Acoustic Imaging Research Articles

Ultrasound identification of the cementoenamel junction and clinical correlation through ex vivo analysis.

Accurately identifying periodontal landmarks via acoustic imaging is increasingly important. Here, we evaluated the accuracy of cementoenamel junction (CEJ) identification using ultrasound by comparing it to clinical methods in 153 extracted human teeth. The distance between the CEJ to a reference point was measured using two clinical methods (visual examination and tactile sensation) as well as ultrasound imaging. Statistical analyses were performed across all teeth and sub-groups, including tooth types (incisors, cuspids, and molars/premolars), and two classifications: A- vs. B- (visually detectable or undetectable CEJ, respectively); and CL-S vs. CL-D (shallow or deep cervical lesions). In A- or CL-S teeth, ultrasound measurements highly agreed with clinical measurements, showing a 1.72-mm 95% CI for A- and 1.99-mm 95% CI for CL-S compared to visual examination, and a 1.77-mm 95% CI for A- and a 2.10-mm 95% CI for CL-S compared to tactile sensation, respectively. For 80% of A- and 76% of CL-S teeth, the difference between ultrasound and visual examination was within ± 20%. For 81% of A- and 80% of CL-S teeth, the difference between ultrasound and tactile sensation was within ± 20%. The variance of ultrasound versus clinical CEJ identifications showed a significant correlation (r = 0.6607) to the cervical lesion depth. The errors between ultrasound and clinical measurements show no significant bias across different tooth types.

Converging acoustic waves through complex media with a four-phase metalens

Wavefront shaping is an established solution to converge acoustic waves through complex media. Discrete phase sampling is inevitable in practice because of manufacture resolution. Limited phase sampling undermines focusing performance, which is hardly improved by increasing the number of phase sections. We propose that such loss can be alleviated when the modulator is designed directly on discrete phases. The feedback-based wavefront shaping with the genetic algorithm has been taken here. The performance of the optimized four-phase metalens catches up with the continuous one and is robust to variations in complex media. Experiments have confirmed its function. These results have potential applications in acoustic focusing and imaging through complex media.

Acoustic Imaging Learning-Based Approaches for Marine Litter Detection and Classification

This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

Acoustic imaging of geometrically shielded sound sources using tailored Green's functions.

In light of the growing market of urban air mobility, it is crucial to accurately detect the stationary or moving noise sources within the complex scattering environments caused by aircraft structures such as airframes and engines. This study combines conventional and wavelet-based beamforming techniques with an acoustic scattering prediction method to develop an acoustic imaging approach that considers scattering effects. Tailored Green's function is numerically evaluated and used to compute the steering vectors and the specific delayed time used in those beamforming methods. By examining common scenarios where a scatterer is positioned between the source plane and the array plane, it is observed that beamforming in a scattering environment differs from that in free space, leading to improved resolution alongside scattering-induced side lobes. The effectiveness of the developed method is validated through numerical simulations and experimental studies, confirming its improved ability to localize both stationary and rotating sound sources in a shielded environment. This advancement offers effective techniques for acoustic measurement and fault monitoring in the presence of structural scatterers.

A theoretical analysis of the logging-while-drilling dipole acoustic reflection measurement

Post-drilling wireline acoustic single-well imaging technology can now detect geological structures tens of meters away from boreholes. Further development of this single-well imaging technology in the logging-while-drilling (LWD) environment will have significant values in real-time applications such as geosteering and reservoir navigation. Based on the wireline imaging application, we propose a new method for the LWD application. In wireline imaging, the four-component (4C) dipole acoustic data are azimuthally rotated to scan the reflectors around the borehole. In LWD, azimuthal scanning is achieved by drilling rotation such that the 4C dipole system in the wireline is replaced by a one-dipole-source and two-receiver LWD system, where the two receivers are mounted on opposite sides of the drill collar. For the LWD application, we first developed the theory for LWD dipole shear-wave reflection imaging and validated the theory using 3D finite-difference waveform modeling. Using the analytical solution, we analyzed the far-field radiation directivity of an acoustic LWD dipole source and the effect of drilling rotation on the shear-wave reflection imaging using the LWD acoustic system. The LWD analysis results show that, for fast formations, the SH-wave is the dominant component for imaging, whereas for slow formations, the P-wave becomes important and can be used for imaging. Our results also indicate that the reflection data acquired by the system are affected by the speed of drilling rotation. The take-off azimuth at the wave radiation may be different from the incident azimuth at the wave reception. Knowing the rotation speed, this azimuth difference can be corrected. A further advantage of using the oppositely mounted receivers is that the reflected wave arrives earlier (later) at the front (back)-side receiver; thus, the arrival time difference between the receivers can be used to eliminate the 180°-azimuth ambiguity of dipole acoustic imaging. For reflection imaging, using the proposed LWD system configuration, we tested its azimuth sensitivity and validated its 180°-ambiguity solution using synthetic LWD and field wireline dipole data. The results of this work, therefore, provide a theoretical foundation for the development of the LWD acoustic reflection imaging system.

