Quasi-static Elastography Research Articles

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

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

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. 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). 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]). 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.

Regularization-Based 2D Strain Tensor Imaging in Quasi-Static Ultrasound Elastography SAGE Publications.

Accurately estimating all strain components in quasi-static ultrasound elastography is crucial for the full analysis of biological media. In this study, 2D strain tensor imaging was investigated, focusing on the use of a regularization method to improve strain images. This method enforces the tissue property of (quasi-) incompressibility, while penalizing strong field variations, to smooth the displacement fields and reduce the noise in the strain components. The performance of the method was assessed with numerical simulations, phantoms, and in vivo breast tissues. For all the media examined, the results showed a significant improvement in both lateral displacement and strain, while axial fields were only slightly modified by the regularization. The introduction of penalty terms allowed us to obtain shear strain and rotation elastograms where the patterns around the inclusions/lesions were clearly visible. In phantom cases, the findings were consistent with the results obtained from the modeling of the experiments. Finally, the easier detectability of the inclusions/lesions in the final lateral strain images was associated with higher elastographic contrast-to-noise ratios (CNRs), with values in the range of [0.54-9.57] versus [0.08-0.38] before regularization.

Reconstructing the Spatial Distribution of the Relative Shear Modulus in Quasi-static Ultrasound Elastography: Plane Stress Analysis.

Quasi-static ultrasound elastography (QSUE) is an imaging technique that mainly provides axial strain maps of tissues when the latter are subjected to compression. In this article, a method for reconstructing the relative shear modulus distribution within a linear elastic and isotropic medium, in QSUE, is introduced. More specifically, the plane stress inverse problem is considered. The proposed method is based on the variational formulation of the equilibrium equations and on the choice of adapted discretization spaces, and only requires displacement fields in the analyzed media to be determined. Results from plane stress and 3-D numerical simulations, as well as from phantom experiments, showed that the method is able to reconstruct the different regions within a medium, with shear modulus contrasts that unambiguously reveal whether inclusions are stiffer or softer than the surrounding material. More specifically, for the plane stress simulations, inclusion-to-background modulus ratios were found to be very accurately estimated, with an error lower than 3%. For the 3-D simulations, for which the plane stress conditions are no longer satisfied, these ratios were, as expected, less accurate, with an error that remained lower than 10% for two of the three cases analyzed but was around 34% for the last case. Concerning the phantom experiments, a comparison with a shear wave elastography technique from a clinical ultrasound scanner was also made. Overall, the inclusion-to-background shear modulus ratios obtained with our approach were found to be closer to those given by the phantom manufacturer than the ratios provided by the clinical system.

“Fast” Solution of the Three-Dimensional Inverse Problem of Quasi-Static Elastography with the Help of the Small Parameter Method

“Fast” Solution of the Three-Dimensional Inverse Problem of Quasi-Static Elastography with the Help of the Small Parameter Method

A novel tissue mechanics-based method for improved motion tracking in quasi-static ultrasound elastography.

Most cancers are associated with biological and structural changes that lead to tissue stiffening. Therefore, imaging tissue stiffness using quasi-static ultrasound elastography (USE) can potentially be effective in cancer diagnosis. USE techniques developed for stiffness image reconstruction use noisy displacement data to obtain the stiffness images. In this study, we propose a technique to substantially improve the accuracy of the displacement data computed through ultrasound tissue motion tracking techniques, especially in the lateral direction. The proposed technique uses mathematical constraints derived from fundamental tissue mechanics principles to regularize displacement and strain fields obtained using Global Ultrasound Elastography (GLUE) and Second-Order Ultrasound Elastography (SOUL) methods. The principles include a novel technique to enforce (1) tissue incompressibility using 3D Boussinesq model and (2) deformation compatibility using the compatibility differential equation. The technique was validated thoroughly using metrics pertaining to Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR) and Normalized Cross Correlation (NCC) for four tissue-mimicking phantom models and two clinical breast ultrasound elastography cases. The results show substantial improvement in the displacement and strain images generated using the proposed technique. The tissue-mimicking phantom study results indicate that the proposed method is superior in improving image quality compared to the GLUE and SOUL techniques as it shows an average axial strain SNR and CNR improvement of 44% and 63%, and lateral strain SNR and CNR improvement of 130% and 435%, respectively. The results of the phantom study also indicate higher accuracy of displacement images obtained using the proposed technique, including improvement ranges of 7-84% and 26-140% for axial and lateral displacement images, respectively. For the clinical cases, the results indicate average improvement of 48% and 64% in SNR and CNR, respectively, in the axial strain images, and average improvement of 40% and 41% in SNR and CNR, respectively, in the lateral strain images. The proposed method is very effective in producing improved estimate of tissue displacement and strain images, especially with the lateral displacement and strain where the improvement is highly remarkable. While the method shows promise for clinical applications, further investigation is necessary for rigorous assessment of the method's performance in the clinic.

