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Objectives: Non-invasive methods for monitoring arterial health and identifying early injury to optimize treatment for patients are desirable. The objective of this study was to demonstrate the use of an adaptive Bayesian regularized Lagrangian carotid strain imaging (ABR-LCSI) algorithm for monitoring of atherogenesis in a murine model and examine associations between the ultrasound strain measures and histology., Methods: Ultrasound radiofrequency (RF) data were acquired from both the right and left common carotid artery (CCA) of 10 (5 male and 5 female) ApoE tm1Unc/J mice at 6, 16 and 24 wk. Lagrangian accumulated axial, lateral and shear strain images and three strain indices-maximum accumulated strain index (MASI), peak mean strain of full region of interest (ROI) index (PMSRI) and strain at peak axial displacement index (SPADI)-were estimated using the ABR-LCSI algorithm. Mice were euthanized (n = 2 at 6 and 16 wk, n = 6 at 24 wk) for histology examination., Results: Sex-specific differences in strain indices of mice at 6, 16 and 24 wk were observed. For male mice, axial PMSRI and SPADI changed significantly from 6 to 24 wk (mean axial PMSRI at 6 wk = 14.10 ± 5.33% and that at 24 wk = -3.03 ± 5.61%, p < 0.001). For female mice, lateral MASI increased significantly from 6 to 24 wk (mean lateral MASI at 6 wk = 10.26 ± 3.13% and that at 24 wk = 16.42 ± 7.15%, p = 0.048). Both cohorts exhibited strong associations with ex vivo histological findings (male mice: correlation between number of elastin fibers and axial PMSRI: r s  = 0.83, p = 0.01; female mice: correlation between shear MASI and plaque score: r s  = 0.77, p = 0.009)., Conclusion: The results indicate that ABR-LCSI can be used to measure arterial wall strain in a murine model and that changes in strain are associated with changes in arterial wall structure and plaque formation., (Copyright © 2023 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Liver cancer is a leading cause of cancer-related deaths; however, primary treatment options such as surgical resection and liver transplant may not be viable for many patients. Minimally invasive image-guided microwave ablation (MWA) provides a locally effective treatment option for these patients with an impact comparable to that of surgery for both cancer-specific and overall survival. MWA efficacy is correlated with accurate image guidance; however, conventional modalities such as B-mode ultrasound and computed tomography have limitations. Alternatively, ultrasound elastography has been used to demarcate post-ablation zones, yet has limitations for pre-ablation visualization because of variability in strain contrast between cancer types. This study attempted to characterize both pre-ablation tumors and post-ablation zones using electrode displacement elastography (EDE) for 13 patients with hepatocellular carcinoma or liver metastasis. Typically, MWA ablation margins of 0.5-1.0 cm are desired, which are strongly correlated with treatment efficacy. Our results revealed an average estimated ablation margin inner quartile range of 0.54-1.21 cm with a median value of 0.84 cm. These treatment margins lie within or above the targeted ablative margin, indicating the potential to use EDE for differentiating index tumors and ablated zones during clinical ablations. We also obtained a high correlation between corresponding segmented cross-sectional areas from contrast-enhanced computed tomography, the current clinical gold standard, when compared with EDE strain images, with r 2 values of 0.97 and 0.98 for pre- and post-ablation regions., Competing Interests: Conflict of interest disclosure Authors have no conflicts of interest to disclose with regards to this work., (Copyright © 2021 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Microwave ablation has become a common treatment method for liver cancers. Unfortunately, microwave ablation success is correlated with clinician's ability for proper electrode placement and assess ablative margins, requiring accurate imaging of liver tumors and ablated zones. Conventionally, ultrasound and computed tomography are utilized for this purpose, yet both have their respective drawbacks. As an alternate approach, electrode displacement elastography offers promise but is still plagued by decorrelation artifacts reducing lesion depiction and visualization. A recent filtering method, namely dictionary representation, has improved contrast-to-noise ratios without reducing delineation contrast. As a supplement to this recent work, this paper evaluates adaptations on this initial dictionary-learning algorithm and applies them to an EDE phantom and 15 in-vivo patient datasets. Two new adaptations of dictionary representations were evaluated, namely a combined dictionary and magnitude-based dictionary representation. When comparing numerical results, the combined dictionary representation algorithm outperforms the previous developed dictionary representation in signal-to-noise (1.54 dB) and contrast-to-noise (0.67 dB) ratios, while a magnitude dictionary representation produces higher noise levels, but improves visualized strain tensor resolution.

Normalized cross-correlation (NCC) function used in ultrasound strain imaging can get corrupted due to signal decorrelation inducing large displacement errors. Bayesian regularization has been applied in an iterative manner to regularize the NCC function and to reduce estimation variance and peak-hopping errors. However, incorrect choice of the number of iterations can lead to over-regularization errors. In this paper, we propose the use of log compression of regularized NCC function to improve subsample estimation. Performance of parabolic interpolation before and after log compression of the regularized NCC function were compared in numerical simulations of uniform and inclusion phantoms. Significant improvement was achieved with the proposed scheme for lateral estimation results. For example, lateral signal-to-noise ratio (SNR) was 10 dB higher after log compression at 3% strain in a uniform phantom. Lateral contrast-to-noise ratio (CNR) was 1.81 dB higher with proposed method at 3% strain in inclusion phantom. No significant difference was observed in axial estimation due to presence of phase information and high sampling frequency. Our results suggest that this simple approach makes Bayesian regularization robust to over-regularization artifacts.

Ultrasound strain imaging utilizes radio-frequency (RF) ultrasound echo signals to estimate the relative elasticity of tissue under deformation. Due to the diagnostic value inherent in tissue elasticity, ultrasound strain imaging has found widespread clinical and preclinical applications. Accurate displacement estimation using pre and post-deformation RF signals is a crucial first step to derive high quality strain tensor images. Incorporating regularization into the displacement estimation framework is a commonly employed strategy to improve estimation accuracy and precision. In this work, we propose an adaptive variation of the iterative Bayesian regularization scheme utilizing RF similarity metric signal-to-noise ratio previously proposed by our group. The regularization scheme is incorporated into a 2D multi-level block matching (BM) algorithm for motion estimation. Adaptive nature of our algorithm is attributed to the dynamic variation of iteration number based on the normalized cross-correlation (NCC) function quality and a similarity measure between pre-deformation and motion compensated post-deformation RF signals. The proposed method is validated for either quasi-static and cardiac elastography or strain imaging applications using uniform and inclusion phantoms and canine cardiac deformation simulation models. Performance of adaptive Bayesian regularization was compared to conventional NCC and Bayesian regularization with fixed number of iterations. Results from uniform phantom simulation study show significant improvement in lateral displacement and strain estimation accuracy. For instance, at 1.5% lateral strain in a uniform phantom, Bayesian regularization with five iterations incurred a lateral strain error of 104.49%, which was significantly reduced using our adaptive approach to 27.51% (p   <  0.001). Contrast-to-noise (CNR e ) ratios obtained from inclusion phantom indicate improved lesion detectability for both axial and lateral strain images. For instance, at 1.5% lateral strain, Bayesian regularization with five iterations had lateral CNR e of  -0.31 dB which was significantly increased using the adaptive approach to 7.42 dB (p   <  0.001). Similar results are seen with cardiac deformation modelling with improvement in myocardial strain images. In vivo feasibility was also demonstrated using data from a healthy murine heart. Overall, the proposed method makes Bayesian regularization robust for clinical and preclinical applications.

