Browsing by Subject "Acoustic radiation force"

Prostate cancer is the most common cancer and second-leading cause of cancer death among men in the United States. Early and accurate diagnosis of prostate cancer remains challenging; following an abnormal rectal exam or elevated levels of prostate-specific antigen in serum, clinical guidelines recommend transrectal ultrasound-guided biopsy. However, lesions are often indistinguishable from noncancerous prostate tissue in conventional B-mode ultrasound images, which have a diagnostic sensitivity of about 30%, so the biopsy is not typically targeted to suspicious regions. Instead, the biopsy systematically samples 12 pre-specified regions of the gland. Systematic sampling often fails to detect cancer during the first biopsy, and while multiparametric MRI (mpMRI) techniques have been developed to guide a targeted biopsy, fused with live ultrasound, this approach remains susceptible to registration errors, and is expensive and less accessible.

The goal of this work is to leverage ultrasound elasticity imaging methods, including acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI), to develop and optimize a robust 3-D elasticity imaging system for ultrasound-guided prostate biopsies and to quantify its performance in prostate cancer detection. Towards that goal, in this dissertation advanced techniques for generating ARFI and SWEI images are developed and evaluated, and a deep learning framework is explored for multiparametric ultrasound (mpUS) imaging, which combines data from different ultrasound-based modalities.

In Chapter 3, an algorithm is implemented that permits the simultaneous imaging of prostate cancer and zonal anatomy using both ARFI and SWEI. This combined sequence involves using closely spaced push beams across the lateral field of view, which enables the collection of higher signal-to-noise (SNR) shear wave data to reconstruct the SWEI volume than is typically acquired. Data from different push locations are combined using an estimated shear wave propagation time between push excitations to align arrival times, resulting in SWEI imaging of prostate cancer with high contrast-to-noise ratio (CNR), enhanced spatial resolution, and reduced reflection artifacts.

In Chapter 4, a fully convolutional neural network (CNN) is used for ARFI displacement estimation in the prostate. A novel method for generating ultrasound training data is described, in which synthetic 3-D displacement volumes with a combination of randomly seeded ellipsoids are used to displace scatterers, from which simulated ultrasonic imaging is performed. The trained network enables the visualization of in vivo prostate cancer and prostate anatomy, providing comparable performance with respect to both accuracy and speed compared to standard time delay estimation approaches.

Chapter 5 explores the application of deep learning for mpUS prostate cancer imaging by evaluating the use of a deep neural network (DNN) to generate an mpUS image volume from four ultrasound-based modalities for the detection of prostate cancer: ARFI, SWEI, quantitative ultrasound, and B-mode. The DNN, which was trained to maximize lesion CNR, outperforms the previous method of using a linear support vector machine to combine the input modalities, and generates mpUS image volumes that provide clear visualization of prostate cancer.

Chapter 6 presents the results of the first in vivo clinical trial that assesses the use of ARFI imaging for targeted prostate biopsy guidance in a single patient visit, comparing its performance with mpMRI-targeted biopsy and systematic sampling. The process of data acquisition, processing, and biopsy targeting is described. The study demonstrates the feasibility of using 3-D ARFI for guiding a targeted biopsy of the prostate, where it is most sensitive to higher-grade cancers. The findings also indicate the potential for using 2-D ARFI imaging to confirm target location during live B-mode imaging, which could improve existing ultrasonic fusion biopsy workflows.

Chapter 7 summarizes the research findings and considers potential directions for future research. By developing advanced ARFI and SWEI imaging techniques for imaging the prostate gland, and combining information from different ultrasound modalities, prostate cancer and zonal anatomy can be imaged with high contrast and resolution. The findings from this work suggest that ultrasound elasticity imaging holds great promise for facilitating image-guided targeted biopsies of clinically significant prostate cancer.

Cardiovascular disease is the leading cause of death for developed countries, including the United States. In order to diagnose and detect certain cardiac diseases, it is necessary to assess myocardial performance and function. One mechanical property that has been shown to reflect myocardial performance is myocardial stiffness. Acoustic radiation force impulse (ARFI) imaging has been demonstrated to be capable of visualizing variations in local stiffness within soft tissue.