Quasi-static ultrasound elastography of ex-vivo porcine vocal folds during passive elongation and adduction

Objectives. The elastic properties of the vocal folds have great influence on the primary sound and thus on the entire subsequent phonation process. Muscle contractions in the larynx can alter the elastic properties of the vocal fold tissue. Quasi-static ultrasound elastography is a non-destructive examination method that can be applied to ex-vivo vocal folds. In this work, porcine vocal folds were passively elongated and adducted and the changes of the elastic properties due to that manipulations were measured. Methods. Manipulations were performed by applying force to sewn-in sutures. Elongation was achieved by a suture attached to the thyroid cartilage, which was pulled forward by defined weights. Adduction was effected by two sutures exerting torque on the arytenoid cartilage. A series of ten specimens was examined and evaluated using a quasi-static elastography algorithm. In addition, the surface stretch was measured optically using tattooed reference points. Results. This study showed that the expected stiffening of the tissue during the manipulations can be measured using quasi-static ultrasound elastography. The measured effect of elongation and adduction, both of which result in stretching of the tissue, is stiffening. However, the relative change of specific manipulations is not the same for the same load on different larynges, but is rather related to stretch caused and other uninvestigated factors. Conclusion. The passive elongation and adduction of vocal folds stiffen the tissue of the vocal folds and can be measured using ultrasound elastography.

A new three-dimensional elastography using phase based shifted Fourier transform

Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues. The aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform (SFT) to perform a quasi-static elastography. Our innovative idea is implementation of a 3D convolution instead of using three 2D convulsions. At first, we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor tissue. The primary raw data consists of a number of points with x, y and z specified for tumor and healthy breast tissue. At this step, we simulated the displacements in directions of x, y and z at each point of the prescribed tissues for 15 ​mm displacement of probe in –Y direction then we collected 1831 points for tumor and 4186 points for breast before and after pressure. After applying a novel reconstruction algorithm, we convolved all images with the 3D Gabor filters to obtain phases, represented displacements of the breast and tumor images for before and after pressure. To reach this goal, we designed a Gabor filter bank based on the dimensions of the input images in different scales, directions, and deviations. Using the 3D SFT, we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the images. Finally, we implemented a 2D analysis of SFT in order to investigate validation of the 3D SFT. In 2D algorithm, we used three two-dimensional convulsions in XY, YZ and XZ planes. The results obtained from the small displacements marked by circles, confirmed the accuracy of the 3D SFT algorithm. These areas of interest are the tumor areas in the 2D analysis.

Deep learning in ultrasound elastography imaging: A review.

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayered perceptron, convolutional neural network, and recurrent neural network, are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.

An Enhanced Method for Full-Inversion-Based Ultrasound Elastography of the Liver.

Similar to many other types of cancer, liver cancer is associated with biological changes that lead to tissue stiffening. An effective imaging technique that can be used for liver cancer detection through visualizing tissue stiffness is ultrasound elastography. In this paper, we show the effectiveness of an enhanced method of quasi-static ultrasound elastography for liver cancer assessment. The method utilizes initial estimates of axial and lateral displacement fields obtained using conventional time delay estimation (TDE) methods in conjunction with a recently proposed strain refinement algorithm to generate enhanced versions of the axial and lateral strain images. Another primary objective of this work is to investigate the sensitivity of the proposed method to the quality of these initial displacement estimates. The strain refinement algorithm is founded on the tissue mechanics principles of incompressibility and strain compatibility. Tissue strain images can serve as input for full-inversion-based elasticity image reconstruction algorithm. In this work, we use strain images generated by the proposed method with an iterative elasticity reconstruction algorithm. Ultrasound RF data collected from a tissue-mimicking phantom and in-vivo data of a liver cancer patient were used to evaluate the proposed method. Results show that while there is some sensitivity to the displacement field initial estimates, overall, the proposed method is robust to the quality of the initial estimates. Clinical Relevance- Improved elasticity images of the liver can aid in achieving more reliable diagnosis of liver cancer.

A Non-invasive Method to Estimate the Stress-Strain Curve of Soft Tissue Using Ultrasound Elastography.

