Session A04: Physical Acoustics

Microspheres, Metamaterials and Manipulation – A Survey of Physical Ultrasonics Research at the National Center for Physical Acoustics

Physical ultrasonics encompasses a broad range of topics from the physics of wave propagation to the physical effects of high intensity sound. This talk will describe multiple lines of research in ultrasonics performed at the National Center for Physical Acoustics at The University of Mississippi. The topics discussed include propagation in complex media, phononic crystals, metasurfaces, inertial cavitation and acoustic manipulation.

    

Acoustic Bubble: A Versatile Tool for Biomedical Lab-on-a-Chip Applications

A typical biomedical analysis process usually requires (1) multiple steps such as sample collection, sample preparation and analytes detection; (2) sophisticated equipment; and (3) professional personnel. All these weaknesses have become a burden to ever-increasing demands of rapid and reliable healthcare for individuals and society. In recent years, microfluidics has shown great potential to become a go-to solution, by which most of these steps can be realized in a single miniaturized device. Especially, by combining acoustics and microfluidics, we can achieve contactless operation with high controllability and high biocompatibility, which is well poised for solving the challenges in various biomedical research related to healthcare.

In this talk, Dr. Gao will present acoustic bubbles (gas/air interfaces) in microfluidic systems for various biomedical applications to address the challenges in fluid transportation, cell manipulation and disease treatment. The versatile functions of acoustic bubbles will be discussed with experimental examples. This talk will also cover the approaches to building various microfluidic biomedical devices using advanced manufacturing technologies and low-cost materials. Building on my background in acoustic microfluidics, bioapplications, and advanced manufacturing technologies, I will share my current work and future research in the development of innovative acoustic microfluidic systems for various biomedical applications.   

Hall effect of angular momentum-carrying acoustic waves interacting with metasurfaces

This research focuses on investigating the fundamental wave dynamics of acoustic Hall effect for angular momentum-carrying acoustic waves interacting with metasurfaces. The Hall effect was first studied in electric and magnetic fields, and then in optics, and is herein extended to acoustics. Metasurfaces can manipulate wavefronts in a desired way that natural materials cannot achieve and has found promising applications in acoustics in the past decades. By introducing metasurfaces, the phenomenon of acoustic Hall effect is herein studied by numerical simulations and experimental measurements.  This fundamental study of the acoustic Hall effect will help to understand the basis of the associated wave dynamics that can find applications like underwater communications and medical treatments. The fundamental physics of angular momentum-carrying acoustic waves interacting with metasurfaces will also inspire studies in the corresponding area in optics.

    

Toward the optimization of a diverging-wave acquisition sequence for pulse-echo ultrasound

Conventional ultrasound imaging utilizes a sequence of focused transmit (FT) beams swept across a region of interest (ROI). Plane-wave transmit (PWT) imaging uses unfocused, planar wavefronts at multiple angles, allowing for rapid compounding and retrospective focusing; however, PWT imaging is restricted by its limited region of overlapping insonification. Diverging-wave transmit (DWT) schemes have also been shown to increase frame rates by compounding beams with broad curvature using virtual sources. To compare the imaging capability among FT, PWT, and DWT schemes, we performed simulations in Field II. Data was simulated using a Verasonics P4-2v cardiac ultrasound probe geometry with N = 64 elements, width w = 0.25 mm, height h = 14 mm, and kerf = 0.05 mm. A center frequency of 2.75 MHz with 74% fractional bandwidth at -6 dB were used for all cases. An ROI was populated with 35 point scatterers at x(0,20) mm by z(5,35) mm with 5-mm spacing in x and z. The full synthetic aperture dataset was calculated for the P4-2v probe and the defined medium. Each imaging case was studied by applying the delay profiles for a fixed focus or virtual source regime. Results showed an increase in lateral resolution of point-spread functions (PSFs) at 15-mm depth for single PWT and DWT beams compared to FT, as expected. Analysis of the PSF contours for all 35 point scatters were used to determine the optimal parameters required to image within a given ROI for each case investigated.

    

Deep Machine Learning for Acoustic Path Length Characterization of Materials Using Acoustic Diffraction

Acoustic non-destructive testing is used in many fields for detection of objects or features of interest. This detection typically requires the decision of an experienced technician, and often the evaluation varies depending on the technician’s experience. This evaluation becomes even more challenging as the object decreases in size. In this study, we propose a Convolutional Neural Network (CNN) to categorize and approximate acoustic anomalies with an eye toward future application to micro-scale specimens such as biofilms. Data are generated by emitting a continuous sound wave at a controlled frequency of 2 MHz through metallic specimens of varying heights each containing an anomaly in the form of a hole of equal radius. Data are collected as the transmitted signal is sampled at several lateral locations on the opposite side of the specimen.  We have developed both a categorical and regression based CNN to analyze the acoustic signal in the frequency domain. Both CNNs take spectrograms representing the change in the amplitude, phase, or both over multiple observation points as input. The first CNN classifies the specimen in regards to four acoustic path length categories: small, medium, large, and largest. The second CNN analyzes the specimens and produces an estimation of the acoustic path length of the anomaly in radians. Both models perform with high accuracy, the categorical achieving upwards of 95% and the regression predicting within a fraction of a radian. With the performance of these models, we demonstrate that utilizing the transfer function to analyze acoustic diffraction patterns leads to the ability to extract, with great precision, features in the input signal that describe the nature of the anomaly.