In this thesis, the initial investigations into the visualization of myocardial performance with ARFI imaging are presented. In vivo ARFI images were acquired with a linear array placed on exposed canine hearts. When co-registered with the electrocardiogram (ECG), ARFI images of the heart reflected the expected changes in myocardial stiffness through the cardiac cycle. With the implementation of a quadratic motion filter, motion artifacts within the ARFI images were reduced to below 1.5 &mu m at all points of the cardiac cycle. The inclusion of pre-excitation displacement estimates in the quadratic motion filter further reduced physiological motion artifacts at all points of the cardiac cycle to below 0.5 &mu m.

In order for cardiac ARFI imaging to more quantitatively assess myocardial performance, novel ARFI imaging sequences and methods were developed to address challenges specifically related to cardiac imaging. These improvements provided finer sampling and improved spatial and temporal resolution within the ARFI images. In vivo epicardial ARFI images of an ovine heart were formed using these sequences, and the quality and utility of the resultant ARFI-induced displacement curves were examined.

In vivo cardiac ARFI images were formed of canine left ventricular free walls while the hearts were externally paced by one of two electrodes positioned epicardially on either side of the imaging plane. Directions and speeds of myocardial stiffness propagation were measured within the ARFI imaging field of view. In all images, the myocardial stiffness waves were seen to be traveling away from the stimulating electrode. The stiffness propagation velocities were also shown to be consistent with propagation velocities measured from elastography and tissue velocity imaging as well as the local epicardial ECG.

ARFI-induced displacement curves of an ovine heart were formed and temporally registered with left ventricular pressure and volume measurements. From these plots, the synchronization of myocardial stiffening and relaxation with the four phases (isovolumic contraction, ejection, isovolumic relaxation, and filling) of the cardiac cycle was determined. These ARFI imaging sequences were also used to correlate changes in left ventricular performance with changes in myocardial stiffness. These preliminary results indicated that changes in the ARFI imaging-derived stiffnesses were consistent with those predicted by current, clinically accepted theories of myocardial performance and function.

These results demonstrate the ability of ARFI imaging to visualize changes in myocardial stiffness through the cardiac cycle and its feasibility to provide clinically useful insight into myocardial performance.

Acoustic radiation force impulses (ARFI) have been used to generated transverse-traveling mechanical waves in various biological tissues. The velocity of these waves is related to a medium's stiffness and thus can offer useful diagnostic information. Consequently, shear wave velocimetry has the potential to investigate cardiac disease states that manifest themselves as changes in tissue stiffness (e.g., ischemia).

The work contained herein focuses on employing ARFI-based shear wave velocimetry techniques, similar to those previously utilized on other organs (e.g., breast, liver), for the investigation of cardiac tissue. To this end, ARFI excitations were used to generate slow-moving (under 3 m/s) mechanical waves in exposed myocardium (with access granted through a thoracotomy); these waves were then tracked with ultrasonic methods. Imaging techniques to increase frame-rate, decrease transducer/tissue heating, and reduce the effects of physiological motion were developed. These techniques, along with two shear wave velocimetry methods (i.e., the Lateral Time-to-Peak and Radon sum transformation algorithms), were utilized to successfully track shear wave propagation through the mid-myocardial layer in vitro and in vivo. In vitro experiments focused on the investigation of a shear wave anisotropy through the myocardium. This experimentation suggests a moderate shear wave velocity anisotropy through regions of the mid-myocardial layer. In vivo experiments focused on shear wave anisotropy (which tend to corroborate the aforementioned in vitro results), temporal/spatial stability of shear wave velocity estimates, and estimation of wave velocity through the cardiac cycle. Shear wave velocity was found to cyclically vary through the cardiac cycle, with the largest estimates occurring during systole and the smallest occurring during diastole. This result suggests a cyclic stiffness variation of the myocardium through the cardiac cycle. A novel, on-axis technique, the displacement ratio rate (DRR) method, was developed and compared to conventional shear wave velocitmetry and ARFI imaging results; all three techniques suggest a similar cyclic stiffness variation.