Minimally invasive treatments such as microwave ablation (MWA) have been growing in popularity for extending liver cancer survival rates in patients, when surgery is not an option. As a non-ionizing, real-time alternative to contrast-enhanced computed tomography, electrode displacement elastography (EDE) has shown promise as an imaging modality for MWA. Despite imaging efficacy, motion artifacts caused by physiological motion result in unintended speckle pattern variance, thereby inhibiting consistent and accurate ablated region visualization. To combat these unavoidable motion artifacts, a Lagrangian deformation tracking (LDT) approach based on freehand EDE was developed to track tissue movement and better define tissue properties. For validating LDT efficacy, a spherical inclusion phantom as well as seven in vivo data sets were processed, and strain tensor images were compared with identical time sampled images estimated using a traditional Eulerian approach. In vivo results revealed greater consistency among visualized LDT strain tensor images, with segmented ablated regions exhibiting standard deviation reductions of up to 98% when compared with Eulerian strain tensor images. Additionally, Lagrangian strain tensor images provided Dice coefficient improvements up to 25%, and success rates improved from approximately 50% to nearly 100% for ablated region visualization., (Copyright © 2019 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Cardiac elastography (CE) is an ultrasound-based technique utilizing radio-frequency (RF) signals for assessing global and regional myocardial function. In this work, a complete strain estimation pipeline for incorporating a Bayesian regularization-based hierarchical block-matching algorithm, with Lagrangian motion description and myocardial polar strain estimation is presented. The proposed regularization approach is validated using finite-element analysis (FEA) simulations of a canine cardiac deformation model that is incorporated into an ultrasound simulation program. Interframe displacements are initially estimated using a hierarchical motion estimation framework. Incremental displacements are then accumulated under a Lagrangian description of cardiac motion from end-diastole (ED) to end-systole (ES). In-plane Lagrangian finite strain tensors are then derived from the accumulated displacements. Cartesian to cardiac coordinate transformation is utilized to calculate radial and longitudinal strains for ease of interpretation. Benefits of regularization are demonstrated by comparing the same hierarchical block-matching algorithm with and without regularization. Application of Bayesian regularization in the canine FEA model provided improved ES radial and longitudinal strain estimation with statistically significant ( ) error reduction of 48.88% and 50.16%, respectively. Bayesian regularization also improved the quality of temporal radial and longitudinal strain curves with error reductions of 78.38% and 86.67% ( ), respectively. Qualitative and quantitative improvements were also visualized for in vivo results on a healthy murine model after Bayesian regularization. Radial strain elastographic signal-to-noise ratio (SNR e ) increased from 3.83 to 4.76 dB, while longitudinal strain SNR e increased from 2.29 to 4.58 dB with regularization.

Hepatocellular carcinoma and liver metastases are common hepatic malignancies presenting with high mortality rates. Minimally invasive microwave ablation (MWA) yields high success rates similar to surgical resection. However, MWA procedures require accurate image guidance during the procedure and for post-procedure assessments. Ultrasound electrode displacement elastography (EDE) has demonstrated utility for non-ionizing imaging of regions of thermal necrosis created with MWA in the ablation suite. Three strategies for displacement vector tracking and strain tensor estimation, namely coupled subsample displacement estimation (CSDE), a multilevel 2-D normalized cross-correlation method, and quality-guided displacement tracking (QGDT) have previously shown accurate estimations for EDE. This paper reports on a qualitative and quantitative comparison of these three algorithms over 79 patients after an MWA procedure. Qualitatively, CSDE presents sharply delineated, clean ablated regions with low noise except for the distal boundary of the ablated region. Multilevel and QGDT contain more visible noise artifacts, but delineation is seen over the entire ablated region. Quantitative comparison indicates CSDE with more consistent mean and standard deviations of region of interest within the mass of strain tensor magnitudes and higher contrast, while Multilevel and QGDT provide higher CNR. This fact along with highest success rates of 89% and 79% on axial and lateral strain tensor images for visualization of thermal necrosis using the Multilevel approach leads to it being the best choice in a clinical setting. All methods, however, provide consistent and reproducible delineation for EDE in the ablation suite., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

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.

A multilevel Lagrangian carotid strain imaging algorithm is analyzed to identify computational bottlenecks for implementation on a graphics processing unit (GPU). Displacement tracking including regularization was found to be the most computationally expensive aspect of this strain imaging algorithm taking about 2.2 h for an entire cardiac cycle. This intensive displacement tracking was essential to obtain Lagrangian strain tensors. However, most of the computational techniques used for displacement tracking are parallelizable, and hence GPU implementation is expected to be beneficial. A new scheme for subsample displacement estimation referred to as a multilevel global peak finder was also developed since the Nelder-Mead simplex optimization technique used in the CPU implementation was not suitable for GPU implementation. GPU optimizations to minimize thread divergence and utilization of shared and texture memories were also implemented. This enables efficient use of the GPU computational hardware and memory bandwidth. Overall, an application speedup of was obtained enabling the algorithm to finish in about 50 s for a cardiac cycle. Last, comparison of GPU and CPU implementations demonstrated no significant difference in the quality of displacement vector and strain tensor estimation with the two implementations up to a 5% interframe deformation. Hence, a GPU implementation is feasible for clinical adoption and opens opportunity for other computationally intensive techniques.

We compared a quasi-static ultrasound elastography technique, referred to as electrode displacement elastography (EDE), with acoustic radiation force impulse imaging (ARFI) for monitoring microwave ablation (MWA) procedures on patients diagnosed with liver neoplasms. Forty-nine patients recruited to this study underwent EDE and ARFI with a Siemens Acuson S2000 system after an MWA procedure. On the basis of visualization results from two observers, the ablated region in ARFI images was recognizable on 20 patients on average in conjunction with B-mode imaging, whereas delineable ablation boundaries could be generated on 4 patients on average. With EDE, the ablated region was delineable on 40 patients on average, with less imaging depth dependence. Study of tissue-mimicking phantoms revealed that the ablation region dimensions measured on EDE and ARFI images were within 8%, whereas the image contrast and contrast-to-noise ratio with EDE was two to three times higher than that obtained with ARFI. This study indicated that EDE provided improved monitoring results for minimally invasive MWA in clinical procedures for liver cancer and metastases., (Copyright © 2017 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Percutaneous needle-based liver ablation procedures are becoming increasingly common for the treatment of small isolated tumors in hepatocellular carcinoma patients who are not candidates for surgery. Rapid 3-D visualization of liver ablations has potential clinical value, because it can enable interventional radiologists to plan and execute needle-based ablation procedures with real time feedback. Ensuring the right volume of tissue is ablated is desirable to avoid recurrence of tumors from residual untreated cancerous cells. Shear wave velocity (SWV) measurements can be used as a surrogate for tissue stiffness to distinguish stiffer ablated regions from softer untreated tissue. This paper extends the previously reported sheaf reconstruction method to generate complete 3-D visualizations of SWVs without resorting to an approximate intermediate step of reconstructing transverse C planes. The noisy data are modeled using a Markov random field, and a computationally tractable reconstruction algorithm that can handle grids with millions of points is developed. Results from simulated ellipsoidal inclusion data show that this algorithm outperforms standard nearest neighbor interpolation by an order of magnitude in mean squared reconstruction error. Results from the phantom experiments show that it also provides a higher contrast-to-noise ratio by almost 2 dB and better signal-to-noise ratio in the stiff inclusion by over 2 dB compared with nearest neighbor interpolation and has lower computational complexity than linear and spline interpolation.

