Spectral strain estimation techniques for tissue elasticity imaging

Three-Dimensional Cardiac Strain Imaging in Healthy Children Using RF-Data

Strain imaging has been performed using 1D, 2D and 3D ultrasound data. Usually, strain is only estimated in the beam direction, because of the availability of phase information. However, strain estimates in other directions are also desired, since most tissues are anisotropic. Beam steering enables measurement of different projections of the 2D displacement field while using phase information. The full 2D displacement field and strain tensor can be derived using multiple projections. This study aims at deriving the full 2D strain tensor by estimating only displacement along the beam while steering the beam at three angles, i.e. 0?, a positive, and a negative beam steering angle. Displacements were iteratively estimated using 2D cross-correlations. The 0? acquisition yielded the vertical displacement vector. Projection of the other measured displacements yielded the horizontal (lateral) vector. The full 2D strain tensor was derived using 1D least squares strain estimators. Since horizontal displacements and strains are usually the least accurate, these were first focused on. Horizontal displacements were estimated for simulated radiofrequency (rf) data of a block with a four times stiffer cylindrical inclusion before and after application of 2% vertical compression. Root mean squared error (RMSE) of the horizontal displacements were calculated for every symmetric pair of beam steering angles in the range of -30 to 30? with angular increments of 5?. The RMSE analysis was performed for signal-to-noise (SNR) levels of 5, 10, 20 and 50 dB. Beam steering combinations of about -25? and 25? were found to be most optimal. The technique's performance was maintained regardless of the SNR level. For an SNR of 5 dB a reduction in RMSE of up to 70% was observed. In analogy with the simulations, experiments were performed with a similar inclusion phantom made of polyvinyl alcohol. Using data from beam steering angles of -25?, 0? and 25? strain images were constructed for all strain components and compared to strain images constructed without beam steering. The horizontal and shear strain images improved clearly with beam steering. Finally, the technique was also applied to derive radial and circumferential strain images for a homogeneous vessel phantom made of gelatin. Radially, SNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> and CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> increased with 2.2 dB and 8 dB and circumferentially with 2.6 dB and 9.2 dB compared to non steered strain estimates. To conclude, the full 2D strain tensor can be accurately measured by using three beam steering angles.

Palpation has been widely used to detect hard tumorous tissues surrounded by softer normal tissues. The goal of ultrasound tissue elasticity imaging is to extract information regarding tissue stiffness that is closely related to pathology. For this tissue elasticity imaging, compression is applied first, and the amount of resulting tissue deformation or strain needs to be estimated. Traditionally, strain estimators aim to accurately derive tissue displacements between pre- and post-compression and compute strain from the displacements. However, the displacement can be as large as a thousand times of strain for typical compression levels used in ultrasound elasticity imaging. Error in displacement estimation leads to a large variance in strain, thus resulting in poor signal to noise ratio for the estimated strain. We have developed a novel strain estimator that directly estimates strain from the phase of temporal and spatial correlation instead of estimating small strain from large displacements. SNRe (signal to noise ratio of elastogram) and CNRe (contrast to noise ratio of elastogram) using the direct strain estimator are at least three times and six times larger than that using conventional displacement-based strain estimators, respectively. These results indicate that the direct strain estimator can significantly improve accuracy and lesion detectability in ultrasound elasticity imaging. In addition, the direct strain estimator is computationally efficient compared to conventional estimators, thus enabling the realtime implementation and clinical use of this new ultrasound imaging mode.

