A 3-D Multiparametric Ultrasound Elasticity Imaging System for Targeted Prostate Biopsy Guidance

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2023

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Abstract

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.

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Chan, Derek Yu Xuan (2023). A 3-D Multiparametric Ultrasound Elasticity Imaging System for Targeted Prostate Biopsy Guidance. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/27716.

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