Automated 3D ultrasound elastography of the breast: a phantom validation study

Discovering 3D hidden elasticity in isotropic and transversely isotropic materials with physics-informed UNets

Three-dimensional variation in structural components or fiber alignments results in complex mechanical property distribution in tissues and biomaterials. In this paper, we use a physics-informed UNet-based neural network model (El-UNet) to discover the three-dimensional (3D) internal composition and space-dependent material properties of heterogeneous isotropic and transversely isotropic materials without a priori knowledge of the composition. We then show the capabilities of El-UNet by validating against data obtained from finite-element simulations of two soft tissues, namely, brain tissue and articular cartilage, under various loading conditions. We first simulated compressive loading of 3D brain tissue comprising of distinct white matter and gray matter mechanical properties undergoing small strains with isotropic linear elastic behavior, where El-UNet reached mean absolute relative errors under 1.5 % for elastic modulus and Poisson's ratio estimations across the 3D volume. We showed that the 3D solution achieved by El-UNet was superior to relative stiffness mapping by inverse of axial strain and two-dimensional plane stress/plane strain approximations. Additionally, we simulated a transversely isotropic articular cartilage with known fiber orientations undergoing compressive loading, and accurately estimated the spatial distribution of all five material parameters, with mean absolute relative errors under 5 %. Our work demonstrates the application of the computationally efficient physics-informed El-UNet in 3D elasticity imaging and provides methods for translation to experimental 3D characterization of soft tissues and other materials. The proposed El-UNet offers a powerful tool for both in vitro and ex vivo tissue analysis, with potential extensions to in vivo diagnostics. STATEMENT OF SIGNIFICANCE: Elasticity imaging is a technique that reconstructs mechanical properties of tissue using deformation and force measurements. Given the complexity of this reconstruction, most existing methods have mostly focused on 2D problems. Our work is the first implementation of physics-informed UNets to reconstruct three-dimensional material parameter distributions for isotropic and transversely isotropic linear elastic materials by having deformation and force measurements. We comprehensively validate our model using synthetic data generated using finite element models of biological tissues with high bio-fidelity-the brain and articular cartilage. Our method can be implemented in elasticity imaging scenarios for in vitro and ex vivo mechanical characterization of biomaterials and biological tissues, with potential extensions to in vivo diagnostics.

Automated 3-D Ultrasound Elastography of the Breast: An In Vivo Validation Study

OBJECTIVE

Studies have indicated that adding 2-D quasi-static elastography to B-mode ultrasound imaging improved the specificity for malignant lesion detection, as malignant lesions are often stiffer (increased strain ratio) compared with benign lesions. This method is limited by its user dependency and so unsuitable for breast screening. To overcome this limitation, we implemented quasi-static elastography in an automated breast volume scanner (ABVS), which is an operator-independent 3-D ultrasound system and is especially useful for screening women with dense breasts. The study aim was to investigate if 3-D quasi-static elastography implemented in a clinically used ABVS can discriminate between benign and malignant breast lesions.

METHODS

Volumetric breast ultrasound radiofrequency data sets of 82 patients were acquired before and after automated transducer lifting. Lesions were annotated and strain was calculated using an in-house-developed strain algorithm. Two strain ratio types were calculated per lesion: using axial and maximal principal strain (i.e., strain in dominant direction).

RESULTS

Forty-four lesions were detected: 9 carcinomas, 23 cysts and 12 other benign lesions. A significant difference was found between malignant (median: 1.7, range: [1.0-3.2]) and benign (1.0, [0.6-1.9]) using maximal principal strain ratios. Axial strain ratio did not reveal a significant difference between benign (0.6, [-12.7 to 4.9]) and malignant lesions (0.8, [-3.5 to 5.1]).

CONCLUSION

Three-dimensional strain imaging was successfully implemented on a clinically used ABVS to obtain, visualize and analyze in vivo strain images in three dimensions. Results revealed that maximal principal strain ratios are significantly increased in malignant compared with benign lesions.