Ultrasound elastography performed under small strain conditions has been intensively studied. However, small deformations may be not sufficiently large to differentiate some abnormal tissues. By combining quasi-static and shear wave elastography, we developed a non-invasive method to estimate the localized stress- strain curve of materials. This method exerts progressive multistep uniaxial compression on the materials, and shear wave measurements were performed at every compression step. This method estimates the 2-D displacements between steps via a 2-D region growing motion tracking method and accumulates these displacements to obtain the large material displacements with respect to the initial configuration. At each step, the shear modulus and stress were calculated according to linear elastic theory. The proposed method was tested on custom-made tissue-mimicking phantoms. Mechanical compression testing was conducted on the samples made of the same material as the phantoms and taken as the reference. The stress-strain curves for the same material from the proposed method and from mechanical testing are in good agreement. The root mean square error (RMSE) and area percentage error (APE) of the stress-strain curve between ultrasound measurement and mechanical testing for soft materials ranged from 0.18 to 0.26 kPa and from 5.6% to 7.8%, respectively. The RMSE and APE for stiff materials ranged from 0.56 to 1.17 kPa and 8.0% to 17.9%. Therefore, our method was able to provide good estimates of the stress-strain curve for tissue-mimicking materials.

Solution of the Two-Dimensional Inverse Problem of Quasistatic Elastography with the Help of the Small Parameter Method

Solution of the Two-Dimensional Inverse Problem of Quasistatic Elastography with the Help of the Small Parameter Method

Does static stretching change uniformly the quadriceps elasticity in physically actives subjects?

Verify the acute responses of static stretching (SS) on the rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL) elasticity and knee/hiprange of motion (ROM). Additionally, to investigate if there are consistency among quadriceps muscle elasticity after SS. Acute effect of SS on pre-post-intervention design. Thirtheen healthy participants (both genders) proposed for a pre-post experimental design. RF, VM, and VL elasticity (strain ratio, SR) was evaluated bilaterally by ultrasound with quasi-static elastography. Higher SR values refer to more rigid tissues. A SS protocol of 3 series of 30s was applied at right lower limb. The left lower limb was considered as control group. Also, photogrammetry evaluated the knee/hip ROM. For SR muscle comparisons, VM was lower (less stiff) than VL and RF at pre- and post-SS. For time comparisons, no differences were observed for SR and ROM at pre- and post-SS. However, the effect size of the quadriceps SR at SS lower limb was higher than control. Acute effects of SS did not change the quadriceps SR or knee/hip ROM in healthy and active subjects. Non-uniform quadriceps SR are observed (VM < VL and RF) independently of SS. Future studies should consider different protocols, muscles, and populations.

Hadamard-Encoded Synthetic Transmit Aperture Imaging for Improved Lateral Motion Estimation in Ultrasound Elastography.

Lateral motion estimation has been a challenge in ultrasound elastography mainly due to the low resolution, low sampling frequency, and lack of phase information in the lateral direction. Synthetic transmit aperture (STA) can achieve high resolution due to two-way focusing and can beamform high-density image lines for improved lateral motion estimation with the disadvantages of low signal-to-noise ratio (SNR) and limited penetration depth. In this study, Hadamard-encoded STA (Hadamard-STA) is proposed for the improvement of lateral motion estimation in elastography, and it is compared with STA and conventional focused wave (CFW) imaging. Simulations, phantom, and in vivo experiments were conducted to make the comparison. The normalized root mean square error (NRMSE) and the contrast-to-noise ratio (CNR) were calculated as the evaluation criteria in the simulations. The results show that, at a noise level of -10 dB and an applied strain of -1% (compression), Hadamard-STA decreases the NRMSEs of lateral displacements by 46.92% and 35.35%, decreases the NRMSEs of lateral strains by 52.34% and 39.75%, and increases the CNRs by 9.70 and 9.75 dB compared with STA and CFW, respectively. In the phantom experiments performed on a heterogeneous tissue-mimicking phantom, the sum of squared differences (SSD) between the reference and the motion-compensated RF data, and the CNR were calculated as the evaluation criteria. At an applied strain of -1.80%, Hadamard-STA is found to decrease the SSDs by 20.91% and 30.99% and increase the CNRs by 14.15 and 24.66 dB compared with STA and CFW, respectively. In the experiments performed on a breast phantom, Hadamard-STA achieves better visualization of the breast inclusion with a clearer boundary between the inclusion and the background than STA and CFW. The in vivo experiments were performed on a patient with a breast tumor, and the tumor could also be better visualized with a more homogeneous background in the strain image obtained by Hadamard-STA than by STA and CFW. These results demonstrate that Hadamard-STA achieves a substantial improvement in lateral motion estimation and maybe a competitive method for quasi-static elastography.