3-D Visualization of Atlantic salmon skin through Ultrasound and Photoacoustic Microscopy.

Three-dimensional photoacoustic imaging (PAM) has emerged as a promising technique for non-invasive label-free visualization and characterization of biological tissues with high spatial resolution and functional contrast. The application of PAM and ultrasound as a microscopy technique of study for Atlantic salmon skin is presented here. A custom ultrasound and photoacoustic experimental setup was used for conducting this experiment with a sample preparation method where the salmon skin is embedded in agarose and lifted from the bottom of the petridish. The results of C-scan, B-scan, and overlayed images of ultrasound and photoacoustic are presented. The results are then analyzed for understanding the pigment map and its relation to salmon behavior to external stimuli. The photoacoustic images are compared with the optical images and analyzed further. A custom colormap and alpha map is designed and the matrices responsible for PAM and ultrasound are inserted together to overlay the ultrasound image and PAM image on top of each other. In this study, we propose an approach that combines scanning acoustic microscopy (SAM) images with PAM images for providing a comprehensive understanding of the salmon skin tissue. Overlaying acoustic and photoacoustic images enabled unique visualization of tissue morphology, with respect to identification of structural features in the context of their pigment distribution.

Simulation and Investigation of Si-Based Piezoelectric Micromachined Ultrasonic Transducer (PMUT) Performances

Micro-electromechanical system (MEMS) based piezoelectric ultrasonic transducers for acoustic imaging of the surroundings are known as piezoelectric micromachined ultrasonic transducers (PMUTs). This research proposes a structural design of the PMUT with four fixed-guided beams. The beam is subjected to lateral loads, with vectors that are perpendicular to the longitudinal axis. This project simulated Piezoelectric Micromachined Ultrasonic Transducer (PMUT) with three different material properties i.e. Aluminium Nitride (AlN), Lead zirconate titanate (PZT) and Zinc Oxide (ZnO). Based on the study, it was found that reducing the beam dimensions and increasing the plate size will result in the first mode frequency reduction from 1.33x107 Hz to 3.74x106 Hz. Other than that, it was found that AlN PMUT experienced the maximum deflection of 6.3413 to 6.3478 μm when the loads applied in the range of 50 to 200 μN/m2. When the piezoelectric material changed to PZT, we obtained the maximum deflections of 0.3771 to 0.3786 μm when the same loads range applied to the PMUT. As for the ZnO PMUT, the maximum deflections obtained were in between 0.1702 μm to 0.1772 μm with the loads are maintained as in the loads applied to the AlN and PZT. This study proved the significant impact of altering the structural dimensions and material properties of PMUTs on their operational characteristics, specifically the first mode frequency and deflection behavior.

Robotically steerable intraluminal imaging probe with side-viewing 3D photoacoustic computed tomography.

This Letter presents, for the first time to our knowledge, a continuum robotic-steered endoluminal volumetric photoacoustic tomography probe. With regard to the design of the probe, a coaxial optical and acoustic imaging system has been developed that employs a custom designed piezoelectric micromachined ultrasonic transducer (PMUT). The dimensions of the system are constrained within a cylindrical housing with a diameter of 12 mm and a length of 30 mm. In regard to the robot design, the motion capabilities of the continuum robot have been successfully modeled and characterized. Based on the proposed system, the potential for early detection and screening of gastrointestinal cancers through phantoms has been validated. The experimental results demonstrate that the robotic-assisted photoacoustic system performs well in localizing unknown lesions and characterizing deep-seated abnormalities, exhibiting promising clinical applications.