3D ultrasound strain imaging of puborectal muscle with and without unilateral avulsion

INTRODUCTION AND HYPOTHESIS

The puborectal muscle (PRM), one of the female pelvic floor (PF) muscles, can get damaged during vaginal delivery, leading to disorders such as pelvic organ prolapse. Current diagnosis involves ultrasound (US) imaging of the female PF muscles, but functional information is limited. Previously, we developed a method for strain imaging of the PRM from US images in order to obtain functional information. In this article, we hypothesize that strain in the PRM would differ from intact to the avulsed end.

METHODS

We calculated strain in PRMs at maximum contraction, along their muscle fiber direction, from US images of two groups of women, which consisted of women with intact (n1 = 8) and avulsed PRMs (unilateral) (n2 = 10). Normalized strain ratios between both ends of the PRM (avulsed or intact) and the mid region were calculated. Subsequently, the difference in ratio between the avulsed and intact PRMs was determined.

RESULTS

We observe from the obtained results that the contraction/strain pattern of intact and undamaged PRMs is different from PRMs with unilateral avulsion. Normalized strain ratios between avulsed and intact PRMs were statistically significant (p = 0.04).

CONCLUSION

In this pilot study, we were able to show that US strain imaging of PRMs can show differences between intact PRMs and PRMs with unilateral avulsion.

Tissue Characterization of Puborectalis Muscle From 3-D Ultrasound

Pelvic floor (PF) muscles have the role of preventing pelvic organ descent. The puborectalis muscle (PRM), which is one of the female PF muscles, can be damaged during child delivery. This damage can potentially cause irreversible muscle trauma and even lead to an avulsion, which is disconnection of the muscle from its insertion point, the pubic bone. Ultrasound imaging allows diagnosis of such trauma based on comparison of geometric features of a damaged muscle with the geometric features of a healthy muscle. Although avulsion, which is considered severe damage, can be diagnosed, microdamage within the muscle itself leading to structural changes cannot be diagnosed by visual inspection through imaging only. Therefore, we developed a quantitative ultrasound tissue characterization method to obtain information on the state of the tissue of the PRM and the presence of microdamage in avulsed PRMs. The muscle was segmented as the region of interest (ROI) and further subdivided into six regions of interest (sub-ROIs). Mean echogenicity, entropy and shape parameter of the statistical distribution of gray values were analyzed on two of these sub-ROIs nearest to the bone. The regions nearest to the bones are also the most likely regions to exhibit damage in case of disconnection or avulsion. This analysis was performed for both the muscle at rest and the muscle in contraction. We found that, for PRMs with unilateral avulsion compared with undamaged PRMs, the mean echogenicity (p = 0.02) and shape parameter (p < 0.01) were higher, whereas the entropy was lower (p < 0.01). This method might be applicable to quantification of PRM damage within the muscle.

Evaluation of Different Types of Breast Lesions With Apparent Diffusion Coefficient and Shear Wave Elastography Values: Comparison of Shear Wave Elastography and Apparent Diffusion Coefficient in Breast Lesions

Objective:

The aim of this study was to compare the stiffness of different histological types of breast lesions by obtaining shear wave elastography (SWE) and apparent diffusion coefficient (ADC) values, and to determine the contribution of these two methods to the diagnosis.

Materials and Methods:

In total, 70 patients with biopsy-proven breast lesions were included in the study. The mean SWE values of breast lesions were recorded and ADC values of these lesions were calculated. Receiver operating characteristic (ROC) curve analyses and the diagnostic accuracies of SWE-ADC values were determined.

