Real-time quasi-static ultrasound elastography

Unveiling the potential of ultrasound in brain imaging: Innovations, challenges, and prospects

Within medical imaging, ultrasound serves as a crucial tool, particularly in the realms of brain imaging and disease diagnosis. It offers superior safety, speed, and wider applicability compared to Magnetic Resonance Imaging (MRI) and X-ray Computed Tomography (CT). Nonetheless, conventional transcranial ultrasound applications in adult brain imaging face challenges stemming from the significant acoustic impedance contrast between the skull bone and soft tissues. Recent strides in ultrasound technology encompass a spectrum of advancements spanning tissue structural imaging, blood flow imaging, functional imaging, and image enhancement techniques. Structural imaging methods include traditional transcranial ultrasound techniques and ultrasound elastography. Transcranial ultrasound assesses the structure and function of the skull and brain, while ultrasound elastography evaluates the elasticity of brain tissue. Blood flow imaging includes traditional transcranial Doppler (TCD), ultrafast Doppler (UfD), contrast-enhanced ultrasound (CEUS), and ultrasound localization microscopy (ULM), which can be used to evaluate the velocity, direction, and perfusion of cerebral blood flow. Functional ultrasound imaging (fUS) detects changes in cerebral blood flow to create images of brain activity. Image enhancement techniques include full waveform inversion (FWI) and phase aberration correction techniques, focusing on more accurate localization and analysis of brain structures, achieving more precise and reliable brain imaging results. These methods have been extensively studied in clinical animal models, neonates, and adults, showing significant potential in brain tissue structural imaging, cerebral hemodynamics monitoring, and brain disease diagnosis. They represent current hotspots and focal points of ultrasound medical research. This review provides a comprehensive summary of recent developments in brain imaging technologies and methods, discussing their advantages, limitations, and future trends, offering insights into their prospects.

Quantification of Cervical Elasticity During Pregnancy Based on Transvaginal Ultrasound Imaging and Stress Measurement

Strain elastography and shear wave elastography are commonly used to quantify cervical elasticity. However, the absence of stress information in strain elastography causes difficulty in inter-session elasticity comparison, and the robustness of shear wave elastography is compromised by cervical tissue's high inhomogeneity.

OBJECTIVE

To overcome these limitations, we develop a quantitative cervical elastography system by adding a stress sensor to a clinically used transvaginal ultrasound imaging system.

METHODS

We record the cervical deformation in B-mode images and measure the probe-surface stress through the sensor. Then we quantify the strain using a customized algorithm and estimate the cervical Young's modulus through stress-strain linear regression.

RESULTS

In phantom experiments, we demonstrate the system's high accuracy (alignment with the quasi-static compression method, p-value = 0.369 > 0.05), robustness (alignment between 60°- and 90°-contact measurements, p-value = 0.638 > 0.05), repeatability (consistency of single sonographers' measurements, coefficient of variation < 0.06), and reproducibility (alignment between two sonographers' measurements, Pearson correlation coefficient = 0.981). Applying it to pregnant participants, we observe significant cervical softening (p-value < 0.001): Young's modulus decreases 3.95% weekly and its geometric mean value during the first (11 to 13 weeks), second, and third trimesters are 13.07 kPa, 7.59 kPa, and 4.40 kPa, respectively.

CONCLUSION

The proposed system is accurate, robust, and safe, and enables longitudinal and inter-examiner comparisons.

SIGNIFICANCE

The system applies to different ultrasound machines with minor software updates, which allows for studies of cervical softening patterns in pregnancy for larger populations, facilitating insights into conditions such as preterm birth.

An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency

Quasi-static ultrasound elastography (USE) is an imaging modality that measures deformation (i.e. strain) of tissue in response to an applied mechanical force. In USE, the strain modulus is traditionally obtained by deriving the displacement field estimated between a pair of radio-frequency data. In this work we propose a recurrent network architecture with convolutional long-short-term memory decoder blocks to improve displacement estimation and spatio-temporal continuity between time series ultrasound frames. The network is trained in an unsupervised way, by optimising a similarity metric between the reference and compressed image. Our training loss is also composed of a regularisation term that preserves displacement continuity by directly optimising the strain smoothness, and a temporal continuity term that enforces consistency between successive strain predictions. In addition, we propose an open-access in vivo database for quasi-static USE, which consists of radio-frequency data sequences captured on the arm of a human volunteer. Our results from numerical simulation and in vivo data suggest that our recurrent neural network can account for larger deformations, as compared with two other feed-forward neural networks. In all experiments, our recurrent network outperformed the state-of-the-art for both learning-based and optimisation-based methods, in terms of elastographic signal-to-noise ratio, strain consistency, and image similarity. Finally, our open-source code provides a 3D-slicer visualisation module that can be used to process ultrasound RF frames in real-time, at a rate of up to 20 frames per second, using a standard GPU.