A comparison of the acoustic waves generated in proton and carbon ion therapy

Hadron therapy, which employs particles such as protons and carbon-ions, is a promising method of cancer treatment due to its unique ability to deliver maximum energy at the Bragg peak near the tumor, sparing surrounding healthy tissue. Ionoacoustic waves, generated by thermal expansion from electronic collisions and localized heating, can be detected to optimize dose delivery and verify particle range, thus improving treatment precision. These waves offer a unique opportunity for comparative studies of different particle therapies. In this study, a mathematical model and computational simulations are used to compare the characteristics of ionoacoustic waves generated in tissue by proton and carbon-ion beams. In particular, we assess the impact of the nuclear fragmentation tail on the ionoacoustic signals generated in carbon-ion therapy. Our approach will allow us to make some important observations to study the comparative effects of proton and carbon-ion therapy. The aim of this work is to perform a comprehensive comparative analysis of ionoacoustic waves from proton and carbon-ion treatments, focusing on their potential for in-vivo range verification. This research addresses the current gap in understanding the use of ionoacoustic signals for range verification in ion beam therapy, which is critical given the growing clinical application of carbon ion therapy and its under-explored acoustic properties. This study pioneers the feasibility of using acoustic imaging from carbon-ion beams to detect the Bragg peak position and measure tumor dose in real-time. Carbon-ion dose mapping and relative biological effectiveness (RBE) assessment can be facilitated by real-time signal monitoring. Our study aims to significantly advance the field by addressing the lack of a verification technique for carbon-ion beams, focusing on the considerable impact of the nuclear fragmentation tail on ionoacoustic signal waveforms, which provides crucial insights into the unique energy deposition properties of carbon-ions.

Acoustic camera applied on sound transmission identification in closed spaces

In the ongoing context of office partitioning, users are increasingly paying attention to acoustic insulation between rooms. The issues commonly related are translated by confidentiality problems and annoying use of sound-enabled videoconferencing. In practice, when an insulation defect is perceived, standardized acoustic insulation measurements are carried out to characterize the overall insulation performance. However, it is often challenging to precisely identify by ear the various paths of sound transmission: through the partition, by poorly caulked junctions, through the ceiling, through the facade, etc. Acoustic antenna solutions employed to identify the location and frequency content of sources have existed for several decades. But, in addition of being expensive and bulky, until now their use was reserved for detection of sources on transportation vehicles (trains, cars, planes, etc.) or industrial equipment (wind turbines, boiler rooms, etc.). In recent years, more accessible and practical antenna solutions known as "acoustic camera" have become available. In this sense, we have been handling an acoustic camera to assist on the detection of the sound transmission paths between two rooms. This work address what has been experienced, outlining results obtained through the (evolving) methodology for sound transmission identification with acoustic camera in closed spaces.

Recent investigation on VHF sound exposure sources around us

We have reported recent investigation of sound exposure sources in very-high frequency (VHF) region as the activities for research project of VHF sounds in Japan. Recently, the devices which utilize or generate VHF sounds as like rodent repelling devices, ultrasonic cleaning machines, IH cookers and corner sensors of automobile continue to increase in public space or our living environment. The VHF sounds in high level have physical or/and psychological effects for young people who can hear such sounds. So, in environment around us, we identified VHF sound sources using acoustic camera and measured the sound pressure level for such sources. In this paper, we introduce newly-discovered several VHF sounds: for example, ultrasounds from corner sensors of automobile car, VHF sounds from photovoltaic installation and VHF sounds of interior train, and so on. From both the results measured by acoustic camera and microphones show source positions and frequency characteristics of VHF sounds. However these play important role in our life environment, we need to note that these contain potential hazard for young people who can hear such VHF sounds.