In this paper, two novel approaches, gradient-based and direct strain estimation techniques, are proposed for high-quality average strain imaging incorporating a cost function maximization. Stiffness typically is a continuous function. Consequently, stiffness of proximal tissues is very close to that of the tissue corresponding to a given data window. Hence, a cost function is defined from exponentially weighted neighboring pre- and post-compression RF echo normalized cross-correlation peaks in the lateral (for displacement estimation) or in both the axial and the lateral (for direct strain estimation) directions. This enforces a controlled continuity in displacement/strain and average displacement/strain is calculated from the corresponding maximized cost function. Axial stress causes lateral shift in the tissue. Therefore, a 1-D post-compression echo segment is selected by incorporating Poisson's ratio. Two stretching factors are considered simultaneously in gradient-based strain estimation that allow imaging the lesions properly. The proposed time-domain gradient-based and direct-strain-estimation-based algorithms demonstrate significantly better performance in terms of elastographic signal-to-noise ratio (SNRe), elastographic contrast-to-noise ratio (CNRe), peak signal-to-noise ratio (PSNR), and mean structural similarity (MSSIM) than the other reported time-domain gradient-based and direct-strain-estimation techniques in finite element modeling (FEM) simulation and phantom experiments. For example, in FEM simulation, it has been found that the proposed direct strain estimation method can improve up to approximately 2.49 to 8.71, 2.2 to 6.63, 1.5 to 5, and 1.59 to 2.45 dB in the SNRe, CNRe, PSNR, and MSSIM compared with the traditional direct strain estimation method, respectively, and the proposed gradient-based algorithm demonstrates 2.99 to 16.26, 18.74 to 23.88, 3 to 9.5, and 0.6 to 5.36 dB improvement in the SNRe, CNRe, PSNR, and MSSIM, respectively, compared with a recently reported time-domain gradient-based technique. The range of improvement as noted above is for low to high applied strains. In addition, the comparative results using the in vivo breast data (including malignant or benign masses) also show that the lesion size is better defined by the proposed gradient-based average strain estimation technique.

Since 2009, there have been ≥170 articles published in the cardiopulmonary literature on right ventricular (RV) echocardiographic strain imaging, including several in Circulation: Cardiovascular Imaging . Of particular interest has been the effort to use RV echocardiographic strain imaging to assess RV systolic function in pulmonary hypertension and the relationship of RV strain to disease severity and outcomes. In this issue of Circulation: Cardiovascular Imaging , Fine et al,1 from the Mayo Clinic echo group, report on retrospectively evaluated data on the relationship of RV strain to outcomes in the largest such study to date, encompassing 575 patients with known or suspected pulmonary hypertension without elevated pulmonary venous pressure during a median follow-up period of 16.5 months.1 The data show a strong association between reductions in mean longitudinal strain and all-cause mortality, and cardiopulmonary mortality and cardiopulmonary events. An exhaustive biostatistical analysis shows the relationship to be quantitative, stronger than that of other echo indices including tricuspid annular plane systolic excursion, independent of clinical covariates and resulting in a significant improvement in predictive value when combined with relevant clinical variables, as reflected in a significant increase in the integrated discrimination improvement index and the c-statistic for the primary end point. Thus, the results promise to improve clinical risk assessment significantly for such patients and may improve clinical decision making. Article see p 711 The apparent benefit is, in large part, a reflection of the relatively weak performance of older 2-dimensional (2D) echocardiographic indices of RV performance including fractional area change and tricuspid annular plane systolic excursion in this setting. RV imaging has been a minefield for echocardiography in adults since its inception. The complexity of ventricular shape, the thinness of the RV free wall, and the limitations in adults of transthoracic and subcostal windows in permitting full visualization …

Performance of Two Dimensional Displacement and Strain Estimation Techniques Using a Phased Array Transducer

Ultrasound strain imaging maps the tissue stiffness in the region of interest by estimating strain when the tissue is stressed. Prestress and poststress ultrasound echoes are processed to estimate the displacements, and the strain image is computed by a spatial derivative of the estimated displacement. Due to the nature of derivative operations, smaller errors in displacement estimation can cause large noise in the computed strain image. When tissue displacements are estimated using the phase of the autocorrelation between the prestress and poststress data (i.e., 1D autocorrelation method), it has been previously reported that local variations in ultrasonic center frequency due to speckle can introduce errors in the estimated displacement. We have developed a new method to compute strain based on two-dimensional autocorrelation. By estimating the local ultrasound frequency, we can improve the accuracy of displacement estimates and hence reduce the noise in strain images. We have analyzed the effect of local frequency changes on noise in strain images and the improvement in the strain signal-to-noise ratio with the 2D autocorrelation method. The simulation results are supported by experiments with homogenous gelatin phantoms and show that strain signal-to-noise ratio with 2D autocorrelation is consistently higher than that with 1D autocorrelation. The 2D autocorrelation can increase the strain signal-to-noise ratio by up to 200%, which leads us to believe that our estimation method can significantly improve the quality of strain images.

