Kathryn Nightingale

This study demonstrates the implementation of a shear wave reconstruction algorithm that enables concurrent acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI) of prostate cancer and zonal anatomy. The combined ARFI/SWEI sequence uses closely spaced push beams across the lateral field of view and simultaneously tracks both on-axis (within the region of excitation) and off-axis (laterally offset from the excitation) after each push beam. Using a large number of push beams across the lateral field of view enables the collection of higher signal-to-noise ratio (SNR) shear wave data to reconstruct the SWEI volume than is typically acquired. The shear wave arrival times were determined with cross-correlation of shear wave velocity signals in two dimensions after 3-D directional filtering to remove reflection artifacts. To combine data from serially interrogated lateral push locations, arrival times from different pushes were aligned by estimating the shear wave propagation time between push locations. Shear wave data acquired in an elasticity lesion phantom and reconstructed using this algorithm demonstrate benefits to contrast-to-noise ratio (CNR) with increased push beam density and 3-D directional filtering. Increasing the push beam spacing from 0.3 to 11.6 mm (typical for commercial SWEI systems) resulted in a 53% decrease in CNR. In human in vivo data, this imaging approach enabled high CNR (1.61-1.86) imaging of histologically-confirmed prostate cancer. The in vivo images had improved spatial resolution and CNR and fewer reflection artifacts as a result of the high push beam density, the high shear wave SNR, the use of multidimensional directional filtering, and the combination of shear wave data from different push beams.

The Radon Transform (RT) approach is a common method used to estimate shear wave trajectory and speed in ultrasound shear wave elasticity imaging (SWEI). The RT calculates the sum of 2D spatiotemporal data amplitude under each potential linear trajectory to determine the trajectory with the greatest value. We divide the RT of data by the RT of a ones matrix to normalize by trajectory path length, enabling the use of arbitrary data masks. We demonstrate that masking can isolate the two simultaneous SH and SV shear wave modes observed in in vivo skeletal muscle data. 38 rotational SWEI acquisitions were collected in vastus lateralis muscle, for a total of 2736 space-time plots, and shear waves were identified by manually drawing masks. Using these labeled data, we trained a deep neural network to generate masks from a space-time plot to reduce the need to hand-draw masks in the future. On a held-out test case, 91% of predicted trajectories corresponded to a labeled shear wave, and estimated speeds had a mean absolute error of 7.6%. Despite frequent use of the RT method in the literature, no openly available code exists. We have released our code at https://github.com/fqjin/radon-transform.

We are investigating the potential for shear wave elasticity imaging (SWEI) derived material parameters to serve as biomarkers for skeletal muscle health. We consider muscle as a transversely isotropic (TI) material and use rotational 3D-SWEI acquisitions to characterize the shear wave propagation in directions along and across the muscle fibers at various passive stretch states. Data were collected in the vastus lateralis of the dominant leg of 10 healthy volunteers at various knee flexion angles (controlled by a BioDex system). The 3D-SWEI acquisitions were analyzed for both shear wave speed and amplitude in the directions along and across the muscle fibers. Relative to the values at 45° knee flexion, the shear wave speed along the fibers changed an average of +78% at 105° knee flexion and -11% at 0° knee flexion, while the shear wave speed across the fibers changed + 17% at 105° knee flexion, with no clear change at 0° knee flexion. These values support the need to control for subject positioning (joint angle) during skeletal muscle SWEI. Shear wave amplitude along the fibers increases by +110% from 45° to 105° knee flexion and changes by -43% from 45 to 0° knee flexion. Interestingly, in the direction along the fibers, the shear wave amplitude increases with increasing flexion even while shear wave speed increases over this same flexion range, opposite the trend expected from isotropic materials. Across the fibers, there is no clear trend in shear wave amplitude from 45 to 105° knee flexion, but amplitude changes by an average of +52% at 0° knee flexion relative to 45° knee flexion. This observation could provide insight for optimization of in vivo imaging protocols and understanding the higher order properties of skeletal muscle. Additionally, we explore the quantification of muscle's response to stretch through the rate of change in SWS with knee flexion at flexion angle above 45° and find linear fits with high R-squared values and slopes of 0.023 ± 0.0034 m/s/°.

There is increasing interest in using ultrasound shear wave elasticity imaging to study tissues described as incompressible, transversely isotropic (ITI) materials, such as skeletal muscle. In silico modeling helps us predict and understand shear wave behavior in complex materials like the ITI model, which supports two shear polarizations with different, direction-dependent propagation speeds. Existing techniques, the finite element method (FEM) and Greens functions, are computationally expensive and generate large file sizes. Physics-informed neural networks (PINNs) is a relatively novel technique to solve partial differential equations and produces solutions that are compressed, analytic, and free of space-time discretization. Here, we solve the 3D wave equation for an ITI material using PINNs and show that solutions match FEM simulations to first order for material parameters based on skeletal muscle. Estimated shear wave speeds for the PINN and FEM solutions differed by an average of 4.7%. Unlike the FEM simulation, the PINN solution had no reflection artifacts at the boundaries. Second-order differences in frequency content and amplitude distribution suggest the need for further validation. PINNs can enable rapid exploration of the complex shear wave behavior in ITI materials and can be extended to different material models by adjusting the wave equation and initial conditions.

We have developed a 3D acoustic radiation force impulse (ARFI) imaging system that screens for prostate cancer and provides imaging guidance for a targeted biopsy in a single clinic visit. ARFI and B-mode volumes are acquired by using a rotation stage to sweep a side-fire transrectal ultrasound probe in elevation. After identifying regions of suspicion in the image volumes, the transducer is rotated to each target and a transperineal biopsy is performed. This work describes the data acquisition, processing, and biopsy targeting processes of this system, presents preliminary results from a clinical study assessing the use of ARFI imaging to guide a targeted biopsy, and discusses some challenges and future work.