules were noticeably degraded due to large physiological motion of the pulsating carotid artery in the proximity. The resultant power Doppler images were corrupted with signal distortion, motion blurring and occurrence of artificial shadow vessels and displayed visibly low signal-to-background contrast. In contrast, the power Doppler images obtained from the motion corrected ultrasound data addressed the issue and considerabley improved the visualization of blood flow. The signal-to-noise ratio and the contrast-to-noise ratio increased by up to 15.2 dB and 12.1 dB, respectively. Across the ten subjects, the highest improvement was observed for the nodule with the largest motion. These preliminary results show the ability of using motion correction to improve the visualization of small vessel blood flow in thyroid, without using any contrast agents. The results of this feasibility study were encouraging, and warrant further development and more in vivo validation in moving tissues and organs.

In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13-55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.

Shear wave elastography is emerging as a clinically valuable diagnostic tool to differentiate between benign and malignant breast masses. Elastography techniques assume that soft tissue can be modelled as a purely elastic medium. However, this assumption is often violated as soft tissue exhibits viscoelastic properties. In order to explore the role of viscoelastic parameters in suspicious breast masses, a study was conducted on a group of patients using shear wave dispersion ultrasound vibrometry in the frequency range of 50-400 Hz. A total of 43 female patients with suspicious breast masses were recruited before their scheduled biopsy. Of those, 15 patients did not meet the data selection criteria. Voigt model based shear elasticity showed a significantly (p = 7.88x10(-6)) higher median value for the 13 malignant masses (16.76±13.10 kPa) compared to 15 benign masses (1.40±1.12 kPa). Voigt model based shear viscosity was significantly different (p = 4.13x10(-5)) between malignant (8.22±3.36 Pa-s) and benign masses (2.83±1.47 Pa-s). Moreover, the estimated time constant from the Voigt model, which is dependent on both shear elasticity and viscosity, differed significantly (p = 6.13x10(-5)) between malignant (0.68±0.33 ms) and benign masses (3.05±1.95 ms). Results suggest that besides elasticity, viscosity based parameters like shear viscosity and time constant can also be used to differentiate between malignant and benign breast masses.

In this work, tissue stiffness estimates are used to differentiate between benign and malignant breast masses in a group of pre-biopsy patients. The rationale being that breast masses are often stiffer than healthy tissue; furthermore, malignant masses are stiffer than benign masses. The comb-push ultrasound shear elastography (CUSE) method is used to noninvasively assess a tissue’s mechanical properties. CUSE utilizes a simultaneous multiple laterally spaced radiation force (ARF) excitations and detection sequence to reconstruct the region of interest (ROI) shear wave speed map, from which a tissue stiffness property is quantified by Young’s modulus. In this study, the tissue stiffness of 73 breast masses is interrogated. The mean shear wave speeds for malignant masses (3.42 ± 1.32 m/s) were higher than benign breast masses (6.04 ± 1.25 m/s). These speed values correspond to higher stiffness in malignant breast masses (114.9 ± 40.6 kPa) than benign masses (39.4 ± 28.1 kPa and p < 0.001), when tissue elasticity is quantified by Young’s modulus. A Young’s modulus > 83 kPa is established as a cut-off value for differentiating between malignant and benign suspicious breast masses, with receiver operating characteristic curve (ROC) of 89.19% sensitivity, 88.69% specificity, and 0.911 for the area under the curve (AUC).

Tissue properties such as elasticity and viscosity have been shown to be related to such tissue conditions as contraction, edema, fibrosis, and fat content among others. Magnetic Resonance Elastography has shown outstanding ability to measure the elasticity and in some cases the viscosity of tissues, especially in the liver, providing the ability to stage fibrotic liver disease similarly to biopsy. We discuss ultrasound methods of measuring elasticity and viscosity in tissues. Many of these methods are becoming widely available in the extant ultrasound machines distributed throughout the world. Some of the methods to be discussed are in the developmental stage. The advantages of the ultrasound methods are that the imaging instruments are widely available and that many of the viscoelastic measurements can be made during a short addition to the normal ultrasound examination time. In addition, the measurements can be made by ultrasound repetitively and quickly allowing evaluation of dynamic physiologic function in circumstances such as muscle contraction or artery relaxation. Measurement of viscoelastic tissue mechanical properties will become a consistent part of clinical ultrasound examinations in our opinion.