Progress in the Application of Ultrasound Elastography for Brain Diseases

Ultrasound (US) can be used to evaluate the brain structure and nervous system damage. Patients with neurologic symptoms need rapid, noninvasive imaging with high spatial resolution and tissue contrast. Magnetic resonance imaging is currently the most sensitive and specific imaging method for evaluating neuropathologic conditions. This approach does present some challenges, such as the need to transport patients who may be seriously ill to the magnetic resonance imaging suite and the need for patients to remain for a considerable time. Cranial US provides a very valuable imaging method for clinicians, which can make a rapid diagnosis and evaluation without ionizing radiation. The main disadvantage of cranial US is its low sensitivity and specificity for subtle/early lesions. In recent years, with the rapid development of anatomic and functional US technology, the practicability of US diagnosis and intervention has been greatly improved. Ultrasound elastography may have the potential to improve the sensitivity and specificity of various cranial nerve conditions. Ultrasound elastography has received considerable critical attention, and an increasing number of studies have recognized its critical role in evaluating brain diseases. At present, US elastography has been applied to the evaluation of traumatic brain injury, ischemic stroke, intraoperative brain tumors, and hypoxic ischemic encephalopathy. The latest animal experiments and human clinical trial developments in the applications of US elastography for brain diseases are summarized in this review.

Automatic Frame Selection using CNN in Ultrasound Elastography

Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency (RF) frames collected before and after some deformation, we estimate displacement and strain images by comparing the RF frames. The quality of the strain image is dependent on the type of motion that occurs during deformation. In-plane axial motion results in high-quality strain images, whereas out-of-plane motion results in low-quality strain images. In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. The RF frames were collected from both phantom and in vivo data.

Accurate and Precise Time-Delay Estimation for Ultrasound Elastography With Prebeamformed Channel Data

Free-hand palpation ultrasound elastography is a noninvasive approach for detecting pathological alteration in tissue. In this method, the tissue is compressed by a handheld probe and displacement of each sample is estimated, a process which is also known as time-delay estimation (TDE). Even with the simplifying assumption that ignores out of plane motion, TDE is an ill-posed problem requiring estimation of axial and lateral displacements for each sample from its intensity. A well-known class of methods for making elastography a well-posed problem is regularized optimization-based methods, which imposes smoothness regularization in the associated cost function. In this article, we propose to utilize channel data that have been compensated for time gain and time delay (introduced by transmission) instead of postbeamformed radio frequency (RF) data in the optimization problem. We name our proposed method Channel data for GLobal Ultrasound Elastography (CGLUE). We analytically derive bias and variances of TDE as functions of data noise for CGLUE and Global Ultrasound Elastography (GLUE) and use the Cauchy-Schwarz inequality to prove that CGLUE provides a TDE with lower bias and variance error. To further illustrate the improved performance of CGLUE, the results of simulation, experimental phantom, and ex-vivo experiments are presented.

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.

Relationship between Lower Urinary Tract Symptoms and Prostatic Urethral Stiffness Using Strain Elastography: Initial Experiences

We attempted to visualize the periurethral stiffness of prostatic urethras using strain elastography in the midsagittal plane of transrectal ultrasonography (TRUS) and to evaluate periurethral stiffness patterns in relation to lower urinary tract symptoms (LUTS). A total of 250 men were enrolled. The stiffness patterns of the entire prostate and individual zones were evaluated using strain elastography during a TRUS examination. After excluding 69 men with inappropriate elastography images, subjects were divided according to periurethral stiffness into either group A (low periurethral stiffness, N = 80) or group B (high periurethral stiffness, N = 101). There were significant differences in patient age (p = 0.022), transitional zone volume (p = 0.001), transitional zone index (p = 0.33), total international prostate symptom score (IPSS) (p < 0.001), IPSS-voiding subscore (p < 0.001), IPSS-storage subscore (p < 0.001), and quality of life (QoL) score (p = 0.002) between groups A and B. After adjusting for relevant variables, significant differences in total IPSS, IPSS-voiding subscore, and QoL score were maintained. Men with high periurethral stiffness were associated with worse urinary symptoms than those with low periurethral stiffness, suggesting that periurethral stiffness might play an important role in the development of LUTS.