Microwave ablation has been used clinically as an alternative to surgical resection. However, lack of real-time imaging to assess treated regions may compromise treatment outcomes. We previously introduced electrode displacement elastography (EDE) for strain imaging and verified its feasibility in vivo on porcine animal models. In this study, we evaluated EDE on 44 patients diagnosed with hepatocellular carcinoma, treated using microwave ablation. The ablated region was identified on EDE images for 40 of the 44 patients. Ablation areas averaged 13.38 ± 4.99 cm 2 on EDE, compared with 7.61 ± 3.21 cm 2 on B-mode imaging. Contrast and contrast-to-noise ratios obtained with EDE were 232% and 98%, respectively, significantly higher than values measured on B-mode images (p < 0.001). This study indicates that EDE is feasible in patients and provides improved visualization of the ablation zone compared with B-mode ultrasound., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

We reconstruct the elastic modulus distribution for one tissue mimicking (TM) phantom and two in vivo biopsy-confirmed liver tumors using curvilinear ultrasound echo data. Spatial distribution of the relative elastic modulus values is determined by solving an inverse problem within a region of interest (ROI). This inverse problem solution requires knowledge of the ultrasonically measured displacement field in a uniform rectilinear grid to ensure that the resolution on the resultant relative elastic modulus elastogram will be uniform over the entire ROI. Taking advantage of a new speckle tracking algorithm, two different displacement tracking strategies are investigated: 1) sector-shaped ultrasound data were converted to ultrasound data on a rectilinear grid prior to speckle tracking and 2) axial and lateral displacements directly obtained from sector-shaped data were converted to vertical and horizontal displacements on a rectilinear grid after speckle tracking. Compared with strain elastography (SE), TM phantom results show that relative elastic modulus imaging (REMI) using Strategy 2 provided higher contrast-to-noise ratios (>300% and 25% increases compared with SE and REMI using Strategy 1, respectively). Furthermore, in phantoms, REMI using Strategy 2 more accurately (a 1.3% difference to shear wave elastography measurements) estimated the elastic contrast ratio between the target and the background, compared with both SE (20%-25%) and REMI using Strategy 1 (4.1%). It was also observed that relative modulus elastograms were more consistent with anatomical structures visualized on corresponding B-mode images for the two in vivo liver cases. Overall, we conclude that applying REMI is feasible for abdominal organs such as the liver. Strategy 2 offered improved and consistent results for the data investigated.

Vascular cognitive decline may be caused by micro-emboli generated by carotid plaque instability. We previously found that maximum strain indices in carotid plaque were significantly correlated with cognitive function. In the work described here, we examined these associations with a larger sample size, as well as evaluated the performance of these maximum strain indices in predicting cognitive impairment. Ultrasound-based strain imaging and cognition assessment were conducted on 75 human patients. Patients underwent one of two standardized cognitive test batteries, either the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) or the National Institute of Neurologic Disorder and Stroke-Canadian Stroke Network (NINDS-CSN) Vascular Cognitive Impairment Harmonization Standards (60 min). Scores were standardized within each battery to allow these data to be combined across all participants. Radiofrequency signals for ultrasound strain imaging were acquired on the carotid arteries using either a Siemens Antares with a VFX 13-5 linear array transducer or a Siemens S2000 with an 18 L6 linear array transducer. The same hierarchical block-matching motion tracking algorithm developed in our laboratory was used to estimate accumulated axial, lateral, and shear strain indices in carotid plaque, with inclusion of adventitia regardless of the ultrasound system and transducer used. Associations between cognitive z-scores and maximum strain indices were examined using Pearson's correlation coefficients. Maximum strain indices were also employed to predict cognitive impairment using receiver operating characteristic analysis. All correlations between maximum strain indices and total cognition were statistically significant (p < 0.05), indicating that these indices have good utility in predicting cognitive impairment. Maximum lateral strain indices provided an area under the curve of 0.85 for symptomatic patients and 0.68 for asymptomatic patients. Our results indicate the important relationship of maximum strain indices to cognitive function and the feasibility of using maximum strain indices to predict cognitive decline with inclusion of the adventitia layer into the segmentation of plaque., (Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Purpose: Ultrasound strain imaging is a relatively low cost and portable modality for monitoring percutaneous thermal ablation of liver neoplasms. However, a 3D strain volume reconstruction from existing 2D strain images is necessary to fully delineate the thermal dose distribution. Tissue mimicking (TM) phantom experiments were performed to validate a novel volume reconstruction algorithm referred to as sheaf of ultrasound planes reconstruction (SOUPR), based on a series of 2D rotational imaging planes., Methods: Reconstruction using SOUPR was formulated as an optimization problem with constraints on data consistency with 2D strain images and data smoothness of the volume data. Reconstructed ablation inclusion dimensions, volume, and elastographic signal to noise ratio (SNRe) and contrast to noise ratio (CNRe) were compared with conventional 3D ultrasound strain imaging based on interpolating a series of quasiparallel 2D strain images with a wobbler transducer., Results: Volume estimates of the phantom inclusion were in a similar range for both acquisition approaches. SNRe and CNRe obtained with SOUPR were significantly higher on the order of 250% and 166%, respectively. The mean error of the inclusion dimension reconstructed with a wobbler transducer was on the order of 10.4%, 3.5%, and 19.0% along the X, Y, and Z axes, respectively, while the error with SOUPR was on the order of 2.6%, 2.8%, and 9.6%. A qualitative comparison of SOUPR and wobbler reconstruction was also performed using a thermally ablated region created in ex vivo bovine liver tissue., Conclusions: The authors have demonstrated using experimental evaluations with a TM phantom that the reconstruction results obtained with SOUPR were superior when compared with a conventional wobbler transducer in terms of inclusion shape preservation and detectability.