Shear wave velocimetry shows promise in future investigations of myocardial elasticity. The DRR method may offer a means for transthoracic characterization of myocardial stiffness. Additionally, the future use of transesophageal and catheter-based transducers presents a way of generating and tracking shear waves in a clinical setting (i.e., when epicardial imaging is not feasible). Lastly, it is hoped that continued investigations into the physical basis of these ARFI-generated mechanical waves may further clarify the relationship between their velocity in myocardium and material stiffness.

Radiofrequency ablation (RFA) is commonly used to treat cardiac arrhythmias, by generating a series of discrete RFA lesions in the myocardium to isolate arrhythmogenic conduction pathways. The size of each lesion is controlled by the temperature of the tissue at the surface or the duration of RF power delivery, but feedback on the extent and transmurality of the generated lesion are unavailable with current technology. Intracardiac Echocardiography (ICE) may provide a solution through Acoustic Radiation Force Impulse (ARFI) imaging or Shear Wave Imaging (SWI), which each generate images of local mechanical compliance from very small ultrasonically-induced waves. This work compares ARFI and SWI in an ex-vivo experiment for lesion boundary assessment and lesion gap resolution.

The rupture of arterial plaques is the most common cause of ischemic complications including stroke, the fourth leading cause of death and number one cause of long term disability in the United States. Unfortunately, because conventional diagnostic tools fail to identify plaques that confer the highest risk, often a disabling stroke and/or sudden death is the first sign of disease. A diagnostic method capable of characterizing plaque vulnerability would likely enhance the predictive ability and ultimately the treatment of stroke before the onset of clinical events.

This dissertation evaluates the hypothesis that Acoustic Radiation Force Impulse (ARFI) imaging can noninvasively identify lipid regions, that have been shown to increase a plaque's propensity to rupture, within carotid artery plaques in vivo. The work detailed herein describes development efforts and results from simulations and experiments that were performed to evaluate this hypothesis.

To first demonstrate feasibility and evaluate potential safety concerns, finite-element method simulations are used to model the response of carotid artery plaques to an acoustic radiation force excitation. Lipid pool visualization is shown to vary as a function of lipid pool geometry and stiffness. A comparison of the resulting Von Mises stresses indicates that stresses induced by an ARFI excitation are three orders of magnitude lower than those induced by blood pressure. This thesis also presents the development of a novel pulse inversion harmonic tracking method to reduce clutter-imposed errors in ultrasound-based tissue displacement estimates. This method is validated in phantoms and was found to reduce bias and jitter displacement errors for a marked improvement in image quality in vivo. Lastly, this dissertation presents results from a preliminary in vivo study that compares ARFI imaging derived plaque stiffness with spatially registered composition determined by a Magnetic Resonance Imaging (MRI) gold standard in human carotid artery plaques. It is shown in this capstone experiment that lipid filled regions in MRI correspond to areas of increased displacement in ARFI imaging while calcium and loose matrix components in MRI correspond to uniformly low displacements in ARFI imaging.

This dissertation provides evidence to support that ARFI imaging may provide important prognostic and diagnostic information regarding stroke risk via measurements of plaque stiffness. More generally, the results have important implications for all acoustic radiation force based imaging methods used clinically.

Prostate cancer (PCa) is the most common non-cutaneous cancer in men in the United States. Early detection of PCa is essential for improving treatment outcomes and survival rates. However, diagnosis of PCa at an early stage is challenged by the lack of an imaging method that can accurately visualize PCas. Because pathological processes change the mechanical properties of the tissue, elasticity imaging methods have the potential to differentiate PCas from other prostatic tissues. Acoustic radiation force impulse (ARFI) imaging is a relatively new elasticity imaging method that visualizes the local stiffness variations inside soft tissue.