Results:

The mean SWE values were 45.47 ± 25.11 kPa and 3.51 ± 1.04 m/s in benign group, and 161.11 ± 219.34 kPa and 5.96 ± 1.06 m/s in malignant group, respectively. The mean ADC values were 1.38 ± 0.32 (×10–3 mm2/s) in benign group and 0.96 ± 0.22 (×10–3 mm2/s) in malignant group, respectively. When the diagnostic performances of both imaging modalities on mass stiffness are evaluated, statistically significant negative correlations were found between SWE lesion values and ADC lesion values.

Conclusion:

Evaluation of tissue elasticity has recently been used frequently in the diagnosis of breast diseases. SWE-ADC values, which are negatively correlated in the diagnosis of breast masses, may prove to be a powerful alternative diagnostic tool that can be used interchangeably, as appropriate.

Multicomponent material property characterization of atherosclerotic human carotid arteries through a Bayesian Optimization based inverse finite element approach

OBJECTIVE

Plaque rupture in atherosclerotic carotid arteries is a main cause of ischemic stroke and it is correlated with high plaque stresses. Hence, analyzing stress patterns is essential for plaque specific rupture risk assessment. However, the critical information of the multicomponent material properties of atherosclerotic carotid arteries is still lacking greatly. This work aims to characterize component-wise material properties of atherosclerotic human carotid arteries under (almost) physiological loading conditions.

METHODS

An inverse finite element modeling (iFEM) framework was developed to characterize fibrous intima and vessel wall material properties of 13 cross sections from five carotids. The novel pipeline comprised ex-vivo inflation testing, pre-clinical high frequency ultrasound for deriving plaque deformations, pre-clinical high-magnetic field magnetic resonance imaging, finite element modeling, and a sample efficient machine learning based Bayesian Optimization.

RESULTS

The nonlinear Yeoh constants for the fibrous intima and wall layers were successfully obtained. The optimization scheme of the iFEM reached the global minimum with a mean error of 3.8% in 133 iterations on average. The uniqueness of the results were confirmed with the inverted Gaussian Process (GP) model trained during the iFEM protocol.

CONCLUSION

The developed iFEM approach combined with the inverted GP model successfully predicted component-wise material properties of intact atherosclerotic human carotids ex-vivo under physiological-like loading conditions.

SIGNIFICANCE

We developed a novel iFEM framework for the nonlinear, component-wise material characterization of atherosclerotic arteries and utilized it to obtain human atherosclerotic carotid material properties. The developed iFEM framework has great potential to be advanced for patient-specific in-vivo application.

Pros and Cons for Automated Breast Ultrasound (ABUS): A Narrative Review

Automated breast ultrasound (ABUS) is an ultrasound technique that tends to be increasingly used as a supplementary technique in the evaluation of patients with dense glandular breasts. Patients with dense breasts have an increased risk of developing breast cancer compared to patients with fatty breasts. Furthermore, for this group of patients, mammography has a low sensitivity in detecting breast cancers, especially if it is not associated with architectural distortion or calcifications. ABUS is a standardized examination with many advantages in both screening and diagnostic settings: it increases the detection rate of breast cancer, improves the workflow, and reduces the examination time. On the other hand, like any imaging technique, ABUS has disadvantages and even some limitations. Many disadvantages can be diminished by additional attention and training. Disadvantages regarding image acquisition are the inability to assess the axilla, the vascularization, and the elasticity of a lesion, while concerning the interpretation, the disadvantages are the artifacts due to poor positioning, lack of contact, motion or lesion related. This article reviews and discusses the indications, the advantages, and disadvantages of the method and also the sources of error in the ABUS examination.

3D Cell Culture: Recent Development in Materials with Tunable Stiffness

It is widely accepted that three-dimensional cell culture systems simulate physiological conditions better than traditional 2D systems. Although extracellular matrix components strongly modulate cell behavior, several studies underlined the importance of mechanosensing in the control of different cell functions such as growth, proliferation, differentiation, and migration. Human tissues are characterized by different degrees of stiffness, and various pathologies (e.g., tumor or fibrosis) cause changes in the mechanical properties through the alteration of the extracellular matrix structure. Additionally, these modifications have an impact on disease progression and on therapy response. Hence, the development of platforms whose stiffness could be modulated may improve our knowledge of cell behavior under different mechanical stress stimuli. In this review, we have analyzed the mechanical diversity of healthy and diseased tissues, and we have summarized recently developed materials with a wide range of stiffness.