Purpose: This paper discusses an application of particle filtering for estimating shear wave velocity in tissue using ultrasound elastography data. Shear wave velocity estimates are of significant clinical value as they help differentiate stiffer areas from softer areas which is an indicator of potential pathology., Methods: Radio-frequency ultrasound echo signals are used for tracking axial displacements and obtaining the time-to-peak displacement at different lateral locations. These time-to-peak data are usually very noisy and cannot be used directly for computing velocity. In this paper, the denoising problem is tackled using a hidden Markov model with the hidden states being the unknown (noiseless) time-to-peak values. A particle filter is then used for smoothing out the time-to-peak curve to obtain a fit that is optimal in a minimum mean squared error sense., Results: Simulation results from synthetic data and finite element modeling suggest that the particle filter provides lower mean squared reconstruction error with smaller variance as compared to standard filtering methods, while preserving sharp boundary detail. Results from phantom experiments show that the shear wave velocity estimates in the stiff regions of the phantoms were within 20% of those obtained from a commercial ultrasound scanner and agree with estimates obtained using a standard method using least-squares fit. Estimates of area obtained from the particle filtered shear wave velocity maps were within 10% of those obtained from B-mode ultrasound images., Conclusions: The particle filtering approach can be used for producing visually appealing SWV reconstructions by effectively delineating various areas of the phantom with good image quality properties comparable to existing techniques.

This paper presents an algorithm for 3-D reconstruction of tumor ablations using ultrasound shear wave imaging with electrode vibration elastography. Radio-frequency ultrasound data frames are acquired over imaging planes that form a subset of a sheaf of planes sharing a common axis of intersection. Shear wave velocity is estimated separately on each imaging plane using a piecewise linear function fitting technique with a fast optimization routine. An interpolation algorithm then computes velocity maps on a fine grid over a set of C-planes that are perpendicular to the axis of the sheaf. A full 3-D rendering of the ablation can then be created from this stack of C-planes; hence the name "Sheaf Of Ultrasound Planes Reconstruction" or SOUPR. The algorithm is evaluated through numerical simulations and also using data acquired from a tissue mimicking phantom. Reconstruction quality is gauged using contrast and contrast-to-noise ratio measurements and changes in quality from using increasing number of planes in the sheaf are quantified. The highest contrast of 5 dB is seen between the stiffest and softest regions of the phantom. Under certain idealizing assumptions on the true shape of the ablation, good reconstruction quality while maintaining fast processing rate can be obtained with as few as six imaging planes suggesting that the method is suited for parsimonious data acquisitions with very few sparsely chosen imaging planes.

Ultrasound frame rates play a key role for accurate cardiac deformation tracking. Insufficient frame rates lead to an increase in signal de-correlation artifacts resulting in erroneous displacement and strain estimation. Joint probability density distributions generated from estimated axial strain and its associated signal-to-noise ratio provide a useful approach to assess the minimum frame rate requirements. Previous reports have demonstrated that bi-modal distributions in the joint probability density indicate inaccurate strain estimation over a cardiac cycle. In this study, we utilize similar analysis to evaluate a 2-D multi-level displacement tracking and strain estimation algorithm for cardiac strain imaging. The effect of different frame rates, final kernel dimensions and a comparison of radio frequency and envelope based processing are evaluated using echo signals derived from a 3-D finite element cardiac model and five healthy volunteers. Cardiac simulation model analysis demonstrates that the minimum frame rates required to obtain accurate joint probability distributions for the signal-to-noise ratio and strain, for a final kernel dimension of 1 λ by 3 A-lines, was around 42 Hz for radio frequency signals. On the other hand, even a frame rate of 250 Hz with envelope signals did not replicate the ideal joint probability distribution. For the volunteer study, clinical data was acquired only at a 34 Hz frame rate, which appears to be sufficient for radio frequency analysis. We also show that an increase in the final kernel dimensions significantly affect the strain probability distribution and joint probability density function generated, with a smaller effect on the variation in the accumulated mean strain estimated over a cardiac cycle. Our results demonstrate that radio frequency frame rates currently achievable on clinical cardiac ultrasound systems are sufficient for accurate analysis of the strain probability distribution, when a multi-level 2-D algorithm and kernel dimensions on the order of 1 λ by 3 A-lines or smaller are utilized., (Copyright © 2014 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Background: Shear wave imaging (SWI) is a new ultrasound technique whose application facilitates quantitative tissue elasticity assessment during transrectal ultrasound biopsies of the prostate gland. The aim of this study was to determine whether SWI quantitative data can differentiate between benign and malignant areas within prostate glands in men suspected of prostate cancer (PCa)., Methods: We conducted a protocol-based, prospective, prebiopsy quantitative SWI of prostate glands in 50 unscreened men suspected of prostate cancer between July 2011 and May 2012. The ultrasound image of whole prostate gland was arbitrarily divided into 12 zones for sampling biopsies, as is carried out in routine clinical practice. Each region was imaged by grey scale and SWI imaging techniques. Each region was further biopsied irrespective of findings of grey scale or SWI on ultrasound. Additional biopsies were taken if SWI abnormal area was felt to be outside of these 12 zones. Quantitative assessment of SWI abnormal areas was obtained in kilopascals (kPa) from abnormal regions shown by SWI and compared with histopathology. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated for SWI (histopathology was a reference standard)., Results: Fifty patients, with a mean age of 69 ± 6.2 years, were recruited into the study. Thirty-three (66%) patients were diagnosed with PCa, while an additional 4 (8%) had atypia in at least one of the 12 prostate biopsies. Thirteen (26%) patients had a benign biopsy. Data analysed per core for SWI findings showed that for patients with PSA <20 μg/L, the sensitivity and specificity of SWI for PCa detection were 0.9 and 0.88, respectively, while in patients with PSA >20 μg/L, the sensitivity and specificity were 0.93 and 0.93, respectively. In addition, PCa had significantly higher stiffness values compared to benign tissues (p <0.05), with a trend toward stiffness differences in different Gleason grades., Conclusion: SWI provides quantitative assessment of the prostatic tissues and, in our preliminary observation, provides better diagnostic accuracy than grey-scale ultrasound imaging.

Accurate subsample displacement estimation is necessary for ultrasound elastography because of the small deformations that occur and the subsequent application of a derivative operation on local displacements. Many of the commonly used subsample estimation techniques introduce significant bias errors. This article addresses a reduced bias approach to subsample displacement estimations that consists of a two-dimensional windowed-sinc interpolation with numerical optimization. It is shown that a Welch or Lanczos window with a Nelder-Mead simplex or regular-step gradient-descent optimization is well suited for this purpose. Little improvement results from a sinc window radius greater than four data samples. The strain signal-to-noise ratio (SNR) obtained in a uniformly elastic phantom is compared with other parabolic and cosine interpolation methods; it is found that the strain SNR ratio is improved over parabolic interpolation from 11.0 to 13.6 in the axial direction and 0.7 to 1.1 in the lateral direction for an applied 1% axial deformation. The improvement was most significant for small strains and displacement tracking in the lateral direction. This approach does not rely on special properties of the image or similarity function, which is demonstrated by its effectiveness with the application of a previously described regularization technique.