We present a full-wave inversion approach with total variation regularization for elastography. The proposed method is based on the minimization of an error in constitutive equations functional augmented with a least squares data misfit term referred to as MECE for “modified error in constitutive equations.” The main theme of this paper is to demonstrate several key strengths of the proposed method on experimental data. In addition, some illustrative examples are provided where the proposed method is compared with a common shear wave elastography (SWE) approach. To this end, ultrasonically tracked displacement data from an acoustic radiation force (ARF) pulse are used in different phantoms including phantom with layered inclusion and triangle inclusion. The results indicate that the MECE approach can produce accurate shear modulus reconstructions in comparison with SWE, especially around the sharp edges in the layered and triangle inclusions. We compare shear modulus reconstruction using MECE and SWE with original inclusion shapes using two-dimensional normalized zero mean cross correlation, edge preservation index and dice coefficient similarity index. [Work supported by NIH Grant R01 CA174723.]

Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From a measured large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate NEMs in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of 31 subjects scanned to date, several had repeated scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom, digitally controlled, uniaxial compression device with force feedback. RF-data were acquired using plane wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8 N/s. A two-dimensional (2D) block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in the Blatz model of tissue elasticity are estimated using iterative optimization. Repeatability between both modes and elastic modulus maps is measured and compared. Preliminary results indicate that when images are acquired in the same region of tissue, the modulus maps are consistent. [Work supported by NIH R01CA195527.]Compression elastography allows the precise measurement of large deformations of soft tissue in vivo. From a measured large deformation, an inverse problem for both the linear and nonlinear elastic moduli distributions can be solved. As part of a larger clinical study to evaluate NEMs in breast cancer, we evaluate the repeatability of linear and nonlinear modulus maps from repeat measurements. Within the cohort of 31 subjects scanned to date, several had repeated scans. These repeated scans were processed to evaluate NEM repeatability. In vivo data were acquired by a custom, digitally controlled, uniaxial compression device with force feedback. RF-data were acquired using plane wave imaging, at a frame-rate of 200 Hz, with a ramp-and-hold compressive force of 8N, applied at 8 N/s. A two-dimensional (2D) block-matching algorithm was used to obtain sample-level displacement fields which were then tracked at subsample resolution using 2D cross correlation. Linear and nonlinear elasticity parameters in the Bl...

BACKGROUND:Lesion stiffness measured by shear wave elastography has shown to effectively separate benign from malignant breast masses. The aim of this study was to evaluate different aspects of Comb-push Ultrasound Shear Elastography (CUSE) performance in differentiating breast masses. METHODS:With written signed informed consent, this HIPAA- compliant, IRB approved prospective study included patients from April 2014 through August 2016 with breast masses identified on conventional imaging. Data from 223 patients (19-85 years, mean 59.93±14.96 years) with 227 suspicious breast masses identifiable by ultrasound (mean size 1.83±2.45cm) were analyzed. CUSE was performed on all patients. Three regions of interest (ROI), 3 mm in diameter each, were selected inside the lesion on the B-mode ultrasound which also appeared in the corresponding shear wave map. Lesion elasticity values were measured in terms of the Young's modulus. In correlation to pathology results, statistical analyses were performed. RESULTS:Pathology revealed 108 lesions as malignant and 115 lesions as benign. Additionally, 4 lesions (BI-RADS 2 and 3) were considered benign and were not biopsied. Average lesion stiffness measured by CUSE resulted in 84.26% sensitivity (91 of 108), 89.92% specificity (107 of 119), 85.6% positive predictive value, 89% negative predictive value and 0.91 area under the curve (P 0.21). CONCLUSION:CUSE was able to distinguish between benign and malignant breast masses with high sensitivity and specificity. Continuity of stiffness maps allowed for choosing multiple quantification ROIs which covered large areas of lesions and resulted in similar diagnostic performance based on average and maximum elasticity. The overall results of this study, highlights the clinical value of CUSE in differentiation of breast masses based on their stiffness.