3D Ultrasound Strain Imaging of Puborectalis Muscle

The female pelvic floor (PF) muscles provide support to the pelvic organs. During delivery, some of these muscles have to stretch up to three times their original length to allow passage of the baby, leading frequently to damage and consequently later-life PF dysfunction (PFD). Three-dimensional (3D) ultrasound (US) imaging can be used to image these muscles and to diagnose the damage by assessing quantitative, geometric and functional information of the muscles through strain imaging. In this study we developed 3D US strain imaging of the PF muscles and explored its application to the puborectalis muscle (PRM), which is one of the major PF muscles.

Optimization of transmission and reconstruction parameters in angular displacement compounding using plane wave ultrasound

In ultrasound elastography, plane-wave acquisitions and angular displacement compounding (ADC) are often used and combined to allow high frame rates and to improve accuracy of lateral displacement estimates, respectively. This study investigates the performance of displacement and strain estimation for ADC as a function of; the main-to-grating-lobe-amplitude ratio which decreases as a function of steering angle; plane-wave acquisition and Delay-and-Sum (DaS)-related parameters; and grating-lobe filter cut-off frequency. Three experiments were conducted with a block phantom to test ADC performance for displacement fields of varying complexity: a lateral transducer shift, phantom rotation and phantom deformation. Experiments were repeated for four linear array transducers (pitch-to-lambda ratios between 0.6 and 1.4). Best ADC performance was found for steering angles that resulted in a theoretically derived main-to-grating-lobe-amplitude ratio of 1.7 dB for pure lateral translation and 6 dB for predominately lateral strain or rotation. Temporal filtering to reduce grating lobe signal or shifting of the receive aperture to receive angles below or above the optimal angle, as dictated by the main-to-grating-lobe-amplitude ratio, did not improve results. The accuracy of lateral displacement and strain estimates was improved by apodization in transmission and a dedicated F-number in DaS (0.75) allowing incidence angles within ± 33° in the active aperture. ADC with the optimized settings as found in this study improves the accuracy of displacements and strain estimates up to 80.7% compared to non-ADC. Compared to ADC settings described in current literature, our optimization improved the accuracy by 11.9% to 75.3% for lateral displacement and strain, and by 89.3% to 96.2% for rotation. The accuracy of ADC in rotation seemed to depend highly on plane-wave and DaS-related parameters which may explain the major improvement compared to settings in current literature. The overall improvement by optimized ADC was statistically significant compared to non-ADC (p = 0.003) and literature (p = 0.002).

Physics-guided machine learning for 3-D quantitative quasi-static elasticity imaging

We present a 3D extension of the Autoprogressive Method (AutoP) for quantitative quasi-static ultrasonic elastography (QUSE) based on sparse sampling of force-displacement measurements. Compared to current model-based inverse methods, our approach requires neither geometric nor constitutive model assumptions. We build upon our previous report for 2D QUSE and demonstrate the feasibility of recovering the 3D linear-elastic material property distribution of gelatin phantoms under compressive loads. Measurements of boundary geometry, applied surface forces, and axial displacements enter into AutoP where a Cartesian neural network constitutive model (CaNNCM) interacts with finite element analyses to learn physically consistent material properties with no prior constitutive model assumption. We introduce a new regularization term uniquely suited to AutoP that improves the ability of CaNNCMs to extract information about spatial stress distributions from measurement data. Results of our study demonstrate that acquiring multiple sets of force-displacement measurements by moving the US probe to different locations on the phantom surface not only provides AutoP with the necessary information for a CaNNCM to learn the 3D material property distribution, but may significantly improve the accuracy of the Young's modulus estimates. Furthermore, we investigate the trade-offs of decreasing the contact area between the US transducer and phantom surface in an effort to increase sensitivity to surface force variations without additional instrumentation. Each of these modifications improves the ability of CaNNCMs trained in AutoP to learn the spatial distribution of Young's modulus from force-displacement measurements.