Sparse recovery by genetic algorithms for acoustic testing applications

Various acoustic testing applications involve solving inverse problems from acoustic measurements, such as acoustic imaging and duct mode identification. In these problems, the physical quantity to be inferred is sparse, which allows applications of certain sparse recovery techniques, such as the compressive sensing. But such methods usually involve very complex theory, which therefore set a high threshold for beginners. By contrast, this work proposes a similar but much simpler method based on the well-known genetic algorithm, which borrows only a few easily understood concepts from nature evolution and can rely on easily accessed software/toolboxes. The performance of the developed method is demonstrated by two acoustic testing experiments in an anechoic chamber, i.e., acoustic imaging and duct mode identification.

Coupling adaptive filters with acoustic travel time tomography for indoor climate measurements in occupied room conditions

Acoustic travel time TOMography (ATOM) as a remote sensing technique allows for the measurement of indoor climate parameters, such as air temperature and airflow velocity distributions. The performance of ATOM in monitoring indoor air temperature in empty room conditions was evaluated recently, utilizing a combined analysis approach of room acoustics and tomography. This study shifts focus to the assessment of ATOM in an occupied room condition where fixed objects are introduced to the test area. The presence of objects in a room can result in notable shifts in the travel times of individual reflections. In some cases, these shifts can cause reflections to disappear completely or be replaced by new ones. These partial disruptions in the reflection pattern present challenges in obtaining accurate information about the room's temperature. To ensure the accurate identification of undisturbed reflection paths, an algorithm was developed that can convert distorted signals into a pure reference signal using a suitable adaptive filter. The measurement results confirm the validity of the approach for a temperature interval of 4°C where the system was trained for two temperatures of 20°C and 24°C in a climate chamber.

Development of low-cost MEMS microphone array for sound localization in urban environments

Understanding noise pollution and mitigating its impact on the quality of life in urban environment is a major challenge from a societal and health point of view. In order to monitor and ultimately understand these noisy environments a process of long term acoustic measurements and analysis is required. These urban environments are noisy and obtaining a clear recording of a desired acoustic signal is difficult hence an effective approach is the use of beamforming theory coupled with a microphone array. In this paper a novel approach utilizing compact low-cost microphone arrays equipped with MEMS sensors for continuous sound localization in urban areas is proposed. This approach focuses on leveraging the affordability and scalability of MEMS sensors to deploy distributed acoustic sensor networks across diverse urban locations. The aim is to capture real-time acoustic data, enabling comprehensive monitoring of sound sources and their spatial distribution within the urban environment. This sensor unit can be upgraded with a camera, i.e. acoustic camera, which will provide information on the conditions of the environment where the measurements are performed. This low-cost solution facilitates long-term monitoring, providing valuable insights into the dynamic nature of urban soundscapes.

An investigation of the performances of asynchronous measurements methods

In acoustical imaging and source localization, several methods for the exploitation of asynchronous array measurements have been proposed, based on covariance matrix completion, Bayesian estimation, fusion of beamforming maps, etc. The goal of asynchronous measurements is to reach the performance of large and dense arrays, with reduced experimental effort, by moving an array of limited size between experiments, and fuse the obtained data to produce an estimation of the distribution of acoustical sources. In this study, we consider the performances that one can expect from asynchronous measurements, and investigate the actual performances of several methods from the state of the art. In particular, the mean squared errors of the estimation of the position and power of an acoustical source are estimated using simulations and experimental measurements.

Markov chain-based temporal correlation processing algorithm in reverberant environment by sparse Bayesian learning for acoustic imaging

Identifying sound sources is an important step in noise control, different sound source localization algorithms have been proposed and studied for almost a century. Most of the studies assume an ideal measurement environment, i.e., an anechoic or half-anechoic environment, but there are limited studies discussing localizing sound sources in a reverberation environment. This paper proposes an acoustic imaging method in a reverberation environment based on the sparse Bayesian learning framework. Initially, a sound propagation model was established by accounting for the multipath effects of the sound source in a reverberant environment. Based on the temporal correlation of a Markov chain, the algorithm is further improved in the sparse Bayesian learning framework. By leveraging the temporal correlation the algorithm provides, identifying the true source in a reverberant environment becomes more accurate. Numerical simulation results have shown that the proposed algorithm achieves real sound source localization and eliminates the influence of image sources in contour maps under realistic reverberant environments.