PURPOSE:The purpose of our study is to correlate quantitatively measured tumor stiffness with immunohistochemical (IHC) subtypes of breast cancer. Additionally, the influence of prognostic histologic features (cancer grade, size, lymph node status, and histological type and grade) to the tumor elasticity and IHC profile relationship will be investigated. METHODS:Under an institutional review board (IRB) approved protocol, B-mode ultrasound (US) and comb-push ultrasound shear elastography (CUSE) were performed on 157 female patients with suspicious breast lesions. Out of 157 patients 83 breast cancer patients confirmed by pathology were included in this study. The association between CUSE mean stiffness values and the aforementioned prognostic features of the breast cancer tumors were investigated. RESULTS:Our results demonstrate that the most statistically significant difference (p = 0.0074) with mean elasticity is tumor size. When considering large tumors (size ≥ 8mm), thus minimizing the statistical significance of tumor size, a significant difference (p< 0.05) with mean elasticity is obtained between luminal A of histological grade I and luminal B (Ki-67 > 20%) subtypes. CONCLUSION:Tumor size is an independent factor influencing mean elasticity. The Ki-67 proliferation index and histological grade were dependent factors influencing mean elasticity for the differentiation between luminal subtypes. Future studies on a larger group of patients may broaden the clinical significance of these findings.

The following information is missing from the Funding section: This work is supported by grants R01CA148994, R01CA174723, R01 EB017213, and R01CA168575 from the National Institutes of Health-National Cancer Institute. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Minimally invasive procedures using ultrasound are becoming prominent, especially in applications like biopsy, catheter guidance, and surgeries. In our previous work a catheter tip is fitted with a miniature omnidirectional piezoelectric crystal; the ultrasonic field from the imaging array and the piezoelectric crystal interact resulting in a symmetric Doppler shift. Previously, a frequency domain algorithm was used to identify pixels having the symmetric Doppler shift. The identified pixels can then be visualized on B-mode and color map with a unique color thus representing the location of the catheter tip. However, frequency domain algorithms are computationally expensive and require changes to data workflow. Using mean frequency estimators with symmetric Doppler shift results in the catheter tip being displayed as zero mean noise making it hard to distinguish from color Doppler noise. To overcome this limitation, complex demodulation can be used. Modulating the time domain signal with sine and cosine by the piezoelectric excitation frequency and summing them up results in cancellation of one of the symmetric Doppler components. With only one frequency component remaining mean frequency estimators can be used to identify the location of the catheter. The presented algorithm has lower computational complexity and requires minimal change in software design.

Vibro-acoustography is an imaging method based on audio-frequency harmonic vibrations induced in the object by the radiation force of focused ultrasound. The purpose of this study is to investigate features of vibro-acoustography images and manifestation of various tissue structures and calcifications in such images. Our motivation for this study is to pave the way for further in vitro and in vivo applications of vibro-acoustography. Here, vibro-acoustography images of excised prostate and in vivo breast are presented and compared with images obtained with other modalities. Resulting vibro-acoustography images obtained with a 3 MHz ultrasound transducer and at a vibration frequency of 50–60 kHz show soft tissue structures, tissue borders, and microcalcifications with high contrast, high resolution, and no speckle. It is concluded that vibro-acoustography offers features that may be valuable for diagnostic purposes.

Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Integrating unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we establish correspondence between the VA images and traditional X-ray mammogram by adopting a flexible control-point selection technique for image registration. A modified second-order polynomial, which simply leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the X-ray mammography with a registration error of less than 1.65 mm. The fused image is defined as a linear integration of the VA and X-ray images. Moreover, a color-based fusion technique was employed to integrate the images for better visualization of structural information.

In this study, comb-push ultrasound shear elastography was the method used to investigate the effect of calcifications on shear wave elastography estimates. The study was divided in two parts. The first part included a finite element method (FEM) simulation to observe closely the effect of calcifications on shear-wave elastography. The second part involved two phantom experiments to investigate the changes of shear wave speed in presence of macro and microcalcifications. Results from the phantom studies showed that the presence of large isolated macrocalcifications can introduce areas with apparent high stiffness mainly below the calcification when they are evaluated by shear wave elastography.