An Improved Region-Growing Motion Tracking Method Using More Prior Information for 3-D Ultrasound Elastography

Three-dimensional (3-D) ultrasound elastography can provide 3-D tissue stiffness information that may be used during clinical diagnoses. In the framework of strain elastography, motion tracking plays an important role. In this study, an improved 3-D region-growing motion tracking (RGMT) algorithm based on a concept of exterior boundary points was developed. In principle, the proposed method first determines displacement at some seed points by strictly checking the local correlation and continuity in the neighborhood of those seeds. Subsequent displacement estimation is then conducted from these initial seeds to obtain displacements associated with other locations. This RGMT algorithm is designed to use more known information-including displacements and correlation values of all known-displacement neighboring points-to estimate the displacement of an unknown-displacement point, whereas previous RGMT methods employed information from only one such point. The algorithm was tested on 3-D ultrasound volumetric data acquired from a simulation, a tissue-mimicking phantom, and five human subjects. Motion-compensated cross correlations (MCCCs), strain contrast, and displacement Laplacian values (representing smoothness of an estimated displacement field) were calculated and used to evaluate the merits of the proposed RGMT method. Compared with a previously published RGMT method, the results show that the proposed RGMT method can provide smaller displacement errors and smoother displacements and improve strain contrast while maintaining reasonably high MCCC values, indicating good motion tracking quality. The proposed method is also computationally more efficient. In summary, our preliminary results demonstrated that the proposed RGMT algorithm is capable of obtaining high-quality 3-D strain elastographic data using modified clinical equipment.

3D normalized cross-correlation for estimation of the displacement field in ultrasound elastography

This paper introduces a novel technique to estimate tissue displacement in quasi-static elastography. A major challenge in elastography is estimation of displacement (also referred to time-delay estimation) between pre-compressed and post-compressed ultrasound data. Maximizing normalized cross correlation (NCC) of ultrasound radio-frequency (RF) data of the pre- and post-compressed images is a popular technique for strain estimation due to its simplicity and computational efficiency. Several papers have been published to increase the accuracy and quality of displacement estimation based on NCC. All of these methods use 2D spatial windows in RF data to estimate NCC, wherein displacement is assumed to be constant within each window. In this work, we extend this assumption along the third dimension. Two approaches are proposed to get third dimension. In the first approach, we use temporal domain to exploit neighboring samples in both spatial and temporal directions. Considering temporal information is important since traditional and ultrafast ultrasound machines are, respectively, capable of imaging at more than 30 frame per second (fps) and 1000 fps. Another approach is to use time-delayed pre-beam formed data (channel data) instead of RF data. In this method information of all channels that are recorded as pre-beam formed data of each RF line will be considered as 3^rd dimension. We call these methods as spatial temporal normalized cross correlation (STNCC) and channel data normalized cross correlation (CNCC) and show that they substantially outperforms NCC using simulation, phantom and in-vivo experiments. Given substantial improvements of results in addition to the relative simplicity of implementing STNCC and CNCC, the proposed approaches can potentially have a large impact in both academic and commercial work on ultrasound elastography.