Intravascular Ultrasound (IVUS) is routinely used in interventional cardiology for imaging coronary plaque morphology. However, the use of B-mode images for tissue characterization and detection of vulnerable coronary plaques is limited. Strain imaging with ultrasound is a new modality that provides additional information for tissue characterization by imaging differences in tissue stiffness. The aim is to differentiate between vulnerable (soft) plaques and less dangerous calcified (hard) plaques. In this work, the applicability of a time efficient strain imaging algorithm in conjunction with data from IVUS array transducers is evaluated. Unfocused radiofrequency (rf) data from the transducer array is acquired using custom made hardware. Rf line reconstruction is performed offline by synthetic aperture focusing techniques. Vessel mimicking phantoms of different geometries and material stiffness are made from agar and Polyvinyl Alcohol Cryogel (PVA). Experiments are conducted in a water tank and a water column is used for applying intraluminal pressure differences required for strain imaging. The results show that strain images can be calculated with A-lines reconstructed from unfocused rf raw data. Regions of different stiffness can be identified qualitatively by local strain variations. With the used algorithm strains of up to 2% can be imaged without significant decor-relation.

We demonstrate the feasibility of ultrasound saline infused sonohysterography based strain imaging, for the determination of variations in the stiffness of uterine masses in-vivo. Strain images are estimated using a two-dimensional multilevel hybrid algorithm developed for ultrasound sector array transducers. Coarse displacements are initially estimated using envelope data, followed by guided finer displacement estimation using window lengths on the order of 4 wavelengths and 5A-lines on RF data. Strain images are obtained by estimating displacement slopes using least squares estimation. The strain images provide additional new information that may supplement current clinical B-mode imaging, used for diagnosis of uterine abnormalities. Stiffer masses such as fibroids are clearly differentiated on the strain images, which appear to be darker on the strain images with the characteristic decorrelation "halo" when compared to the normal uterine tissue. In addition, uterine polyps are visualized with a lighter grayscale when compared to the background myometrium, indicating the presence of a softer mass. Our results demonstrate the feasibility of improving clinical diagnosis based on strain imaging.

Ultrasound elastography in liver

BackgroundThe flexor pronator mass (FPM) is an important dynamic stabilizer to valgus stress at the elbow and has been reported to protect against ulnar collateral ligament (UCL) injury. Active gripping and pronation have demonstrated reduced ulnohumeral joint space and change in material properties of the UCL when examined in vivo via ultrasound. No studies have utilized ultrasonography and shear wave elastography to characterize the medial elbow’s response to FPM activation under valgus stress. This cross-sectional, repeated measures study aims to characterize medial elbow changes in UCL and FPM tissue stiffness and joint space width (JSW) during valgus stress with incremental FPM activation through gripping.MethodsThirteen participants (6 male, 7 female) aged 18–40 year with a BMI < 30 and no history of upper extremity injury were included in this study. Elbows were placed in a telos stress device at 30° of flexion and a 100N valgus stress was applied. Participants then activated the FPM by gripping a spherical dynamometer at 100, 75, 50, 25, and 0% of maximal grip strength. UCL thickness, ulnohumeral (UH) JSW, UCL stiffness, and FPM stiffness were measured for each condition and compared via a two-way repeated measures ANOVA and a post hoc Fischer’s Least Significant Difference test.ResultsMen and women showed no baseline differences in UCL thickness or UH JSW. JSW was significantly wider under valgus load, 2.22 ± 0.42 mm vs. 2.99 ± 0.46 mm in males and 2.15 ± 0.41 mm vs. 2.99 ± 0.55 mm in females (p < 0.05). No statistically significant differences were demonstrated in UH JSW by gripping force magnitude and differences by sex were not observed. Additionally, no significant changes in tissue stiffness were observed during dynamic conditions for shear wave velocities for either the UCL or FPM.ConclusionGripping does not change UH JSW or medial elbow tissue stiffness in the joint testing configuration and external loading conditions applied in this study. This suggests that gripping may not be as protective during the high valgus stress observed in baseball pitching as believed, and that the influence of FPM activity may be joint position or load dependent.