In this study, we investigated the effects of single macrocalcifications and groups of microcalcifications on shear wave elastography. Supersonic shear imaging (SSI) and comb-push ultrasound shear elastography (CUSE) were performed on three sets of phantoms to investigate how calcifications of different sizes and distributions influence measured elasticity. Our results demonstrate that the presence of large isolated macrocalcifications and highly concentrated clusters of microcalcifications can introduce areas with apparent high elasticity when they are evaluated by shear wave elastography.

PurposeTo investigate the effects of macrocalcifications and clustered microcalcifications associated with benign breast masses on shear wave elastography (SWE).MethodsSuperSonic Imagine (SSI) and comb-push ultrasound shear elastography (CUSE) were performed on three sets of phantoms to investigate how calcifications of different sizes and distributions influence measured elasticity. To demonstrate the effect in vivo, three female patients with benign breast masses associated with mammographically-identified calcifications were evaluated by CUSE.ResultsApparent maximum elasticity (Emax) estimates resulting from individual macrocalcifications (with diameters of 2mm, 3mm, 5mm, 6mm, 9mm, 11mm, and 15mm) showed values over 50 kPa for all cases, which represents more than 100% increase over background (~21kPa). We considered a 2cm-diameter circular region of interest for all phantom experiments. Mean elasticity (Emean) values varied from 26 kPa to 73 kPa, depending on the macrocalcification size. Highly dense clusters of microcalcifications showed higher Emax values than clusters of microcalcification with low concentrations, but the difference in Emean values was not significant.ConclusionsOur results demonstrate that the presence of large isolated macrocalcifications and highly concentrated clusters of microcalcifications can introduce areas with apparent high elasticity in SWE. Considering that benign breast masses normally have significantly lower elasticity values than malignant tumors, such areas with high elasticity appearing due to presence of calcification in benign breast masses may lead to misdiagnosis.

Clinically, there are two important issues in breast imaging: detection of microcalcifications and identification of mass lesions. X-ray mammography is the main imaging method used for detection of microcalcification, and ultrasound imaging is normally used for detection of mass lesions in breast. Both these methods have limitations that reduce their clinical usefulness. For this reasons, alternative breast imaging modalities are being sought. vibro-acoustography is an imaging modality that has emerged in recent years. This method is based on low-frequency harmonic vibrations induced in the object by the radiation force of ultrasound. This paper describes potential applications of vibro-acoustography for breast imaging and addresses the critical imaging issues such as detection of microcalcifications and mass lesions in breast. Recently, we have developed a vibro-acoustography system for in vivo breast imaging and have tested it on a number of volunteers. Resulting images show soft tissue structures and calcifications within breast with high contrast, high resolution, and no speckles. The results have been verified using X-ray mammography. The encouraging results from in vitro and in vivo experiments suggest that further development of vibro-acoustography technology may lead to a new clinical tool that can be used to detect microcalcifications as well as mass lesions in breast.

Vibro-acoustography has gained interest in the recent years as a new modality for medical imaging. This method is based on low-frequency vibrations induced in the object by the radiation force of ultrasound. This paper focuses on potential applications of vibro-acoustography in breast imaging, including detection of microcalcifications, detection of arterial calcifications, and soft tissue imaging. In addition, we will briefly discuss our recent results of in vivo breast vibro-acoustography. Future developments and potential impact of vibro-acoustography in breast imaging are also discussed.

A novel method for detection and imaging of microcalcifications in breast tissue is presented. The method, called vibro-acoustography, uses the radiation force of ultrasound to vibrate tissue at low (kHz) frequency and utilizes the resulting response to produce images that are related to the hardness of the tissue. The method is tested on human breast tissues. The resulting vibro-acoustographic images are in agreement with corresponding X-ray mammography images of the specimens. The existence of microcalcifications in locations indicated by vibro-acoustography is confirmed by histology. Microcalcifications as small as 110 /spl mu/m in diameter are detected by this method. Resulting vibro-acoustographic images show microcalcifications with high contrast with respect to the background soft tissue. Structures such as dense sclerotic tissue do not seem to interfere with detection of microcalcifications.

