Browsing by Subject "Ultrasound elastography"

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.

Shear wave elastography imaging (SWEI) of skeletal muscle is of great interest to the medical community, as there is a large need for a non-invasive, quantitative biomarker of muscle health that relates to muscle function. SWEI measures mechanical properties by generating quantitative images of tissue stiffness using an acoustic radiation force (ARF) excitation in the material and measuring the resulting shear waves that propagate outward. Most SWEI tools assume an isotropic, linear, elastic material, however skeletal muscle is commonly modeled as transversely isotropic (TI) due to the alignment of the muscle fibers. This means that shear wave speed (c, SWS) is dependent on the direction of the traveling shear wave relative to the fibers in skeletal muscle.

If muscle is assumed to be incompressible and transversely isotropic (ITI) it can be described with three parameters: the longitudinal shear modulus μ_L, the transverse shear modulus μ_T, and a single parameter combining longitudinal and transverse Young's moduli (E_L and E_T) called tensile anisotropy χ_E. In an elastic ITI material, there are two shear wave modes with different polarizations that can be excited: the shear horizontal (SH) and the shear vertical (SV). Shear moduli μ_L and μ_T can be measured solely based on the observation of the SH mode, however to quantify tensile anisotropy χ_E using SWEI, it is necessary to observe the SV mode. This thesis explores characterizing skeletal muscle as an ITI material and explores factors that affect the measurement of the SV mode wave. In order to evaluate the use of χ_E as a biomarker of muscle health we must understand the factors that affect its measurement using SWEI.

Chapter 3 demonstrates feasibility of measuring both the SH and SV modes using a 3D rotational SWEI system in the vastus lateralis muscle in vivo. We develop and validate methodology to estimate μ_L, μ_T, and χ_E and describe measurements these parameters in vivo.

Chapter 4 explores the factors that affect the SV mode waves, using Green's function simulations to perform a parametric analysis to determine the optimal interrogation parameters to facilitate visualization and quantification of SV waves in muscle. We evaluate the impact of five factors: μ_L, μ_T, and χ_E as well as fiber tilt angle θ_tilt and F-number of the push geometry on SV mode speed, amplitude, and rotational distribution.

Chapter 5 extends the work in Chapter 4 to understand SH and SV wave propagation in 3D by simulating multiple observation tilt angles and all 3 components of displacement. Tilting the observation plane to particular angles allows for maximization of the strength of the SH or SV waves, demonstrating that observation of these tilted planes in in vivo data would increase opportunities for estimation of SH and SV waves.

The work presented in this thesis explores using 3D SWEI to better characterize skeletal muscle as an ITI material, specifically by assessing the SH and SV mode shear wave speed. This work also investigates factors that affect measurement of SV mode waves, and thus the ability to estimate χ_E, towards a better understanding of χ_E for use as a potential biomarker of muscle health.