Global Ultrasound Elastography in Spatial and Temporal Domains

In this paper, a novel computationally efficient quasi-static ultrasound elastography technique is introduced by optimizing an energy function. Unlike conventional elastography techniques, three radio frequency (RF) frames are considered to devise a nonlinear cost function consisting of data intensity similarity term, spatial regularization terms and, most importantly, temporal continuity terms. We optimize the aforesaid cost function efficiently to obtain the time-delay estimation (TDE) of all samples between the first two and last two frames of ultrasound images simultaneously, and spatially differentiate the TDE to generate axial strain map. A novelty in our spatial and temporal regularizations is that they adaptively change based on the data, which leads to substantial improvements in TDE. We handle the computational complexity resulting from incorporation of all samples from all three frames by converting our optimization problem to a sparse linear system of equations. Consideration of both spatial and temporal continuity makes the algorithm more robust to signal decorrelation than the previous algorithms. We name the proposed method GUEST: Global Ultrasound Elastography in Spatial and Temporal directions. We validated our technique with simulation, experimental phantom, and in vivo liver data and compared the results with two recently proposed TDE methods. In all the experiments, GUEST substantially outperforms other techniques in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and strain ratio (SR) of the strain images.

Dictionary Representations for Electrode Displacement Elastography

Ultrasound electrode displacement elastography (EDE) has demonstrated the potential to monitor ablated regions in human patients after minimally invasive microwave ablation procedures. Displacement estimation for EDE is commonly plagued by decorrelation noise artifacts degrading displacement estimates. In this paper, we propose a global dictionary learning approach applied to denoising displacement estimates with an adaptively learned dictionary from EDE phantom displacement maps. The resulting algorithm is one that represents displacement patches sparsely if they contain low noise and averages remaining patches thereby denoising displacement maps while retaining important edge information. The results of dictionary-represented displacements presented with a higher signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) with improved contrast, as well as improved phantom inclusion delineation when compared to initial displacements, median-filtered displacements, and spline smoothened displacements, respectively. In addition to visualized noise reduction, dictionary-represented displacements presented with the highest SNR, CNR, and improved contrast with values of 1.77, 4.56, and 4.35 dB, respectively, when compared to axial strain tensor images estimated using the initial displacements. Following EDE phantom imaging, we utilized dictionary representations from in vivo patient data, further validating efficacy. Denoising displacement estimates are a newer application for dictionary learning producing strong ablated region delineation with little degradation from denoising.

Nonlocal Coherent Denoising of RF Data for Ultrasound Elastography

Ultrasound elastography infers mechanical properties of living tissues from ultrasound radiofrequency (RF) data recorded while the tissues are undergoing deformation. A challenging yet critical step in ultrasound elastography is to estimate the tissue displacement (or, equivalently the time delay estimate) fields from pairs of RF data. The RF data are often corrupted with noise, which causes the displacement estimator to fail in many in vivo experiments. To address this problem, we present a nonlocal, coherent denoising approach based on Bayesian estimation to reduce the impact of noise. Despite incoherent denoising algorithms that smooth the B-mode images, the proposed denoising algorithm is used to suppress noise while maintaining useful information such as speckle patterns. We refer to the proposed approach as COherent Denoising for Elastography (CODE) and evaluate its performance when CODE is used in conjunction with the two state-of-art elastography algorithms, namely: (i) GLobal Ultrasound Elastography (GLUE) and (ii) Dynamic Programming Analytic Minimization elastography (DPAM). Our results show that CODE substantially improves the strain result of both GLUE and DPAM.