The work presented in this dissertation investigates the feasibility of prostate ARFI imaging. Volumetric ARFI data acquisition and display methods were developed to visualize anatomic structures and pathologies in ex vivo human prostates. The characteristic appearances of various prostatic tissues in ARFI images were identified by correlating ARFI images with McNeal's zonal anatomy and the correlated histological slides, in which prostatic pathologies were delineated by a pathologist blinded to the ARFI images. The results suggest ARFI imaging is able to differentiate anatomic structures and identify suspicious PCa regions in the prostate.

To investigate the correlation between ARFI displacement amplitudes and the underlying tissue stiffness in the prostate ARFI images, the mechanical properties of prostatic tissues were characterized using a quantitative method, based upon shear wave elasticity imaging (SWEI). Co-registered ARFI and SWEI datasets were acquired in excised prostate specimens to reconstruct the shear moduli of prostatic tissues. The results demonstrated that variations in ARFI displacement amplitudes were inversely related to the underlying tissue stiffness; and the reconstructed shear moduli of prostatic tissues had good agreements with those reported in literature. The study suggests the matched ARFI and SWEI datasets provide complementary

information about tissue's elasticity.

To increase the efficiency of the data acquisition, a novel imaging sequence was developed to acquired matched ARFI-SWEI datasets without increasing the number of excitations compared to a conventional ARFI imaging sequence. Imaging parameters were analyzed both theoretically and experimentally. An analytical model was derived to quantify the fundamental accuracy limit in the reconstructed shear modulus, and demonstrated good agreement with the experimental data. The novel sequence was demonstrated in tissue-mimicking phantoms.

Finally, ARFI imaging sequences were developed in a transrectal probe, and ARFI images were presented from in vivo data acquired in patients under radical prostatectomy. The in vivo ARFI images demonstrated decreased contrast and resolution as compared to the matched ex vivo ARFI data. However, prostate anatomy and some PCa were successfully visualized in the in vivo ARFI images. Thus, we conclude that ARFI imaging has the potential to provide image guidance for locating cancerous regions during PCa diagnosis and treatment.

Implemented on an intracardiac echocardiography transducer, acoustic radiation force methods may provide a useful means of characterizing the heart's elastic properties. Elasticity imaging may be of benefit for diagnosis and characterization of infarction and heart failure, as well as for guidance of ablation therapy for the treatment of arrhythmias. This thesis tests the hypothesis that with appropriately designed imaging sequences, intracardiac acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) are viable tools for quantification of myocardial elasticity, both temporally and spatially. Multiple track location SWEI (MTL-SWEI) is used to show that, in healthy in vivo porcine ventricles, shear wave speeds follow the elasticity changes with contraction and relaxation of the myocardium, varying between 0.9 and 2.2 m/s in diastole and 2.6 and 5.1 m/s in systole. Infarcted tissue is less contractile following infarction, though not unilaterally stiffer. Single-track-location SWEI (STL-SWEI) is proven to provide suppression of speckle noise and enable improved resolution of structures smaller than 2 mm in diameter compared to ARFI and MTL-SWEI. Contrast to noise ratio and lateral edge resolution are shown to vary with selection of time step for ARFI and arrival time regression filter size for STL-SWEI and MTL-SWEI.

In 1.5 mm targets, STL-SWEI achieves alternately the tightest resolution (0.3 mm at CNR = 3.5 for a 0.17 mm filter) and highest CNR (8.5 with edge width = 0.7 mm for a 0.66 mm filter) of the modalities, followed by ARFI and then MTL-SWEI.

In larger, 6 mm targets, the CNR-resolution tradeoff curves for ARFI and STL-SWEI overlap for ARFI time steps up to 0.5 ms and kernels $\leq$1 mm for STL-SWEI. STL-SWEI can operate either with a 25 dB improvement over MTL-SWEI in CNR at the same resolution, or with edge widths 5$\times$ as narrow at equivalent CNR values, depending on the selection of regression filter size. Ex vivo ablations are used to demonstrate that ARFI, STL-SWEI and MTL-SWEI each resolve ablation lesions between 0.5 and 1 cm in diameter and gaps between lesions smaller than 5 mm in 3-D scans. Differences in contrast, noise, and resolution between the modalities are discussed. All three modalities are also shown to resolve ``x''-shaped ablations up to 22 mm in depth with good visual fidelity and correspondence to surface photographs, with STL-SWEI providing the highest quality images. Series of each type of image, registered using 3-D data from an electroanatomical mapping system, are used to build volumes that show ablations in in vivo canine atria. In vivo images are shown to be subject to increased noise due to tissue and transducer motion, and the challenges facing the proposed system are discussed. Ultimately, intracardiac acoustic radiation force methods are demonstrated to be promising tools for characterizing dynamic myocardial elasticity and imaging radiofrequency ablation lesions.