The rupture of atherosclerotic plaques represents one of the leading causes of morbidity and mortality. The aim of our study is to assess the morphology, functionality and composition of plaques formed in common carotid artery using a non-invasive in-vivo imaging technology, namely combined ultrasound, strain and photoacoustics. The imaging approach was tested ex-vivo on excised rabbit aortas (two with and two without plaques) using the integrated vascular imaging probe. The artery was flushed with blood at a pulsatile flow (1.5 Hz pulse rate and 50 m/s flow velocity). Ultrasound B-mode images and strain images, recorded at 296 frames per second, were obtained using a 12 MHz linear array transducer. For photoacoustic imaging the same ultrasound transducer was coupled with 19 optical fibers connected to OPO laser system (operated at 5 ns pulse duration, 590 nm and 700 nm spectral range). To analyze the advantages of the three imaging techniques, hyper- and hypoechogenic regions were separately isolated and the corresponding strain and photoacoustic signals were compared. Spatial variations in ultrasound echogeneicity are related to changes in arterial structure and function, i.e. hyperechogenic regions correspond to 15% or more decrease in peak strain rate and correlate with fibrotic tissue (as identified by photoacoustic images). The results of our study suggest that combined ultrasound, strain and photoacoustic imaging provides complementary information. Analysis of co-registered ultrasound, strain and photoacoustic images can be used to differentiate morphology, functionality and composition of plaques necessary for clinical diagnosis.

Ultrasound strain imaging has great importance as tumor or cancerous tissues are much stiffer than normal tissues. Estimations of strain calculating the time delays between consecutive ultrasound echo signals are common approaches in quasi-static elastography. Because of the ill posed nature of strain images, the ultrasound strain estimation still remains challenging. In this paper, we present an iterative strain estimator that uses mutual information between stochastic pre and post compressed radio frequency (RF) signals to define time delays. For each RF signal, a past and future is defined by cutting sample sequences into two parts. The time delay is considered to be the time shift between the cutting moments for which the mutual information between both past vectors and future vectors reaches a minimum. Displacement calculation with this time delay provides effective strain estimation for small strains. A synthetic phantom of tissue with tumor is modeled using the finite element method (FEM) and the pre-/post-compressed RF signals are generated using ultrasound simulation package Field II. Using these simulated RF data displacements and strain of deformation are calculated in MATLAB. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) are found better compared to conventional cross-correlation method. This algorithm can detect inclusions having echogenicity very close to that of the surrounding tissues and hence can be useful for detection of tumors or cancers.

Strain estimation techniques can provide valuable insights into regional cardiac (dys)function. However, a manual delineation of the left ventricle is required, intrinsically leading to a time consuming analysis with significant observer variability. In this study we therefore propose a fully automated strain estimation strategy with minimal user input, and illustrate its applicability in a realistic clinical setting. To test the proposed approach, volumetric data (32±3 Hz) was recorded in 3 healthy volunteers and 3 patients with acute myocardial infarction. User interaction time was considerably shorter than manual contouring (19±3s vs 390±90s) and eliminated observer variability. The fully automated strain estimation pipeline showed strain estimates in both subject groups that were in agreement with conventional analysis. Comparing the infarct regions against the remote regions in the patient group, end-systolic strain was reduced by 13.7%, 3.8% and 8.5% for the automated method, and by 12.3%, 5.0% and 9.0% if manually contoured, for the radial, longitudinal and circumferential strain component respectively. Furthermore, the strain curves of both investigated strategies matched well. The proposed automated approach is an attractive solution for clinical practice.

A novel two-way image quality assessment method is proposed for free-hand strain imaging. In elasticity imaging, tissue with different stiffness exhibit varying contrast in the strain images and detectability of a lesion is measured using elastographic contrast-to-noise ratio (CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> ). Representing quality of strain images quantitatively is vital for improving imaging techniques and also for clinical diagnosis. It avoids the subjective approach of interpreting strain images. Conventionally, contrast between stiff lesion and surrounding soft tissue is measured using contrast-to-noise ratio and strain image with the highest CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> amplitude is considered an optimal strain image. However experimental results have suggested that merely CNR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</sub> metric is often misleading and does not always represent the true elastic modulus contrast as the correlation coefficient falls below an acceptable levels and accuracy is compromised. Therefore in this study, the objective is to propose a comprehensive strain image quality assessment method which is reliable for clinical examinations and research.