Supervised Classification of the Accuracy of the Time Delay Estimation in Ultrasound Elastography

The accuracy of time-delay estimation (TDE) in ultrasound elastography is usually measured by calculating the value of normalized cross correlation (NCC) at the estimated displacement. NCC value, however, could be very high at a displacement estimate with large error, a well-known problem in TDE referred to as peak-hopping. Furthermore, NCC value could suffer from jitter error, which is due to electric noise and signal decorrelation. Herein, we propose a novel method to assess the accuracy of TDE by investigating the NCC profile around the estimated time-delay. We extract several features from the NCC profile, and utilize support vector machine to classify peak-hopping and jitter error. The results on simulation, phantom, and in vivo data show the significant improvement of the proposed algorithm compared to the state of the art techniques.

Ultrasound 2D strain measurement for arm lymphedema using deformable registration: A feasibility study

Purpose

Lymphedema, a swelling of the extremity, is a debilitating morbidity of cancer treatment. Current clinical evaluation of lymphedema is often based on medical history and physical examinations, which is subjective. In this paper, the authors report an objective, quantitative 2D strain imaging approach using a hybrid deformable registration to measure soft-tissue stiffness and assess the severity of lymphedema.

Methods

The authors have developed a new 2D strain imaging method using registration of pre- and post-compression ultrasound B-mode images, which combines the statistical intensity- and structure-based similarity measures using normalized mutual information (NMI) metric and normalized sum-of-squared-differences (NSSD), with an affine-based global and B-spline-based local transformation model. This 2D strain method was tested through a series of experiments using elastography phantom under various pressures. Clinical feasibility was tested with four participants: two patients with arm lymphedema following breast-cancer radiotherapy and two healthy volunteers.

Results

The phantom experiments have shown that the proposed registration-based strain method significantly increased the signal-to-noise and contrast-to-noise ratio under various pressures as compared with the commonly used cross-correlation-based elastography method. In the pilot study, the strain images were successfully generated for all participants. The averaged strain values of the lymphedema affected arms were much higher than those of the normal arms.

Conclusions

The authors have developed a deformable registration-based 2D strain method for the evaluation of arm lymphedema. The initial findings are encouraging and a large clinical study is warranted to further evaluate this 2D ultrasound strain imaging technology.

Poor reproducibility of compression elastography in the Achilles tendon: same day and consecutive day measurements

OBJECTIVE

To determine the reproducibility of compression elastography (CE) when measuring strain data, a measure of stiffness of the human Achilles tendon in vivo, over consecutive measures, consecutive days and when using different foot positions.

MATERIALS AND METHODS

Eight participants (4 males, 4 females; mean age 25.5 ± 2.51 years, range 21-30 years; height 173.6 ± 11.7 cm, range 156-189 cm) had five consecutive CE measurements taken on one day and a further five CE measures taken, one per day, at the same time of day, every day for a consecutive 5-day period. These 80 measurements were used to assess both the repeatability and reproducibility of the technique. Means, standard deviations, coefficient of variation (CV), Pearson correlation analysis (R) and intra-class correlation coefficients (ICC) were calculated.

RESULTS

For CE data, all CVs were above 53%, R values indicated no-to-weak correlations between measures at best (range 0.01-0.25), and ICC values were all classified in the poor category (range 0.00-0.11). CVs for length and diameter measures were acceptably low indicating a high level of reliability.

CONCLUSIONS

Given the wide variation obtained in the CE results, it was concluded that CE using this specific system has a low level of reproducibility for measuring the stiffness of the human Achilles tendon in vivo over consecutive days, consecutive measures and in different foot positions.

Rotation Elastogram Estimation Using Synthetic Transmit-aperture Technique: A Feasibility Study

It is well-documented in literature that benign breast lesions, such as fibroadenomas, are loosely bonded to their surrounding tissue and tend to slip under a small quasi-static compression, whereas malignant lesions being firmly bonded to their surrounding tissue do not slip. Recent developments in quasi-static ultrasound elastography have shown that an image of the axial-shear strain distribution can provide information about the bonding condition at the lesion-surrounding tissue boundary. Further studies analyzing the axial-shear strain elastograms revealed that nonzero axial-shear strain values appear inside the lesion, referred to as fill-in, only when a lesion is loosely bonded and asymmetrically oriented to the axis of compression. It was argued that the fill-in observed in axial-shear strain elastogram is a surrogate of the actual rigid-body rotation undergone by such a benign lesion due to slip boundary condition. However, it may be useful and perhaps easy to interpret, if the actual rigid-body rotation of the lesion can itself be visualized directly. To estimate this rotation tensor and its spatial distribution map (called a Rotation Elastogram [RE]), it would be necessary to improve the quality of lateral displacement estimates. Recently, it has been shown in the context of Non-Invasive Vascular Elastography (NIVE) that the Synthetic Transmit Aperture (STA) technique can be adapted for elastography to improve the lateral displacement estimates. Therefore, the focus of this work was to investigate the feasibility of employing the STA technique to improve the lateral displacement estimation and assess the resulting improvement in the RE quality. This investigation was done using both simulation and experimental studies. The image quality metric of contrast-to-noise ratio (CNR) was used to evaluate the quality of rotation elastograms. The results demonstrate that the contrast appeared in RE only in the case of loosely bonded inclusion, and the quality of RE improved considerably by employing the STA technique.