Hepatocellular carcinoma (HCC) lesions are often preceded by liver cirrhosis or Hepatitis C, and patients with these diagnoses are monitored every six months with an ultrasound screening. B-mode ultrasound is an ideal imaging modality for regular screening; however, ultrasound has demonstrated a low sensitivity for detecting small, early stage HCCs. Studies using ultrasonic elasticity methods have shown increased HCC lesion contrast compared to B-mode ultrasound. This thesis presents the preliminary work of shear wave elasticity imaging (SWEI) methods to improve estimates of viscoelastic parameters in the context of liver screening for tumors, with the goals of (1) using multi-dimensional directional filtering and shear wave reconstruction to reduce reflection artifacts, (2) evaluating bias introduced from small depth-of-field (DOF) excitations into frequency dependent shear wave speed (SWS) and attenuation estimates and (3) evaluating the feasibility of combining on-axis and off-axis elasticity methods to screen the entire liver.

Interfaces of different shear stiffness causes propagating shear waves to be reflected, which can lead to artifacts in SWS reconstructions due to the reflections both in and out of the imaging plane. Two-dimensional (2-D), three-dimensional (3-D), and four- dimensional (4-D) directional filters were applied to shear wave data, and SWS images were reconstructed with 2-D and 3-D shear wave reconstruction methods to quantify the reduction in image artifacts. For 2-D SWS image reconstructions, 3-D directional filters showed greater improvements in image quality than 2-D filters, and 4-D directional filters showed marginal improvement over 3-D filters. The 4-D directional filters have the largest impact in reducing reflection artifacts in 3-D SWS volumes.

Commercial scanners reconstruct shear wave speeds for a region of interest using time-of-flight (TOF) methods reporting a single SWS (or elastic modulus) to the end user under the assumptions that tissue is elastic and independent of frequency. Human tissues are known to be viscoelastic (VE), resulting in dispersion and attenuation. Existing methods to quantify shear wave dispersion and attenuation commonly make an assumption that the acoustic radiation force excitation acts as a cylindrical source with a known geometric shear wave amplitude decay. The bias in shear dispersion and attenuation estimates associated with making this cylindrical wave assumption (up to 15% for dispersion and 41% for attenuation) when applied to shear wave sources with finite depth extents in realistic focal geometries is greater for more tightly-focused acoustic radiation force sources with smaller DOF.

Curvilinear transducers are the standard probe used in ultrasound HCC screenings; however, previous studies using curvilinear arrays performing liver SWEI have been limited by penetration depth of the acoustic radiation force excitation. In order for SWEI to be feasible as an imaging method to screen for and detect HCC lesions, large, low frequency arrays designed for deep abdominal imaging must be used. A prototype low frequency deep abdominal curvilinear array and a proposed low frequency matrix array were simulated, and the combined dynamic on-axis response in the region of excitation (ROE) and the propagating shear wave (off-axis) response were used to create quantitative shear wave images of a spherical lesion using sparse acoustic radiation force excitations. The on-axis behavior in the ROE is related to the underlying stiffness of the material and a lookup table (LUT) approach can be used to determine a SWS in this region. Combined with traditional off-axis TOF methods, the combined methods can reduce the sparsity that would otherwise occur inside the ROE, which allows a larger field-of-view (FOV) to be interrogated with the same number of excitations. The on-axis and off-axis methods can be combined in either 2-D or 3-D reconstructions. The performance of the deep abdominal curvilinear array was comparable to the matrix array for 2-D SWEI imaging. A benefit of the curvilinear array over a large matrix array is its ability to image through intercostal acoustic windows. If there is not a sufficient subcostal acoustic window to use the matrix array to image the entire liver, a combination of both the matrix array and the curvilinear array can be used to scan the entire organ.