Deep learning-enhanced blind separation of incoherent and spatially disjoint sound sources

Separating incoherent sound sources within complex acoustical fields presents a significant challenge in acoustic imaging. Existing methods, such as Principal Component Analysis (PCA) applied to the Cross-Spectral Matrix (CSM), yield 'virtual' sources based on statistical orthogonality. However, this approach often fails to accurately identify distinct physical sources, primarily due to its reliance on statistical orthogonality solely. A state-of-the-art method involves computing a rotation matrix to enforce criteria such as least spatial entropy, or spatial orthogonality among sources, a process that, while effective, significantly increases computational complexity and time. This work introduces a hybrid approach combining PCA and deep learning for predicting spatially disjoint source maps from virtual sources. By simulating sound sources in random quantities and locations, we train a neural network tailored to this task. We address the order mismatch between PCA-derived virtual sources and pre-simulated labels by framing source separation as a set prediction problem, utilizing the Hungarian loss for efficient mismatch resolution. This method simplifies the separation process, offering faster post-training computations and eliminating the need for complex optimizations. Validation in simulated environments and real-world datasets has shown the model's effectiveness in source separation for acoustic imaging, indicating the potential of integrating deep learning with existing methods.

Assessment of planar array accuracy for 3D sound source reconstruction with Bayesian Focusing

As planar microphone arrays were initially designed for bidimensional sound source localisation, their use for three-dimensional acoustic imaging consistently suffers from a poor resolution along the normal axis. Such a statement is already well documented for beamforming approaches and their declensions. Bearing this in mind, this paper delves into the possibilities offered by inverse methods and especially iterative Bayesian Focusing to tackle this issue and deliver accurate source positions in three dimensions based on planar array measurements only. To this end, an experimental set up is designed: omnidirectional sources are mounted in a wooden mock-up featuring faces at different distances from an 81 MEMs planar array. Based on this measurements, a benchmark study of sound source reconstruction on a 3D mesh of the mock-up is conducted and compared to classical 2D acoustic maps set at different depths. The latter process is declined for various source correlation values and at extended frequency ranges. By doing so, the current paper delivers an overview of the ability to discriminate acoustic sources along the normal axis of a planar array, on a simple and well controlled test case, with a view to provide guidelines for further applications of 3D acoustic imaging on complex geometries.

Importance of station-to-station line orientation in performing coastal acoustic tomography to obtain sea current speed

To perform the coastal acoustic tomography measurement, the locations of stations should be first selected at some appropriate sites on or near the coast. When selecting the locations, it is important to consider vehicle accessibility, the presence of structures such as barges, and the absence of obstructions to sound transmission and reception. Based on these conditions, the five stations were selected for the coastal acoustic tomography observation in Yeosu Bay of South Korea in 2023. In this observation, total six sound propagation lines were used for the inversion of the sea current speed. The sea current speed estimated for each sound propagation line was compared to that from ADCP installed in Yeosu Bay. As a result, when the angle between a line along which the Yeosu Bay's dominant sea current flows and the sound propagation line was small, the estimated current speed was similar to that from ADCP data. This can be considered to be because the smaller the time difference obtained from tomography measurement, the greater the impact of arrival time measurement error on the current speed. This result shows that the orientation of the lines between stations should also be considered important for selecting the locations of stations.

Application of noise remote sensing to conquer loud vehicles in Amsterdam

Techniques for remote sensing of noise are rapidly evolving, allowing for automatic and unsupervised monitoring of individual noise events. In the H2020 project NEMO, noise remote sensing was developed and applied to detect individual loud vehicles in normal road or rail traffic. Potential applications include urban vehicle access restrictions (UVAR) such as in low emission zones, based on real world emissions rather than type approval data. The city of Amsterdam is also experimenting with influencing driving behaviour and raising road user awareness to reduce the number of high noise emitters. Amsterdam uses dynamic roadside signalling for this, which shows a message "too loud" to noise vehicles passing by. New tests are now performed with a more advanced acoustic camera to localize and track the individual vehicle, connected to license plate and speed detectors. This paper describes how NEMO results are used to achieve an actual reduction of high emitters in Amsterdam. First results of experiments are presented along with technical challenges that need to be overcome. These challenges include reduction of measurement uncertainty as well as uncertainty as to what vehicle a high noise event belongs to.