Second-Order Ultrasound Elastography With L1-Norm Spatial Regularization.

Time delay estimation (TDE) between two radio-frequency (RF) frames is one of the major steps of quasi-static ultrasound elastography, which detects tissue pathology by estimating its mechanical properties. Regularized optimization-based techniques, a prominent class of TDE algorithms, optimize a nonlinear energy functional consisting of data constancy and spatial continuity constraints to obtain the displacement and strain maps between the time-series frames under consideration. The existing optimization-based TDE methods often consider the L2 -norm of displacement derivatives to construct the regularizer. However, such a formulation over-penalizes the displacement irregularity and poses two major issues to the estimated strain field. First, the boundaries between different tissues are blurred. Second, the visual contrast between the target and the background is suboptimal. To resolve these issues, herein, we propose a novel TDE algorithm where instead of L2 -, L1 -norms of both first- and second-order displacement derivatives are taken into account to devise the continuity functional. We handle the non-differentiability of L1 -norm by smoothing the absolute value function's sharp corner and optimize the resulting cost function in an iterative manner. We call our technique Second-Order Ultrasound eLastography (SOUL) with the L1 -norm spatial regularization ( L1 -SOUL). In terms of both sharpness and visual contrast, L1 -SOUL substantially outperforms GLobal Ultrasound Elastography (GLUE), tOtal Variation rEgulaRization and WINDow-based time delay estimation (OVERWIND), and SOUL, three recently published TDE algorithms in all validation experiments performed in this study. In cases of simulated, phantom, and in vivo datasets, respectively, L1 -SOUL achieves 67.8%, 46.81%, and 117.35% improvements of contrast-to-noise ratio (CNR) over SOUL. The L1 -SOUL code can be downloaded from http://code.sonography.ai.

Physical Principles of Elastography: A Primer for Radiologists

AbstractElastography is the noninvasive method of qualitative and quantitative evaluation of strain and elastic modulus distribution in soft tissues. In simpler terms, elastography is the science of measuring tissue stiffness, the deviation of which correlates with pathology of the tissue/organs being evaluated. Whereas, elasticity, refers to the property of solid matter to return to their original shape and size after removal of the deforming forces. In all forms of elastography, irrespective of the types of deforming forces or moduli, the deformation of tissue occurs in the form of shear deformation. The velocity of shear waves in the deformed tissue depends on its density and on the shear modulus. The direction of propagation of shear wave is perpendicular to the inciting mechanical or acoustic wave. The shear wave is then subsequently tracked using multiple tracking pulses, which measures tissue displacement in response to the passing shear wave. The calculated speed of the shear wave is then converted to conventional Young's modulus for the purpose of computing the tissue stiffness.The currently used elastography techniques are static or quasi-static elastography and dynamic elastography. Strain elastography (a form of static or quasi-static elastography) is based on the principle of acquisition of radio-frequency (RF) signals before and after the application of a deforming force in the form of slight compression of tissue by a transducer. RF signals are compared between the pre-compression image data set and the post-compression image data set and correlated between the two data sets.Dynamic elastography may be either ultrasound (US) based or magnetic resonance (MR) based. The types of dynamic US elastography are: acoustic radiation force impulse imaging (ARFI), transient elastography (TE), point shear wave elastography (pSWE), and shear wave elastography (SWE). ARFI uses a standard transducer to produce and propagate rapid bursts of long focused ultrasound pulses, also called as “push pulses” which cause tissue deformity, the propagation of which is tracked using radio-frequency echo tracking. In TE, a probe mounted on a vibrator is used to produce a small thump by piston like motion of transducer. The shear wave which arises from the edges of the transducer is tracked using high pulse repetition frequency tissue Doppler and computed using M-mode for display of quantitative parameters. Point shear wave, also known as quantitative ARFI, uses shear waves generated using transient tissue displacement caused by ARFI and are subsequently subjected to tracking by Doppler. Shear wave elastography is based on the principles of imaging shear wave speed. An acoustic radiation force impulse is transmitted along the acoustic axis to produce tissue displacement and deformation at points of acoustic axis. The generated shear wave is imaged using RF echo tracking over a grid of points, which is translated into a real time image. MR elastography is a dynamic technique and the basic principles of MR elastography are the same as other forms of dynamic elastography. MR elastography has limited utility in iron-overload states and in addition, due to the large amount of time required for acquisition, the technique is not suitable for unstable patients.This review presents a simplified summary of the principles of elastography along with definition of the terms and the types of elastography which are currently available to radiologists for clinical application and concludes with a brief on the newer developments for the future.