Shear wave elastography (SWE) techniques have been introduced and have shown promising potency in providing diagnostic information about thyroid nodules. Comb-push Ultrasound Shear Elastography (CUSE) has been recently introduced as a technique that is capable of measuring mechanical properties of soft tissues with a novel acoustic radiation force excitation fashion, leading to notable advantages of fast acquisition of shear wave speed data within a large field of view. In this study, we demonstrated the preliminary utility of CUSE in imaging thyroid tissue and thyroid nodules in a group of 5 healthy volunteers and 10 patients. The shear wave speed values were converted into Young's modulus (E) and suggested a clear difference between benign (91.2±34.8 kPa) and malignant (173.0±17.1 kPa) thyroid nodules, thus indicating the potential of using CUSE in differentiating between benign and malignant nodules in thyroid.

Two-dimensional (2D) shear wave elastography has emerged as a popular clinical and research tool for a variety of applications. Implementing 2D shear wave elastography on a conventional ultrasound scanner, however, is a challenging task due to the low imaging frame rate, which is essentially limited by the low parallel beamforming capability provided by the hardware beamformers on these scanners. This study developed a time aligned sequential tracking (TAST) technique that enables high pulse repetition frequency (PRF) shear wave tracking on conventional ultrasound scanners. The Comb-push Ultrasound Shear Elastography (CUSE) technique was combined with TAST to realize large field-of-view (FOV) 2D shear wave elastography. CUSE and TAST were implemented on the General Electric LOGIQ E9 (LE9) scanner. Phantom experiments showed that the LE9 could reconstruct large FOV, high quality 2D shear wave speed maps with accurate shear wave speed measurements that are in good agreement to other 2D shear wave imaging techniques. An inclusion phantom study showed that LE9 had comparable performance to the Aixplorer (Supersonic Imagine) in terms of bias and precision in measuring different sized inclusions. Finally, in vivo case studies of a breast with a malignant mass, and a liver from a healthy subject demonstrated the feasibility of using LE9 for in vivo 2D shear wave elastography.

In clinical practice, an overwhelming majority of biopsied thyroid nodules are benign. Therefore, there is a need for a complementary and noninvasive imaging tool to provide clinically relevant diagnostic information about thyroid nodules to reduce the rate of unnecessary biopsies. The goal of this study was to evaluate the feasibility of utilizing comb-push ultrasound shear elastography (CUSE) to measure the mechanical properties (i.e., stiffness) of thyroid nodules and use this information to help classify nodules as benign or malignant. CUSE is a fast and robust 2-D shear elastography technique in which multiple laterally distributed acoustic radiation force beams are utilized simultaneously to produce shear waves. Unlike other shear elasticity imaging modalities, CUSE does not suffer from limited field of view (FOV) due to shear wave attenuation and can provide a large FOV at high frame rates. To evaluate the utility of CUSE in thyroid imaging, a preliminary study was performed on a group of five healthy volunteers and 10 patients with ultrasound-detected thyroid nodules prior to fine needle aspiration biopsy. The measured shear wave speeds in normal thyroid tissue and thyroid nodules were converted to Young's modulus (E), indicating a measure of tissue stiffness. Our results indicate an increase in E for thyroid nodules compared to normal thyroid tissue. This increase was significantly higher in malignant nodules compared to benign. The Young's modulus in normal thyroid tissue, benign and malignant nodules were found to be $23.2 \pm 8.29$ kPa, $91.2\pm 34.8$ kPa, and $173.0\pm 17.1$ kPa, respectively. Results of this study suggest the utility of CUSE in differentiating between benign and malignant thyroid nodules.

The bladder stiffens with age and various pathological processes. We have previously proposed the use of Ultrasound Bladder Vibrometry (UBV) to measure mechanical properties of the bladder wall. In UBV, ultrasound radiation force is focused at the bladder wall to excite impulsive anti-symmetric Lamb waves, and pulse-echo ultrasound is used to track tissue motion. Fourier-space analysis of tissue motion is used to calculate Lamb wave velocity dispersion, i.e. the change of Lamb wave velocity as a function of frequency. The Lamb wave velocity dispersion equation is fit to the dispersion data to estimate bladder viscoelastic parameters. Ex vivo formalin studies aimed at demonstrating the ability of UBV to detect bladder stiffening are demonstrated. UBV measurements of compliant and incompliant neurogenic patient bladders were compared with concomitant Urodynamic Cystometry Studies (UDS), which are the clinical gold standard for evaluation of bladder compliance. Results of UBV and UDS measurements in neurogenic patients were in good agreement. Healthy bladders were more compliant than the neurogenic bladders as their bladder elasticity does not increase significantly over the full range of bladder capacity. The results presented in this study demonstrate the potential of UBV serving as a surrogate to UDS studies.