Diagnostic ultrasound imaging is sometimes unable to yield clinically useful data. This is caused by the presence of body walls. This thesis examines how body walls impacts the propagation of ultrasound in the human body and searches for ways to improve image quality.Previous works have demonstrated remarkable improvements in tissue stiffness quantification with the use of elevated Mechanical Index (MI) during ultrasonic harmonic shear wave elasticity imaging (SWEI). Since ultrasound SWEI sequences consist of a long push pulse and a short tracking pulse, it remains unclear which one of the two pulses is impacted more by the body wall and benefits more from elevated MI. In Chapter 3, an opposing window experiment is devised and built to isolate the impacts of the body wall on push and track beams. Track beams are found to be more affected by the presence of body walls and to benefit from higher MI transmits. In Chapter 4, 3D nonlinear ultrasound simulations and experimental measurements were used to estimate the range of in situ pressures that can occur during transcutaneous abdominal imaging, and to identify the sources of error when estimating in situ peak rarefaction pressures (PRP) using linear derating as specified by the MI guideline. Using simulations, it was found that for a large transmit aperture (F/1.5) MI consistently overestimated in situ PRP by 20-48%, due primarily to phase aberration. For a medium transmit aperture (F/3), the MI accurately estimated the in situ PRP to within 8%. For a small transmit aperture (F/5), MI consistently underestimated the in situ PRP by 32-50%, with peak locations occurring 1-2 cm before the focal depth, often within the body wall itself. The large variability across body wall samples and focal configurations demonstrates the limitations of the simplified linear derating scheme. The results suggest that patient specific in situ PRP estimation would allow for increases in transmit pressures, particularly for tightly focused beams, to improve diagnostic image quality while ensuring patient safety. Tissue harmonic signal quality has been shown to improve with elevated acoustic pressure. The peak rarefaction pressure (PRP) for a given transmit, however, is limited by the FDA guideline for the mechanical index (MI). In Chapter 4, We demonstrated that the MI overestimates in situ PRP for tightly focused beams in vivo due primarily to phase aberration. In Chapter 5, we evaluate two spatial coherence-based image quality metrics, short-lag spatial coherence (SLSC) and harmonic short-lag spatial coherence (HSC), as proxy estimates for phase aberration and assess their correlation with in situ PRP in simulations and experimentally when imaging through abdominal body walls. We demonstrate strong correlation between both spatial coherence-based metrics with in situ PRP (r2 = 0.77 for HSC, r2 = 0.67 for SLSC), an observation that could be leveraged in the future for patient-specific selection of acoustic output.

Tissue stiffness can be an indicator of various types of ailments. However, no standard diagnostic imaging modality has the capability to depict the stiffness of tissue. To overcome this deficiency, various elasticity imaging methods have been proposed over the past 20 years. A promising technique for elasticity imaging is acoustic radiation force impulse (ARFI) based shear wave imaging. Spatially localized acoustic radiation force excitation is applied impulsively to generate shear waves in tissue and its stiffness is quantified by measuring the shear wave speed (SWS).

The aim of this thesis is to contribute to both the clinical application of ARFI shear wave imaging and its technical development using the latest advancements in ultrasound imaging capabilities.

To achieve the first of these two goals, a pilot imaging study was conducted to evaluate the suitability of ARFI shear wave imaging for the assessment of liver fibrosis using a rodent model of the disease. The stiffness of severely fibrotic rat livers were found to be significantly higher than healthy livers. In addition, liver stiffness was correlated with fibrosis as quantified using collagen content.

Based on these findings, an imaging study was conducted on patients undergoing liver biopsy at the Duke University Medical Center. A robust SWS estimation algorithm was implemented to deal with noisy patient shear wave data using the random sample consensus (RANSAC) approach. RANSAC estimated liver stiffness was found to be higher in severely fibrotic and cirrhotic livers, suggesting that ARFI shear wave imaging may potentially be useful for the staging of severe

fibrosis in humans.