Echocardiographic strain imaging is a promising new method for quantifying and displaying the health of cardiac muscle. Accurate regional myocardial function analysis requires high spatial and temporal resolution in addition to fidelity to the underlying deformation. However, all current clinical approaches use speckle-tracking algorithms applied to B-mode images derived from envelope signals. Such approaches are inherently of lower spatial resolution, since they require larger data blocks for deformation tracking due to the absence of phase information. In this paper, we compare the strain estimation performance using B-mode, envelope and radiofrequency signals, utilizing data acquired from a uniformly elastic tissue mimicking phantom, cardiac simulation, and clinical in vivo data. Signal-to-noise ratio improvements using radiofrequency signals for linear and phased array geometries were 5.80 dB and 9.48 dB over that obtained with envelope signals (at peak strain) in phantom studies, respectively. Cardiac simulation studies demonstrate that when averaged over the two cardiac cycles, the mean standard deviation of estimated strain using envelope signals from two of the six segments for a short-axes view (anterior and anterolateral) were 48% and 44% higher than that obtained using radiofrequency signals. These segments were chosen since one was along while the other was situated lateral to the beam propagation direction. In a similar manner, in vivo analysis on a volunteer also indicate that the standard deviation of the estimated strain using B-mode and envelope signals were 16% and 42% higher than that obtained using radiofrequency signals in the anteroseptal segment, and 45% and 27% in the anterior segment. These results demonstrate the significant reduction in the variability of strain estimated along with improvements in the spatial resolution and signal-to-noise ratios obtained using radiofrequency signals., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Purpose: Quantification of stiffness changes may provide important diagnostic information and aid in the early detection of cancers. Shear wave elastography is an imaging technique that assesses tissue stiffness using acoustic radiation force as an alternate to manual palpation reported previously with quasistatic elastography. In this study, the elastic properties of tissue mimicking materials, including agar, polyacrylamide (PAA), and silicone, are evaluated with an objective to determine material characteristics which resemble normal and cancerous prostate tissue., Methods: Acoustic properties and stiffness of tissue mimicking phantoms were measured using compressional mechanical testing and shear wave elastography using supersonic shear imaging. The latter is based on the principles of shear waves generated using acoustic radiation force. The evaluation included tissue mimicking materials (TMMs) within the prostate at different positions and sizes that could mimic cancerous and normal prostate tissue. Patient data on normal and prostate cancer tissues quantified using biopsy histopathology were used to validate the findings. Pathologist reports on histopathology were blinded to mechanical testing and elastographic findings., Results: Young's modulus values of 86.2 ± 4.5 and 271.5 ± 25.7 kPa were obtained for PAA mixed with 2% Al(2)O(3) particles and silicone, respectively. Young's modulus of TMMs from mechanical compression testing showed a clear trend of increasing stiffness with an increasing percentage of agar. The silicone material had higher stiffness values when compared with PAA with Al(2)O(3). The mean Young's modulus value in cancerous tissue was 90.5 ± 4.5 kPa as compared to 93.8 ± 4.4 and 86.2 ± 4.5 kPa obtained with PAA with 2% Al(2)O(3) phantom at a depth of 52.4 and 36.6 mm, respectively., Conclusions: PAA mixed with Al(2)O(3) provides the most suitable tissue mimicking material for prostate cancer tumor material, while agar could form the surrounding background to simulate normal prostate tissue.

Purpose: Electrode vibration elastography is a new shear wave imaging technique that can be used to visualize thermal ablation zones. Prior work has shown the ability of electrode vibration elastography to delineate radiofrequency ablations; however, there has been no previous study of delineation of microwave ablations or radiological-pathological correlations using multiple observers., Methods: Radiofrequency and microwave ablations were formed in ex vivo bovine liver tissue. Their visualization was compared on shear wave velocity and maximum displacement images. Ablation dimensions were compared to gross pathology. Elastographic imaging and gross pathology overlap and interobserver variability were quantified using similarity measures., Results: Elastographic imaging correlated with gross pathology. Correlation of area estimates was better in radiofrequency than in microwave ablations, with Pearson coefficients of 0.79 and 0.54 on shear wave velocity images and 0.90 and 0.70 on maximum displacement images for radiofrequency and microwave ablations, respectively. The absolute relative difference in area between elastographic imaging and gross pathology was 18.9% and 22.9% on shear wave velocity images and 16.0% and 23.1% on maximum displacement images for radiofrequency and microwave ablations, respectively., Conclusions: Statistically significant radiological-pathological correlation was observed in this study, but correlation coefficients were lower than other modulus imaging techniques, most notably in microwave ablations. Observers provided similar delineations for most thermal ablations. These results suggest that electrode vibration elastography is capable of imaging thermal ablations, but refinement of the technique may be necessary before it can be used to monitor thermal ablation procedures clinically.

Purpose: To track variations in the deformation of the arterial wall noninvasively by estimating the accumulated displacement and strain over a cardiac cycle may provide useful indicators of vascular health., Methods: In this paper, we propose an approach to track a region of interest (ROI) locally and estimate arterial stiffness variation in a familial hypercholesterolemic swine model of spontaneous atherosclerosis that allows for systematic and reproducible study of progression of the disease mechanism., Results: Strain and displacement indices may be derived from the variations of the accumulated displacement and accumulated strain (obtained from the gradient of the accumulated displacement) over a cardiac cycle to predict not only the likelihood of developing vascular diseases, but also the sites where they may occur. Currently, an ROI thickness value of less than one mm within the arterial wall is necessary for the axial accumulated displacement and strain to obtain reproducible estimates., Conclusions: Accumulated axial displacement and strain estimation on the artery wall shown in this paper indicate the repeatability of these measurements over several cardiac cycles and over five familial hypercholesterolemic swine. Our results also demonstrate the need for a small region of interest within the arterial walls for accurate and robust estimates of arterial function.

Purpose: Accurate cardiac deformation analysis for cardiac displacement and strain imaging over time requires Lagrangian description of deformation of myocardial tissue structures. Failure to couple the estimated displacement and strain information with the correct myocardial tissue structures will lead to erroneous result in the displacement and strain distribution over time., Methods: Lagrangian based tracking in this paper divides the tissue structure into a fixed number of pixels whose deformation is tracked over the cardiac cycle. An algorithm that utilizes a polar-grid generated between the estimated endocardial and epicardial contours for cardiac short axis images is proposed to ensure Lagrangian description of the pixels. Displacement estimates from consecutive radiofrequency frames were then mapped onto the polar grid to obtain a distribution of the actual displacement that is mapped to the polar grid over time., Results: A finite element based canine heart model coupled with an ultrasound simulation program was used to verify this approach. Segmental analysis of the accumulated displacement and strain over a cardiac cycle demonstrate excellent agreement between the ideal result obtained directly from the finite element model and our Lagrangian approach to strain estimation. Traditional Eulerian based estimation results, on the other hand, show significant deviation from the ideal result. An in vivo comparison of the displacement and strain estimated using parasternal short axis views is also presented., Conclusions: Lagrangian displacement tracking using a polar grid provides accurate tracking of myocardial deformation demonstrated using both finite element and in vivo radiofrequency data acquired on a volunteer. In addition to the cardiac application, this approach can also be utilized for transverse scans of arteries, where a polar grid can be generated between the contours delineating the outer and inner wall of the vessels from the blood flowing though the vessel.