There is an urgent need for a noninvasive but more accurate method to assess breast masses before referring the patient to biopsy. In this study, we use the slow viscoelastic creep response to evaluate breast masses at low (

Image fusion, or combination of two images obtained by different techniques, has been widely used in medical imaging. Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Combining unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we studied the VA and X-ray image pairs and adopted two methods for fusing the registered VA and X-ray image. In the first method, the fused image is defined as a linear combination of the VA and X-ray images. Moreover, a color-based fusion technique was employed to combine the images for better visualization of structural information. By combining the information from X-ray mammogram and VA modalities into a single image, the accuracy of mammography interpretation which is important for women's health can be increased.

Image registration has been widely used for generating more diagnostic and clinical values in medical imaging. On the other hand, inaccurate image registration and incorrect localization of region of interest risks a potential impact on patients. Vibro-acoustography (VA) is a new imaging modality that has been applied to both medical and industrial imaging. Combining unique diagnostic information of VA with other medical imaging is one of our research interests. In this work, we studied the VA and x-ray image pairs and adopted a flexible control-point selection technique for image registration. A modified second-order polynomial, which leads to a scale/rotation/translation invariant registration, was used. The results of registration were used to spatially transform the breast VA images to map with the x-ray mammography with a registration error of less than 1.65 mm. These two completely different modalities were combined to generate an image including a ratio of each image pixel value. Therefore, the proposed technique allows clinicians to maximize their insight by combining the information from x-ray mammogram and VA modalities into a single image.

Purpose or Objective To evaluate the performance of Comb-push Ultrasound Shear Elastography (CUSE) for classification of breast masses. Materials and Methods CUSE is an ultrasound-based quantitative two-dimensional shear wave elasticity imaging technique, which utilizes multiple laterally distributed acoustic radiation force (ARF) beams to simultaneously excite the tissue and induce shear waves. Female patients who were categorized as having suspicious breast masses underwent CUSE evaluations prior to biopsy. An elasticity estimate within the breast mass was obtained from the CUSE shear wave speed map. Elasticity estimates of various types of benign and malignant masses were compared with biopsy results. Results Fifty-four female patients with suspicious breast masses from our ongoing study are presented. Our cohort included 31 malignant and 23 benign breast masses. Our results indicate that the mean shear wave speed was significantly higher in malignant masses (6 ± 1.58 m/s) in comparison to benign masses (3.65 ± 1.36 m/s). Therefore, the stiffness of the mass quantified by the Young’s modulus is significantly higher in malignant masses. According to the receiver operating characteristic curve (ROC), the optimal cut-off value of 83 kPa yields 87.10% sensitivity, 82.61% specificity, and 0.88 for the area under the curve (AUC). Conclusion CUSE has the potential for clinical utility as a quantitative diagnostic imaging tool adjunct to B-mode ultrasound for differentiation of malignant and benign breast masses.

Vibro-acoustography is an imaging modality that has emerged in recent years. This method is based on low-frequency harmonic vibrations induced in the object by the radiation force of ultrasound. The sound produced due to object vibration is received by an audio hydrophone and the information is mapped into an image. This paper describes application of vibro-acoustography for in vivo breast imaging. Recently, we have developed a vibro-acoustography system for in vivo breast imaging and have tested it on a number of volunteers. These results demonstrate that vibro-acoustography has suitable resolution, contrast, and signal-to-noise ratio such that soft tissue structures, cysts, and a variety of breast abnormalities and masses can be delineated in the image. Lack of image speckle in vibro-acoustography allows one to detect small microcalcifications in the image. The results have been verified using X-ray mammography. The encouraging results from in vivo experiments suggest that further development of vibro-acoustography technology may lead to a new clinical tool for breast imaging applications