Combining Total Variation Regularization with Window-Based Time Delay Estimation in Ultrasound Elastography

A major challenge of free-hand palpation ultrasound elastography (USE) is estimating the displacement of RF samples between pre- and post-compressed RF data. The problem of displacement estimation is ill-posed since the displacement of one sample by itself cannot be uniquely calculated. To resolve this problem, two categories of methods have emerged. The first category assumes that the displacement of samples within a small window surrounding the reference sample is constant. The second class imposes smoothness regularization and optimizes an energy function. Herein, we propose a novel method that combines both approaches, and as such, is more robust to noise. The second contribution of this work is the introduction of the L1 norm as the regularization term in our cost function, which is often referred to as the total variation (TV) regularization. Compared to previous work that used the L2 norm regularization, optimization of the new cost function is more challenging. However, the advantages of using the L1 norm are twofold. First, it leads to substantial improvement in the sharpness of displacement estimates. Second, to optimize the cost function with the L1 norm regularization, we use an iterative method that further increases the robustness. We name our proposed method tOtal Variation Regularization and WINDow-based time delay estimation (OVERWIND) and show that it is robust to signal decorrelation and generates sharp displacement and strain maps for simulated, experimental phantom and in-vivo data. In particular, OVERWIND improves strain contrast-to-noise ratio (CNR) by 27.26%, 144.05%, and 49.90% on average in simulation, phantom, and in-vivo data, respectively, compared to our recent Global Ultrasound Elastography (GLUE) method.

Future of breast elastography

Both strain elastography and shear wave elastography have been shown to have high sensitivity and specificity for characterizing breast lesions as benign or malignant. Training is important for both strain and shear wave elastography. The unique feature of benign lesions measuring smaller on elastography than B-mode imaging and malignant lesions appearing larger on elastography is an important feature for characterization of breast masses. There are several artifacts which can contain diagnostic information or alert to technique problems. Both strain and shear wave elastography continue to have improvements and new techniques will soon be available for clinical use that may provide additional diagnostic information. This paper reviews the present state of breast elastography and discusses future techniques that are not yet in clinical practice.

Electromagnetic tracking-based freehand 3D quasi-static elastography with 1D linear array: a phantom study

Recent developments in hardware and scanning protocols have advanced conventional 2D quasi-static elastography to 3D level, which provides an intuitive visualization of lesions. A 2D linear array or scanning mechanism is typically required for 3D quasi-static elastography, requiring expensive and specifically designed hardware. In this study, we propose a novel method based on a commercial electromagnetic tracking system for freehand 3D quasi-static elastography with 1D linear array. Phantom experiments are performed to validate the feasibility of the proposed method. During data acquisition, the probe contacts the surface of an elasticity phantom and moves in the elevational direction, while applying sinusoidal-like axial compression to the phantom. For each frame of ultrasound data, the 3D coordinates and orientations of the probe are obtained from an electromagnetic tracking system. A correlation-based algorithm is adopted to obtain a series of axial strain images. Volumetric strain data are reconstructed by using the recorded 3D coordinates and orientations of the probe corresponding to each strain image. The diameters of inclusions are then obtained from the slice plots of the volumetric strain data. The volumes of inclusions are estimated from the isosurface plots. The experimental result shows that the volume estimation of the inclusions has good accuracy, with errors within 2%, while the diameters of the inclusions estimated from three orthogonal planes have larger errors up to 18%. In conclusion, the present framework would promise a reliable and effective solution for freehand 3D quasi-static elastography with 1D linear array.

Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use

Automated breast ultrasonography (ABUS) is a new imaging technology for automatic breast scanning through ultrasound. It was first developed to overcome the limitation of operator dependency and lack of standardization and reproducibility of handheld ultrasound. ABUS provides a three-dimensional representation of breast tissue and allows images reformatting in three planes, and the generated coronal plane has been suggested to improve diagnostic accuracy. This technique has been first used in the screening setting to improve breast cancer detection, especially in mammographically dense breasts. In recent years, numerous studies also evaluated its use in the diagnostic setting: they showed its suitability for breast cancer staging, evaluation of tumor response to neoadjuvant chemotherapy, and second-look ultrasound after magnetic resonance imaging. The purpose of this article is to provide a comprehensive review of the current body of literature about the clinical performance of ABUS, summarize available evidence, and identify gaps in knowledge for future research.