In this article, we demonstrate the feasibility of saline infusion sonohysterography-based strain imaging for the determination of stiffness variations in uterine masses in vivo. Strain images are estimated using a 2-dimensional multilevel hybrid algorithm developed for sector array ultrasound transducers. Coarse displacements are initially estimated using envelope echo signals, followed by a guided finer displacement estimation using window lengths on the order of 6 wavelengths and 7 A-lines on radiofrequency data. Strain images are obtained by estimating displacement slopes using least squares estimation. In this prospective study, we show that stiffer masses such as fibroids appear darker or as regions with low strain on strain images and are thus clearly differentiated when compared to normal uterine tissue. A high strain boundary around stiffer masses referred to as a "halo" due to increased slipping or sliding of the mass during the applied deformation is also visualized. Uterine polyps, on the other hand, are visualized as masses that are brighter or regions with high strain when compared to the background myometrium, indicating the presence of a softer mass. Axial strain images provide additional new information that may supplement current clinical B-mode imaging used for the diagnosis of uterine abnormalities. Our results show the feasibility of improving clinical diagnosis based on strain imaging.

Elastographic imaging can be used to monitor ablation procedures; however, confident and clear determination of the ablation boundary is essential to ensure complete treatment of the pathological target. To investigate the potential for ablation boundary representation on elastographic images, local variations in the viscoelastic properties in radiofrequency-ablated regions that were formed in vivo in porcine liver tissue were quantified using dynamic indentation. Spatial stiffness maps were then correlated to stained histology, the gold standard for the determination of the ablation periphery or boundary. Regions of interest in 11 radiofrequency ablation samples were indented at 18-24 locations each, including the central zone of complete necrosis and more peripheral transition zones including normal tissue. Storage modulus and the rate of stiffening were both greatest in the central ablation zone and decreased with radial distance away from the center. The storage modulus and modulus contrast at the ablation outer transition zone boundary were 3.1 ± 1.0 kPa and 1.6 ± 0.4, respectively, and 36.2 ± 9.1 kPa and 18.3 ± 5.5 at the condensation boundary within the ablation zone. Elastographic imaging modalities were then compared to gross pathology in ex vivo bovine liver tissue. Area estimated from strain, shear-wave velocity, and gross pathology images were 470, 560, and 574 mm(2), respectively, and ablation widths were 19.4, 20.7, and 23.0 mm. This study has provided insights into spatial stiffness distributions within radiofrequency ablations and suggests that low stiffness contrast on the ablation periphery leads to the observed underestimation of ablation extent on elastographic images.

Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5% and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7% strain. It has to be noted that for applied deformations lower than 1%, since signal decorrelation errors are minimal, using this approach may degrade the displacement image.

This paper presents a new shear wave velocity imaging technique to monitor radio-frequency and microwave ablation procedures, coined electrode vibration elastography. A piezoelectric actuator attached to an ablation needle is transiently vibrated to generate shear waves that are tracked at high frame rates. The time-to-peak algorithm is used to reconstruct the shear wave velocity and thereby the shear modulus variations. The feasibility of electrode vibration elastography is demonstrated using finite element models and ultrasound simulations, tissue-mimicking phantoms simulating fully (phantom 1) and partially ablated (phantom 2) regions, and an ex vivo bovine liver ablation experiment. In phantom experiments, good boundary delineation was observed. Shear wave velocity estimates were within 7% of mechanical measurements in phantom 1 and within 17% in phantom 2. Good boundary delineation was also demonstrated in the ex vivo experiment. The shear wave velocity estimates inside the ablated region were higher than mechanical testing estimates, but estimates in the untreated tissue were within 20% of mechanical measurements. A comparison of electrode vibration elastography and electrode displacement elastography showed the complementary information that they can provide. Electrode vibration elastography shows promise as an imaging modality that provides ablation boundary delineation and quantitative information during ablation procedures.

Purpose: A three-dimensional finite element analysis based canine heart model is introduced that would enable the assessment of cardiac function., Methods: The three-dimensional canine heart model is based on the cardiac deformation and motion model obtained from the Cardiac Mechanics Research Group at UCSD. The canine heart model is incorporated into ultrasound simulation programs previously developed in the laboratory, enabling the generation of simulated ultrasound radiofrequency data to evaluate algorithms for cardiac elastography. The authors utilize a two-dimensional multilevel hybrid method to estimate local displacements and strain from the simulated cardiac radiofrequency data., Results: Tissue displacements and strains estimated along both the axial and lateral directions (with respect to the ultrasound scan plane) are compared to the actual scatterer movement obtained using the canine heart model. Simulation and strain estimation algorithms combined with the three-dimensional canine heart model provide high resolution displacement and strain curves for improved analysis of cardiac function. The use of principal component analysis along parasternal cardiac short axis views is also presented., Conclusions: A 3D cardiac deformation model is proposed for evaluating displacement tracking and strain estimation algorithms for cardiac strain imaging. Validation of the model is shown using ultrasound simulations to generate axial and lateral displacement and strain curves that are similar to the actual axial and lateral displacement and strain curves.

Quasi-static electrode displacement elastography, used for in-vivo imaging of radiofrequency ablation-induced lesions in abdominal organs such as the liver and kidney, is extended in this paper to dynamic vibrational perturbations of the ablation electrode. Propagation of the resulting shear waves into adjoining regions of tissue can be tracked and the shear wave velocity used to quantify the shear (and thereby Young's) modulus of tissue. The algorithm used utilizes the time-to-peak displacement data (obtained from finite element analyses) to calculate the speed of shear wave propagation in the material. The simulation results presented illustrate the feasibility of estimating the Young's modulus of tissue and is promising for characterizing the stiffness of radiofrequency-ablated thermal lesions and surrounding normal tissue.

Radio-frequency (RF) ablation is a minimally invasive treatment for tumors in various abdominal organs. It is effective if good tumor localization and intraprocedural monitoring can be done. In this paper, we investigate the feasibility of using an ultrasound-based Young's modulus reconstruction algorithm to image an ablated region whose stiffness is elevated due to tissue coagulation. To obtain controllable tissue deformations for abdominal organs during and/or intermediately after the RF ablation, the proposed modulus imaging method is specifically designed for using tissue deformation fields induced by the RF electrode. We have developed a new scheme under which the reconstruction problem is simplified to a 2-D problem. Based on this scheme, an iterative Young's modulus reconstruction technique with edge-preserving regularization was developed to estimate the Young's modulus distribution. The method was tested in experiments using a tissue-mimicking phantom and on ex vivo bovine liver tissues. Our preliminary results suggest that high contrast modulus images can be successfully reconstructed. In both experiments, the geometries of the reconstructed modulus images of thermal ablation zones match well with the phantom design and the gross pathology image, respectively.