The relationship between breast arterial calcification and coronary artery calcification and stenosis is currently an area of active research. It has been suggested in the literature that calcified arteries in the breast may be positively correlated with coronary artery disease. The efficacy of x-ray mammography, the main breast imaging method, is generally reduced in radiologically dense breasts. In a recent study, we have shown that vibro-acoustography (VA), a novel noninvasive imaging technique that is based on the dynamic response of the object to a vibrating force, can detect microcalcifications in breast regardless of breast density. Here, we study application of VA in detecting calcified arteries in breast tissue. Initial mammogram images clearly showed 14 calcified arteries. The corresponding VA images showed all calcified arteries as fragmented linear structures. The VA appearance of the arteries highly correlated with their distinctive x-ray appearances, which allowed us to identify all of the calcified arteries in excised breast tissue. This method may eventually play a role in identifying individuals with an increased risk of coronary artery disease.

X-ray mammography is the principal modality used today for detection of breast microcalcifications and breast lesions associated with breast cancer. X-ray mammography, however, is ionizing and its sensitivity is greatly reduced in dense breasts. Hence, alternative noninvasive and nonionizing breast imaging tools that can aid physicians to better diagnose early-stage breast lesions are of great interest. Vibro-acoustography is a novel noninvasive imaging technique that uses ultrasound in a fundamentally new way. This method uses the radiation force of ultrasound to vibrate the tissue at low (kilohertz) frequency and records the resulting response to produce images that are related to the mechanical properties of the tissue. The goal of this study is to evaluate the performance of vibro-acoustography in detecting breast microcalcifications by conducting vibro-acoustography on 74 fixed breast tissue samples with known microcalcifications based on their radiographs. The results indicate that in most cases micro-calcifications can be detected by vibro-acoustography. Further development of vibro-acoustography may lead to a novel-imaging tool for in vivo detection of microcalcifications.

Objective The relationship between breast arterial calcification and coronary artery calcification and stenosis is currently an area of active research. It has been suggested in the literature that calcified arteries in the breast may be positively correlated with coronary artery disease. The sensitivity of x-ray mammography, the main breast imaging method, is reduced in radiologically dense breasts. In a recent study, we showed that vibro-acoustography, a novel noninvasive imaging technique that is based on the dynamic response of the object to a vibrating force, can detect microcalcifications in the breast regardless of breast density. In this study, we examined the application of vibro-acoustography in detecting calcified arteries in breast tissue. Methods Experiments were conducted on 207 postsurgical excised human breast tissue samples. Tissues specimens were imaged with a high-resolution x-ray mammography unit. Each sample with confirmed arterial calcification was then scanned by the vibro-acoustography system, and the resulting image was compared with the corresponding mammogram. We also studied the histologic characteristics of each sample to positively identify the disease and the presence of arterial calcification. Results Initial mammograms clearly showed 14 calcified arteries. The corresponding vibro-acoustographic images showed all calcified arteries as fragmented linear structures. The vibro-acoustographic appearance of the arteries was highly correlated with their distinctive radiographic appearance, which allowed us to identify all the calcified arteries in the vibro-acoustographic images. Conclusions Vibro-acoustography can be used to detect calcified arteries in excised breast tissue. This method may eventually play a role in identifying individuals with an increased risk of coronary artery disease.

In this work, the results from our Comb Push Ultrasound Shear Elastography (CUSE) assessment of suspicious breast lesions are presented. The elasticity value of the breast lesions are correlated to histopathological findings to evaluate their diagnostic value in differentiating between malignant and benign breast lesions. A total of 44 patients diagnosed with suspicious breast lesions were evaluated using CUSE prior to biopsy. All patient study procedures were conducted according to the protocol approved by Mayo Clinic Institutional Review Board (IRB). Our cohort consisted of 27 malignant and 17 benign breast lesions. The results indicate an increase in shear wave velocity in both benign and malignant lesions compared to normal breast tissue. Furthermore, the Young’s modulus is significantly higher in malignant lesions. An optimal cut-off value of the Young’s modulus ≥ 80 kPa was observed for the receiver operating characteristic (ROC) curve. This is concordant with the published cut-off values of elasticity for suspicious breast lesions. [This work is supported in part by the grant 3R01CA148994-04S1 and 5R01CA148994-04 from NIH.]