To achieve the second aim of this thesis, a system capable of monitoring ARFI induced shear wave propagation in 3D was implemented using a 2D matrix array transducer. This capability was previously unavailable with conventional 1D arrays. This system was used to study the precision of time-of-flight (TOF) based SWS estimation. It was found that by placing tracking beam locations at the edges of the SWS measurement region of interest using the 2D matrix array, TOF SWS precision could be improved in a homogeneous medium.

The 3D shear wave imaging system was also used to measure the SWS in muscle, which does not conform to the isotropic mechanical behavior usually assumed for tissue, due to the parallel arrangement of muscle fibers. It is shown that the SWS along and across the fibers, as well as the 3D fiber orientation can be estimated from a single 3D shear wave data-set. In addition, these measurements can be made independent of the probe orientation relative to the fibers. This suggests that 3D shear wave imaging can be useful for characterizing anisotropic mechanical properties of tissue.

Heart failure is one of the most common cardiac disorders and is projected to increase in prevalence over the next few decades. It can arise from a wide variety of root causes such as coronary artery disease, hypertension, cardiomyopathy, or cardiotoxicity and can manifest as systolic and/or diastolic dysfunction. Traditionally, its diagnosis has been based on monitoring qualitative changes in cardiac structure, such as chamber geometry and wall motion patterns, or quantitative changes in indices of cardiac function, such as the blood flow velocities and ventricular ejection fraction. These parameters are assessed in clinical settings using medical imaging modalities like ultrasound and magnetic resonance imaging. Recent research into cardiac pathophysiology has indicated that the progression of cardiac disease is often accompanied by changes in the mechanical properties of cardiac muscle. Interrogation of these changes could be used to gain useful diagnostic insight into the etiology of heart failure.

Acoustic radiation force (ARF)-based techniques, such as acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI), provide the means to measure mechanical properties of soft tissues using ultrasound. They operate on the principle that ultrasound can be used to remotely generate as well as track micron-level vibrations in the body and thus derive mechanical properties such as tissue stiffness. ARFI and SWEI have previously been shown to capture dynamic changes in myocardial stiffness in Langendorff set-ups, open-chest experiments, and intracardiac settings. This dissertation explores the challenges and opportunities of implementing acoustic radiation force-based methods for noninvasive applications via transthoracic imaging windows.

Transthoracic imaging of the heart using ultrasound can be challenging for a number of reasons. The two main sources of signal degradation that were hypothesized to impact ARFI and SWEI in this environment are acoustic clutter and intrinsic tissue motion. Acoustic clutter refers to incorrectly localized echoes which lead to the degradation of target conspicuity, border delineation, and image quality. Intrinsic tissue motion, on the other hand, impedes the ability to accurately measure the ARF-induced motion and consequently affects the estimation of tissue stiffness. The work presented herein focuses on quantifying the level of both sources of signal degradation under \textit{in vivo} imaging conditions and evaluating the effectiveness of strategies to minimize their impact. Lastly, the feasibility of tracking dynamic myocardial stiffness through the cardiac cycle via transthoracic imaging windows on human volunteers was investigated.

Harmonic imaging is often used to suppress acoustic clutter in clinical settings. Clutter levels are also closely tied to the choice of beamforming configuration used. Quantifying the impact of harmonic imaging and transmit beamforming (focused versus plane wave) on acoustic clutter, under \textit{in vivo} transthoracic imaging conditions is therefore important. Clutter level, for a given imaging scenario, was quantified using contrast between the cardiac chambers and the interventricular septum. Substantial variations in clutter levels were observed across as well as within volunteers. Harmonic imaging had a measurable impact in suppressing clutter under both the plane wave (2.97$\,$dB) and focused (6.1$\,$dB) configurations. However, even in the optimal configuration (harmonic-focused), clutter levels varied over a broad range (4$\,$-$\,$22$\,$dB). These results suggest that acoustic clutter, while consistently lowered through the use of harmonic imaging, is still likely to be a major detriment to transthoracic measurement of myocardial stiffness.