Two-dimensional (2D) cross-correlation algorithms are necessary to estimate local displacement vector information for strain imaging. However, most of the current two-dimensional cross-correlation algorithms were developed for linear array transducers. Although sector and phased array transducers are routinely used for clinical imaging of abdominal and cardiac applications, strain imaging for these applications has been performed using one-dimensional (1D) cross-correlation analysis. However, one-dimensional cross-correlation algorithms are unable to provide accurate and precise strain estimation along all the angular insonification directions which can range from -45 degrees to 45 degrees with sector and phased array transducers. In addition, since sector and phased array based images have larger separations between beam lines as the pulse propagates deeper into tissue, signal decorrelation artifacts with deformation or tissue motion are more pronounced. In this article, the authors propose a multilevel two-dimensional hybrid algorithm for ultrasound sector and phased array data that demonstrate improved tracking and estimation performance when compared to the traditional 1D cross-correlation or 2D cross-correlation based methods. Experimental results demonstrate that the signal-to-noise and contrast-to-noise ratio estimates improve significantly for smaller window lengths for the hybrid method when compared to the currently used one-dimensional or two-dimensional cross-correlation algorithms. Strain imaging results on ex vivo thermal lesions created in liver tissue and in vivo on cardiac short-axis views demonstrate the improved image quality obtained with this method.

In the current practice of ultrasound elastography, only the axial component of the displacement vector is estimated and used to produce strain images. A method was recently proposed by our group to estimate both the axial and lateral components of a displacement vector using RF echo signal data acquired along multiple angular insonification directions of the ultrasound beam. Previous work has demonstrated that it is important to choose appropriate values for the maximum beam angle and angular increment to achieve optimal performance with this technique. In this paper, we present error propagation analysis using the least-square fitting process for the optimization of the angular increment and the maximum beam steered angle. Ultrasound simulations are performed to corroborate the theoretical prediction of the optimal values for the maximum beam angle and angular increment. Selection of the optimal parameters depends on system parameters, such as center frequency and aperture size. For typical system parameters, the optimal maximum beam angle is around 10 degrees for axial strain estimation and around 15 degrees for lateral strain estimation. The optimal angular increment is around 4 degrees-6 degrees, which indicates that only five to seven beam angles are required for this strain-tensor estimation technique.

Cardiac elastography using radiofrequency echo signals can provide improved 2D strain information compared to B-mode image data, provided data are acquired at sufficient frame rates. In this paper, we evaluate ultrasound frame rate requirements for unbiased and robust estimation of tissue displacements and strain. Both tissue-mimicking phantoms under cyclic compressions at rates that mimic the contractions of the heart and in vivo results are presented. Sinusoidal compressions were applied to the phantom at frequencies ranging from 0.5 to 3.5 cycles/sec, with a maximum deformation of 5% of the phantom height. Local displacements and strains were estimated using both a two-step one-dimensional and hybrid two-dimensional cross-correlation method. Accuracy and repeatability of local strains were assessed as a function of the ultrasound frame rate based on signal-to-noise ratio values. The maximum signal-to-noise ratio obtained in a uniformly elastic phantom is 20 dB for both a 1.26 Hz and a 2 Hz compression frequency when the radiofrequency echo acquisition is at least 12 Hz and 20 Hz respectively. However, for compression frequencies of 2.8 Hz and 4 Hz the maximum signal-to-noise ratio obtained is around 16 dB even for a 40 Hz frame rate. Our results indicate that unbiased estimation of displacements and strain require ultrasound frame rates greater than ten times the compression frequency, although a frame rate of about two times the compression frequency is sufficient to estimate the compression frequency imparted to the tissue-mimicking phantom. In vivo results derived from short-axis views of the heart acquired from normal human volunteers also demonstrate this frame rate requirement for elastography.

A 2-D strain estimation algorithm was used to estimate tendon strain from ultrasound data collected during muscle twitch contractions. We first used speckle tracking techniques to estimate frame-to-frame displacements of all pixels within a rectangular region of interest (ROI) positioned over a tendon. A weighted, least-squares approach was then solved for the displacements of the ROI endpoints that best fit the pixel displacements. We summed endpoint displacements across successive frames to determine the cumulative endpoint motion, which was then used to estimate the cumulative strain along the tendinous fibers. The algorithm was applied to ultrasound radiofrequency data, acquired at 74 frames per second over the tibialis anterior (TA) musculotendon junction (MTJ). The TA muscle was electrically stimulated with the subject holding voluntary preloads of 0%, 10%, 20%, 30%, 40%, and 50% of a maximum voluntary contraction (MVC). Peak tendon strains computed using elastography (0.06 to 0.80%) were slightly larger and occurred earlier (50-90 ms after stimulus) than calculations based on visual analysis of B-mode images. This difference likely reflected the more localized nature of the elastographic strain values. Estimates of the tangential elastic modulus (192 +/- 58 MPa) were consistent with literature values obtained using more direct approaches. It is concluded that automated elastographic approaches for computing in vivo tendon strains could provide new insights into musculotendon dynamics and function.

Two anthropomorphic uterine phantoms were developed that allow assessment and comparison of strain imaging systems adapted for use with saline-infused sonohysterography (SIS). Tissue-mimicking (TM) materials consist of dispersions of safflower oil in gelatin. TM fibroids are stiffer than the TM myometrium/cervix, and TM polyps are softer. The first uterine phantom has 3-mm-diameter TM fibroids distributed randomly in TM myometrium. The second uterine phantom has a 5-mm and 8-mm spherical TM fibroid, in addition to a 5-mm spherical and a 12.5-mm-long (medicine capsule-shaped) TM endometrial polyp protruding into the endometrial cavity; also, a 10-mm spherical TM fibroid projects from the serosal surface. Strain images using the first phantom show the stiffer 3-mm TM fibroids in the myometrium. Results from the second uterine phantom show that, as expected, parts of inclusions projecting into the uterine cavity will appear very stiff, whether they are stiff or soft. Results from both phantoms show that although there is a five-fold difference in the Young's moduli values, there is not a significant difference in the strain in the transition from the TM myometrium to the TM fat. These phantoms allow for realistic comparison and evolution of SIS strain imaging techniques and can aid clinical personnel to develop skills for SIS strain imaging.