Bladder compliance is defined as bladder’s ability to expand in volume without a significant change in pressure. The bladder wall is a multi-layered structure including a muscular layer and fibrous connective tissue. As the percent of connective tissue in the bladder interstitium increases compared to smooth muscle, the bladder becomes more rigid and less capable of expanding during filling. Currently, urodynamic studies (UDS) are considered as the clinical gold standard for bladder compliance assessment. This procedure is invasive and is associated with patient discomfort and is expensive. Ultrasound bladder vibrometry (UBV) is a novel acoustic-radiation-forced-based method for noninvasive assessment of bladder compliance. In UBV, an impulsive acoustic radiation force is focused on the bladder wall (under B-mode ultrasound image guidance) to induce vibrations which may be modeled by as a Lamb wave. High frame rate ultrasound is then utilized to detect the induced waves. This wave motion is then used to estimate the viscoelastic properties and the compliance of the bladder wall. Our results reveal a remarkable agreement between UBV and UDS cystometry measures, suggesting the potential of UBV as a viable clinical tool for the assessment of bladder compliance.

Thyroid cancer is the fastest growing age and gender adjusted cancer in 2011 according the American Cancer Society. The majority of the clinically diagnosed thyroid nodules are benign while less than 5% represent intrathyroidal cancers. Currently, the clinical gold-standard procedure for assessing the thyroid nodules is needle biopsy, a procedure that is associated with significant financial burden as well as pain and risk for patients. Therefore, a noninvasive, affordable, and potentially widely available method to differentiate between benign and malignant thyroid nodules can play an important role in reducing the number of unnecessary biopsies. In this study, we investigate the feasibility of two acoustic radiation force elastography techniques, shear wave dispersion ultrasound vibrometry (SDUV) and comb-push ultrasound shear wave elastography (CUSE imaging), in identifying thyroid nodules (imaging) and differentiating between benign and malignant pathologies based on their elasticity and viscosity (SDUV measurements). Our preliminary results show that the measured shear elasticity and shear viscosity parameters depend on tissue type; hence, these measurements may be utilized to differentiate between healthy normal thyroid tissue, benign nodules, and malignant nodules. Further studies on a large population of patients is required to better evaluate the role of the combination of elasticity and viscosity properties of tissue in differentiating various thyroid nodules.

Objective: This paper introduces an alternative non-invasive and non-ionizing breast imaging tool called vibro-acoustography (VA). This method is based on low-frequency harmonic vibrations induced in the object by the radiation force of ultrasound. Contrast in Vibro-acoustography images represent several properties of the object, including the dynamic characteristics at the audio frequency and scattering and absorption properties at the ultrasound frequency. X-ray mammography and ultrasound are the main imaging modality used today for detection of breast lesions. X-ray mammography, however, is ionizing and its sensitivity is greatly reduced in dense breasts. Ultrasound images suffer from speckles or other or artifact that prevents the physician from delineating the mass lesion from the background tissue. Hence, VA may be used as a stand-alone imaging device or as a tool to provide supplementary information to other existing imaging modalities. Materials & Methods: Matching vibro-acoustography and mammography images of human breast are acquired using a combined mammography/vibro-acoustography machine. Images of various types of breast masses are investigated and their characteristics features are compared. Image features, such as lesion borders, interior texture, and speculation pattern, are studied. Results: Resulting images show soft tissue structures, breast lesions and microcalcifications within breast with high contrast, high resolution, and no speckles. The results have been verified using x-ray mammograms of the breast. Vibro-acoustography images demonstrate that calcified masses normally show sharp borders, benign non-calcified masses have smooth texture, and, in some cases, malignant masses are characterized with spiculation. Conclusion: The encouraging results which are based on the data collected from an ongoing study indicate that the above mentioned image features may be used for differentiation of breast masses and suggest that further development of vibro-acoustography technology may lead to a new clinical tool for various imaging applications. Work supported in part by Grant CA121579-01A1from NIH and Grant BCTR#0504550 from Susan G. Komen Breast Cancer Foundation. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4047. doi:1538-7445.AM2012-4047