The heart exhibits complex and rapid three-dimensional motion; this could be a dominant confounder when attempting to measure micron-level ARF-induced displacements. Intrinsic cardiac motion of the interventricular septum, as observed through the parasternal long- and short-axis views, was analyzed in both the time- and frequency-domain. Two types of motion filters, frequency-based (high-pass filters) and recovery-based (polynomial filters) were compared to assess their ability to separate the axial component of cardiac motion from the ARF-induced motion. The effect of non-axial cardiac motion on speckle decorrelation was quantified using temporal coherence and related to the uncertainty of axial displacement estimation or jitter. High-pass filters with cutoffs $>$75$\,$Hz and quadratic polynomial filters were found to be equally effective at compensating for axial tissue motion. While high-pass filters are independent of a recovery-time assumption, they introduce a downward bias to measured ARF-induced motion; this bias increases with cutoff frequency. Temporal coherence was empirically related to measured displacement estimation jitter. At end-diastole, temporal coherence was high and jitter was low (0.5$\,$-$\,$2.5$\,\mu$m). In other phases of the cardiac cycle, however, jitter was found to increase dramatically with the span of the temporal window over which it was computed. Jitter for short spans, 2$\,$ms, was found to be in the range of 2$\,$-$\,$8$\,\mu$m, However, for spans of 10$\,$ms, it could be as high as 10$\,$-$\,$20$\,\mu$m. These results indicate that the noise-floor for micron-level axial displacement estimation in the myocardium via transthoracic imaging windows can be fairly high (compared to the magnitude of ARF-induced displacements) and can vary considerably over the cardiac cycle.

In the final study, M-mode ARFI imaging was performed on twelve healthy volunteers to track stiffness changes within the interventricular septum in the parasternal long- and short-axis views. Myocardial stiffness dynamics over the cardiac cycle were quantified using five indices: stiffness ratio, rates of relaxation and contraction, and time constants of relaxation and contraction. Yield of ARFI acquisitions was evaluated based on metrics of signal strength and tracking fidelity such as displacement signal-to-noise, signal-to-clutter level, temporal coherence of speckle, and spatial similarity within the region-of-excitation. These were quantified using the mean ARF-induced displacements over the cardiac cycle, the contrast between the myocardium and the cardiac chambers, the minimum correlation coefficients of RF signals (over a 2$\,$ms window), and the correlation between displacement traces across simultaneously-acquired azimuthal beams, respectively. Forty-one percent of ARFI acquisitions were determined to be \say{successful} using a mean ARF-induced displacement threshold of 1.5 $\mu$m. \say{Successful} acquisitions were found to have higher i) signal-to-clutter levels, ii) temporal coherence, and iii) spatial similarity compared to \say{unsuccessful} acquisitions. Median values of these three metrics, between the two groups, were measured to be 13.42$\,$dB vs. 5.42$\,$dB, 0.988 vs. 0.976, and 0.984 vs. 0.849, respectively. Signal-to-clutter level, temporal coherence, and spatial similarity were also found to correlate with each other. Across the cohort of healthy volunteers, stiffness ratio was measured to be 2.74$\,\pm\,$0.86; rate of relaxation was 7.82$\,\pm\,$4.69$\,$/s and contraction was -7.31$\,\pm\,$3.79$\,$/s; time constant of relaxation was 35.90$\,\pm\,$20.04$\,$ms, and contraction was 37.24$\,\pm\,$19.85$\,$ms. ARFI-derived indices of myocardial stiffness were found to be similar in both views.

In summary, despite the many challenges that are inherent to the transthoracic imaging environment, acoustic radiation force-based techniques were found to capture the dynamic trends of myocardial stiffness when appropriate conditions are met. Future work to improve the strength of ARF-excitations, better characterize or circumvent the influence of noise sources such as acoustic clutter and tissue motion, and explore the association between ARFI/SWEI-derived myocardial stiffness and traditional indices of cardiac function will be critical to realizing the diagnostic potential of acoustic radiation force-based ultrasound methods in clinical cardiology.