Design and Testing of a Single-Element Ultrasound Viscoelastography System for Point-of-Care Edema Quantification

Management of fluid overload in patients with end-stage renal disease represents a unique challenge to clinical practice because of the lack of accurate and objective measurement methods. Currently, peripheral edema is subjectively assessed by palpation of the patient's extremities, ostensibly a qualitative indication of tissue viscoelastic properties. New robust quantitative estimates of tissue fluid content would allow clinicians to better guide treatment, minimizing reactive treatment decision making. Ultrasound viscoelastography (UVE) can be used to estimate strain in viscoelastic tissue, deriving material properties that can help guide treatment. We are developing and testing a simple, low-cost UVE system using a single-element imaging transducer that is simpler and less computationally demanding than array-based systems. This benchtop validation study tested the feasibility of using the UVE system by measuring the mechanical properties of a tissue-mimicking material under large strains. We generated depth-dependent creep curves and viscoelastic parameter maps of time constants and elastic moduli for the Kelvin model of viscoelasticity. During testing, the UVE system performed well, with mean UVE-measured strain matching standard mechanical testing with maximum absolute errors ≤4%. Motion tracking revealed high correlation and signal-to-noise ratios, indicating that the system is reliable.

Performance study of a new time-delay estimation algorithm in ultrasonic echo signals and ultrasound elastography

Time-delay estimation has countless applications in ultrasound medical imaging. Previously, we proposed a new time-delay estimation algorithm, which was based on the summation of the sign function to compute the time-delay estimate (Shaswary et al., 2015). We reported that the proposed algorithm performs similar to normalized cross-correlation (NCC) and sum squared differences (SSD) algorithms, even though it was significantly more computationally efficient. In this paper, we study the performance of the proposed algorithm using statistical analysis and image quality analysis in ultrasound elastography imaging. Field II simulation software was used for generation of ultrasound radio frequency (RF) echo signals for statistical analysis, and a clinical ultrasound scanner (Sonix® RP scanner, Ultrasonix Medical Corp., Richmond, BC, Canada) was used to scan a commercial ultrasound elastography tissue-mimicking phantom for image quality analysis. The statistical analysis results confirmed that, in overall, the proposed algorithm has similar performance compared to NCC and SSD algorithms. The image quality analysis results indicated that the proposed algorithm produces strain images with marginally higher signal-to-noise and contrast-to-noise ratios compared to NCC and SSD algorithms.

Edge-preserving ultrasonic strain imaging with uniform precision

Ultrasound elastography involves measuring the mechanical properties of tissue, and has many applications in diagnostics and intervention. A common step in different elastography methods is imaging the tissue while it undergoes deformation and estimating the displacement field from the images. A popular next step is to estimate tissue strain, which gives clues into the underlying tissue elasticity modulus. To estimate the strain, one should compute the gradient of the displacement image, which amplifies the noise. The noise is commonly minimized by least square estimation of the gradient from multiple displacement measurements, which reduces the noise by sacrificing image resolution. In this work, we adaptively adjust the level and orientation of the smoothing using two different mechanisms. First, the precision of the displacement field decreases significantly in the regions with high signal decorrelation, which requires increasing the smoothness. Second, smoothing the strain field at the boundaries between different tissue types blurs the edges, which can render small targets invisible. To minimize blurring and noise, we perform anisotropic smoothing parallel to the direction of edges. The first mechanism ensures that textures/variations in the strain image reflect underlying tissue properties and are not caused by errors in the displacement estimation. The second mechanism keeps the edges between different tissue structures sharp while minimizing the noise. We validate the proposed method using phantom and in-vivo clinical data.