3-D Single Breath-Hold Shear Strain Estimation for Improved Breast Lesion Detection and Classification in Automated Volumetric Ultrasound Scanners

Automated breast volume scanner (ABVS) is an ultrasound imaging modality used in breast cancer screening. It has high sensitivity but limited specificity as it is hard to discriminate between benign and malignant lesions by echogenic properties. Specificity might be improved by shear strain imaging as malignant lesions, firmly bonded to its host tissue, show different shear patterns compared to benign lesions, often loosely bonded. Therefore, 3-D quasi-static elastography was implemented in an ABVS-like system. Plane wave instead of conventional focused transmissions were used to reduce scan times within a single breath hold. A 3-D strain tensor was obtained and shear strains were reconstructed in phantoms containing firmly and loosely bonded lesions. Experiments were also simulated in finite-element models (FEMs). Experimental results, confirmed by FEM-results, indicated that loosely bonded lesions showed increased maximal shear strains (~2.5%) and different shear patterns compared to firmly bonded lesions (~0.9%). To conclude, we successfully implemented 3-D elastography in an ABVS-like system to assess lesion bonding by shear strain imaging.

Factors Affecting the Quality of Breast Quasistatic Ultrasound Elastograms

OBJECTIVES

To explore factors affecting the quality of quasistatic ultrasound elastograms of the breast and to evaluate their accuracy in distinguishing benign and malignant breast lesions.

METHODS

A total of 663 patients with 702 breast lesions were recruited. All patients received both conventional ultrasonography and quasistatic elastography. Patients' breast lesions were divided into A and B groups according to satisfactory and unsatisfactory elastographic examinations. Group A included 590 satisfactory elastograms, whereas Group B included 112 unsatisfactory elastograms. Various factors-maximum depth of the lesion, maximum transverse diameter of the lesion, thickness of the adipose layer, thickness of the glandular layer, thickness of the breast, distance between the nipple and lesion, age, body mass index, and menopausal status-were analyzed and compared between the groups to gauge their effects on the quality of the elastograms.

RESULTS

Significantly deeper lesions, higher maximum transverse lesion diameters, thicker adipose layers, thicker glandular layers, and thicker breasts were identified in group B patients compared to group A patients (P < .05). Multivariate logistic regression analyses showed that the maximum depth of the lesion, thickness of the adipose layer, and thickness of the breast were independent factors in the quality of elastograms. The area under the curve for the maximum depth of the lesion was 0.986 with the optimal cutoff threshold of 2.5 cm.

CONCLUSIONS

Quasistatic elastography can be a supplementary approach to conventional ultrasonography in helping improve the diagnostic accuracy of breast lesions. The depth and size of breast lesions are correlated with the quality of elastograms.

Specific Ultrasound Data Acquisition for Tissue Motion and Strain Estimation: Initial Results

Ultrasound applications such as elastography can benefit from 3-D data acquisition and processing. In this article, we describe a specific ultrasound probe, designed to acquire series of three adjacent imaging planes over time. This data acquisition makes it possible to consider the out-of-plane motion that can occur at the central plane during medium scanning, and is proposed with the aim of improving the results of strain imaging. In this first study, experiments were conducted on phantoms, and controlled axial and elevational displacements were applied to the probe using a motorized system. Radiofrequency ultrasound data were acquired at a 40-MHz sampling frequency with an Ultrasonix ultrasound scanner, and processed using a 3-D motion estimation method. For each of the 2-D regions of interest of the central plane in pre-compression data, a 3-D search was run to determine its corresponding version in post-compression data, with this search taking into account the region-of-interest deformation model chosen. The results obtained with the proposed ultrasound data acquisition and strain estimation were compared with results from a classic approach and illustrate the improvement produced by considering the medium's local displacements in elevation, with notably an increase in the mean correlation coefficients achieved.