Passive acoustic mapping utilizing optimal beamforming in ultrasound therapy monitoring

Abstract

Passive acoustic mapping (PAM) is a promising imaging method that enables real-time three-dimensional monitoring of ultrasound therapy through the reconstruction of acoustic emissions passively received on an array of ultrasonic sensors. A passive beamforming method is presented that provides greatly improved spatial accuracy over the conventionally used time exposure acoustics (TEA) PAM reconstruction algorithm. Both the Capon beamformer and the robust Capon beamformer (RCB) for PAM are suggested as methods to reduce interference artifacts and improve resolution, which has been one of the experimental issues previously observed with TEA. Simulation results that replicate the experimental artifacts are shown to suggest that bubble interactions are the chief cause. Analysis is provided to show that these multiple bubble artifacts are generally not reduced by TEA, while Capon-based methods are able to reduce the artifacts. This is followed by experimental results from in vitro experiments and in vivo oncolytic viral therapy trials that show improved results in PAM, where RCB is able to more accurately localize the acoustic activity than TEA.

A multi-frequency sparse hemispherical ultrasound phased array for microbubble-mediated transcranial therapy and simultaneous cavitation mapping

“…For example, one study employed sparse-aperture weighted beamforming (Kozick & Kassam, 1991) with a 2D boundary array (2.4 cm×2.4 cm aperture) (Coviello, et al, 2012) and demonstrated a marginal improvement (22 % decrease laterally, 25 % decrease axially) in the resulting size of the free-field PSF compared to that found using TEA. Another study by the same group employed robust capon beamforming (Stoica, et al, 2003) with a 1D linear array (3.8 cm aperture) (Coviello, et al, 2015) and demonstrated a reduction the presence of tail artifacts that can occur using TEA with small-aperture arrays when bubble clouds are present within the transmit volume (Coviello, et al, 2013). However, these tail artifacts have not been observed when large-aperture receiver arrays are employed, either in simulations (Jones, et al, 2013) or experiments (O'Reilly, et al, 2014; Jones, et al, 2015; Jones & Hynynen, 2016).…”

Quantitative Frequency-Domain Passive Cavitation Imaging

“…For both images, there are pixels outside the tube with an amplitude that is within −3 dB of the maximum pixel value in the image. This strong signal outside of the tube would still be seen if the images were shown on a linear scale [33], [40], [52], [53]. …”

“…Stoica et al [88] developed the robust Capon beamformer (RCB) that allowed the distance between each element and the pixel to vary by a set parameter, ε , when computing the apodization weights. Coviello et al [40] utilized the Capon beamformer and the RCB for passive cavitation imaging in a flow phantom perfused with Sonovue microbubbles in vitro . The Capon beamformer mapped cavitation activity predominantly outside the flow phantom due, in part, to an inactive element on the imaging array that confounded the Capon beamformer algorithm.…”

“…The Capon beamformer mapped cavitation activity predominantly outside the flow phantom due, in part, to an inactive element on the imaging array that confounded the Capon beamformer algorithm. The passive cavitation images created via the RCB significantly reduced the axial elongation artifacts compared to the delay, sum, and integrate method [40]. Drawbacks to the RCB are that it is more computationally intensive than traditional delay and sum algorithms and requires empirical determination of ε .…”

“…The imaging algorithm has also been modified to account for aberration [51]–[54] and to improve the image resolution [40], [55], [56]. These studies have all relied on relative time-of-flight information, which allows the image resolution to be independent of the therapy pulse shape.…”