Second-Generation Dual Scan Mammoscope With Photoacoustic, Ultrasound, and Elastographic Imaging Capabilities.

We recently developed the photoacoustic dual-scan mammoscope (DSM), a system that images the patient in standing pose analog to X-ray mammography. The system simultaneously acquires three-dimensional photoacoustic and ultrasound (US) images of the mildly compressed breast. Here, we describe a second-generation DSM (DSM-2) system that offers a larger field of view, better system stability, higher ultrasound imaging quality, and the ability to quantify tissue mechanical properties. In the new system, we doubled the field of view through laterally shifted round-trip scanning. This new design allows coverage of the entire breast tissue. We also adapted precisely machined holders for the transducer-fiber bundle sets. The new holder increased the mechanical stability and facilitated image registration from the top and bottom scanners. The quality of the US image is improved by increasing the firing voltage and the number of firing angles. Finally, we incorporated quasi-static ultrasound elastography to allow comprehensive characterization of breast tissue. The performance of the new system was demonstrated through in vivo human imaging experiments. The experimental results confirmed the capability of the DSM-2 system as a powerful tool for breast imaging.

Enabling quantitative robot-assisted compressional elastography via the extended Kalman filter

Compressional or quasi-static elastography has demonstrated the capability to detect occult cancers in a variety of tissue types, however it has a serious limitation in that the resulting elastograms are generally qualitative whereas other forms of elastography, such as shear-wave, can produce absolute measures of elasticity for histopathological classification. We address this limitation by introducing a stochastic method using an extended Kalman filter and robot-assistance to obtain quantitative elastograms which are resilient to measurement noise and system uncertainty. In this paper, the probabilistic framework is described, which utilizes many ultrasound acquisitions obtained from multiple palpations, to fuse data and uncertainty from a robotic manipulator’s joint encoders and force/torque sensor directly into the inverse reconstruction of the elastogram. Quantitative results are demonstrated over homogeneous and inclusion gelatin phantoms using a seven degree of freedom manipulator for a range of initial elasticity assumptions. Results imply resilience to poorly assumed initial conditions as all trials were within 5 kPa of the elasticity measured by a mechanical testing system. Moreover, the presence or absence of an inclusion is clear in all reconstructed elastograms even when artifacts are present in displacement fields, indicating further robustness to measurement noise. The proposed stochastic method allows fusion of data from a robot’s sensors directly into compressional elastography image reconstruction which may stabilize optimization and improve accuracy. This approach provides a mathematical framework to readily incorporate measurements from additional sensors in future applications which may extend the capabilities of compressional elastography beyond that of producing quantitative elasticity measurements.

Reconstructing the shear modulus contrast of linear elastic and isotropic media in quasi-static ultrasound elastography.

This study focuses on the reconstruction of the shear modulus contrast in linear elastic and isotropic media, in quasi-static ultrasound elastography. The method proposed is based on the variational formulation of the equilibrium equations and on the choice of adapted discretization spaces to estimate the parameters of interest. Experimental results obtained with CIRS phantoms are presented, for which regions with different mechanical properties can be clearly identified in the stiffness contrast maps. Elastic modulus images collected with a shear-wave elastography technique from a clinical ultrasound scanner (Aixplorer) are also provided for comparison. Results show very similar values for the modulus ratios determined by the two elastography approaches.

2D tissue strain tensor imaging in quasi-static ultrasound elastography.

Accurately estimating all strain components in quasi-static ultrasound elastography is crucial for the full analysis of biological media. In this paper, 2D strain tensor imaging is investigated, using a partial differential equation (PDE)-based regularization method. More specifically, this method employs the tissue property of incompressibility to smooth the displacement fields and reduce the noise in the strain components. The performance of the method is assessed with phantoms and in vivo breast tissues. For all the media examined, the results showed a significant improvement in both lateral displacement and strain but also, to a lesser extent, in the shear strain. Moreover, axial displacement and strain were only slightly modified by the regularization, as expected. Finally, the easier detectability of the inclusion/lesion in the final lateral strain images is associated with higher elastographic contrast-to-noise ratios (CNRs), with values in the range [0.68 - 9.40] vs [0.09 - 0.38] before regularization.