A survey of ultrasound elastography approaches to percutaneous ablation monitoring

Percutaneous thermal ablation has been widely used as a minimally invasive treatment for tumors. Treatment monitoring is essential for preventing complications while ensuring treatment efficacy. Mechanical testing measurements on tissue reveal that tissue stiffness increases with temperature and ablation duration. Different types of imaging methods can be used to monitor ablation procedures, including temperature or thermal strain imaging, strain imaging, modulus imaging, and shear modulus imaging. Ultrasound elastography demonstrates the potential to become the primary imaging modality for monitoring percutaneous ablation. This review briefly presented the state-of-the-art ultrasound elastography approaches for monitoring radiofrequency ablation and microwave ablation. These techniques were divided into four groups: quasi-static elastography, acoustic radiation force elastography, sonoelastography, and applicator motion elastography. Their advantages and limitations were compared and discussed. Future developments were proposed with respect to heat-induced bubbles, tissue inhomogeneities, respiratory motion, three-dimensional monitoring, multi-parametric monitoring, real-time monitoring, experimental data center for percutaneous ablation, and microwave ablation monitoring.

In vivo assessment of the biomechanical properties of the uterine cervix in pregnancy

Measuring the stiffness of the cervix might be useful in the prediction of preterm delivery or successful induction of labor. For that purpose, a variety of methods for quantitative determination of physical properties of the pregnant cervix have been developed. Herein, we review studies on the clinical application of these new techniques. They are based on the quantification of mechanical, optical, or electrical properties associated with increased hydration and loss of organization in collagen structure. Quasi-static elastography determines relative values of stiffness; hence, it can identify differences in deformability. Quasi-static elastography unfortunately cannot quantify in absolute terms the stiffness of the cervix. Also, the current clinical studies did not demonstrate the ability to predict the time point of delivery. In contrast, measurement of maximum deformability of the cervix (e.g. quantified with the cervical consistency index) provided meaningful results, showing an increase in compliance with gestational age. These findings are consistent with aspiration measurements on the pregnant ectocervix, indicating a progressive decrease of stiffness along gestation. Cervical consistency index and aspiration measurements therefore represent promising techniques for quantitative assessment of the biomechanical properties of the cervix.

Ultrasound 2D Strain Estimator Based on Image Registration for Ultrasound Elastography

In this paper, we present a new approach to calculate 2D strain through the registration of the pre- and post-compression (deformation) B-mode image sequences based on an intensity-based non-rigid registration algorithm (INRA). Compared with the most commonly used cross-correlation (CC) method, our approach is not constrained to any particular set of directions, and can overcome displacement estimation errors introduced by incoherent motion and variations in the signal under high compression. This INRA method was tested using phantom and in vivo data. The robustness of our approach was demonstrated in the axial direction as well as the lateral direction where the standard CC method frequently fails. In addition, our approach copes well under large compression (over 6%). In the phantom study, we computed the strain image under various compressions and calculated the signal-to-noise (SNR) and contrast-to-noise (CNS) ratios. The SNR and CNS values of the INRA method were much higher than those calculated from the CC-based method. Furthermore, the clinical feasibility of our approach was demonstrated with the in vivo data from patients with arm lymphedema.

An efficient block matching and spectral shift estimation algorithm with applications to ultrasound elastography

An efficient block matching and spectral shift estimation algorithm for freehand quasi-static ultrasound elastography is described in this paper. The proposed method provides a balance between computational speed and robustness against displacement estimation error and bias; a fundamental aspect of elastography. The new algorithm was tested on an extensive set of simulated 1-D RF ultrasound signals, replicating various strain profiles. Additionally, real 2-D scans were conducted on an ultrasound phantom with prescribed elastic properties; the algorithm output was further validated with a comparison to a finite element model (FEM) of the phantom. Clinical data from a breast cancer study and histology slides were used to demonstrate the in vivo use of the new elastography technique. The algorithm showed a significant computational savings (at least 60 times faster) over existing spectral shift analysis methods. Accurate strain images were produced in as little as 2 s with the scope for further speed enhancements through parallel processing; making real-time implementation a future possibility. Moreover, it demonstrated a robustness toward displacement estimation error when compared with conventional gradient-based techniques, and was able to perform at high strain values (>5%) while showing relative insensitivity to various parameters settings, such as sample rate and block window size; a desirable performance for a practical clinical tool.