Many ultrasonic parameters, primarily related to attenuation and scatterer size, have been used to characterize the composition of atherosclerotic plaque tissue. In this study, we combine elastographic (axial strain ratio) and ultrasonic tissue characterization parameters, namely the attenuation coefficient and a scattering parameter associated with an "equivalent" scatterer size to delineate between fibrous, calcified, and lipidic plaque tissue. We present results obtained from 44 ex vivo atherosclerotic plaque specimens obtained after carotid endarterectomy on human patients. Our results in the frequency range 2.5 - 7.5 MHz indicate that softer plaques (with higher values of the strain ratio) are usually associated with larger equivalent scatterer size estimates (200 - 500 microm) and lower values of the attenuation coefficient slope (<1 dB/cm/MHz). On the other hand, stiffer plaques (with lower strain ratio values) are associated with smaller equivalent scatterer size estimates (100 - 200 microm) and higher values of the attenuation coefficient slope (1 - 3 dB/cm/MHz). These results indicate that ultrasonic tissue characterization and strain parameters have the potential to differentiate between different plaque types. These parameters can be estimated from radio-frequency data acquired under in vivo conditions and may help the clinician decide on appropriate interventional techniques.

Two-dimensional (2D) transducer arrays represent a promising solution for implementing real time three-dimensional (3D) ultrasound elastography. 2D arrays enable electronic steering and focusing of ultrasound beams throughout a 3D volume along with improved slice thickness performance when compared to one-dimensional (1D) transducer arrays. Therefore, signal decorrelation caused by tissue motion in the elevational (out-of-plane) direction needs to be considered. In this paper, a closed form expression is derived for the correlation coefficient between pre- and postdeformation ultrasonic radio frequency signals. Signal decorrelation due to 3D motion of scatterers within the ultrasonic beam has been considered. Computer simulations are performed to corroborate the theoretical results. Strain images of a spherical inclusion phantom generated using 1D and 2D array transducers are obtained using a frequency domain simulation model. Quantitative image quality parameters, such as the signal-to-noise and contrast-to-noise ratios obtained using 1D, 2D, and 3D motion tracking algorithms, are compared to evaluate the performance with the 3D strain imaging system. The effect of the aperture size for 2D arrays and other factors that affect signal decorrelation are also discussed.

Because ablation therapy alters the elastic modulus of tissues, emerging strain imaging methods may enable clinicians for the first time to have readily available, cost-effective, real-time guidance to identify the location and boundaries of thermal lesions. Electrode displacement elastography is a method of strain imaging tailored specifically to ultrasound-guided electrode-based ablative therapies (e.g., radio-frequency ablation). Here tissue deformation is achieved by applying minute perturbations to the unconstrained end of the treatment electrode, resulting in localized motion around the end of the electrode embedded in tissue. In this article, we present a method for three-dimensional (3D) elastographic reconstruction from volumetric data acquired using the C7F2 fourSight four-dimensional ultrasound transducer, provided by Siemens Medical Solutions USA, Inc. (Issaquah, WA, USA). Lesion reconstruction is demonstrated for a spherical inclusion centered in a tissue-mimicking phantom, which simulates a thermal lesion embedded in a normal tissue background. Elastographic reconstruction is also performed for a thermal lesion created in vitro in canine liver using radio-frequency ablation. Postprocessing is done on the acquired raw radio-frequency data to form surface-rendered 3D elastograms of the inclusion. Elastographic volume estimates of the inclusion compare reasonably well with the actual known inclusion volume, with 3D electrode displacement elastography slightly underestimating the true inclusion volume.

This article describes the evaluation of a novel method of tissue displacement for use in the elastographic visualization of radio-frequency (rf) ablation-induced lesions. The method involves use of the radio-frequency ablation electrode as a displacement device, which provides localized compression in the region of interest. This displacement mechanism offers the advantage of easy in vivo implementation since problems such as excessive lateral and elevational displacements present when using external compression are reduced with this approach. The method was tested on a single-inclusion tissue-mimicking phantom containing a radio-frequency ablation electrode rigidly attached to the inclusion center. Full-frame rf echo signals were acquired from the phantom before and after electrode displacements ranging from 0.05 to 0.2 mm. One-dimensional cross-correlation analysis between pre- and postcompression signals was used to measure tissue displacements, and strains were determined by computing the gradient of the displacement. The strain contrast, contrast-to-noise ratio, and signal-to-noise ratio were estimated from the resulting strain images. Comparisons are drawn between the elastographically measured dimensions and those known a priori for the single-inclusion phantom. Electrode displacement elastography was found to slightly underestimate the inclusion dimensions. The method was also tested on a second tissue-mimicking phantom and on in vitro rf-ablated lesions in canine liver tissue. The results validate previous in vivo findings that electrode displacement elastography is an effective method for monitoring rf ablation.

Elastography or elasticity imaging techniques typically image local strains or Young's modulus variations along the insonification direction. Recently, techniques that utilize angular displacement estimates obtained from multiple angular insonification of tissue have been reported. Angular displacement estimates obtained along different angular insonification directions have been utilized for spatial-angular compounding to reduce noise artifacts in axial-strain elastograms, and for estimating the axial and lateral components of the displacement vector and the corresponding strain tensors. However, these angular strain estimation techniques were based on the assumption that noise artifacts in the displacement estimates were independent and identically distributed and that the displacement estimates could be modeled using a zero-mean normal probability density function. Independent and identically distributed random variables refer to a collection of variables that have the same probability distribution and are mutually independent. In this article, a modified least-squares approach is presented that does not make any assumption regarding the noise in the angular displacement estimates and incorporates displacement noise artifacts into the strain estimation process using a cross-correlation matrix of the displacement noise artifacts. Two methods for estimating noise artifacts from the displacement images are described. Improvements in the strain tensor (axial and lateral) estimation performance are illustrated utilizing both simulation data obtained using finite-element analysis and experimental data obtained from a tissue-mimicking phantom. Improvements in the strain estimation performance are quantified in terms of the elastographic signal-to-noise and contrast-to-noise ratios obtained with and without the incorporation of the displacement noise artifacts into the least-squares strain estimator.

In this article we investigate the generation of shear strain elastograms induced using a lateral shear deformation. Ultrasound simulation and experimental results demonstrate that the shear strain elastograms obtained under shear deformation exhibit significant differences between bound and unbound inclusions in phantoms, when compared to shear strain images induced upon an axial compression. A theoretical model that estimates the decorrelation between pre- and postdeformation radio frequency signals, as a function of extent of shear deformation, is also developed. Signal-to-noise ratios of shear strain elastograms obtained at different shear angles are investigated theoretically and verified using ultrasound simulations on a uniformly elastic phantom. For the simulation and experiment, a two-dimensinal block-matching-based algorithm is used to estimate the axial and lateral displacement. Shear strains are obtained from the displacement vectors using a least-squares strain estimator. Our results indicate that the signal-to-noise ratio (SNR) of shear strain images increases to reach a maximum and saturates, and then decreases with increasing shear angle. Using typical system parameters, the maximum achievable SNR for shear strain elastography is around 8 (18 dB), which is comparable to conventional axial strain elastography induced by axial compression. Shear strain elastograms obtained experimentally using single inclusion tissue-mimicking phantoms with both bound and unbound inclusions (mimicking cancerous masses and benign fibroadenomas, respectively) demonstrate the characteristic differences in the depiction of these inclusions on the shear strain elastograms.