A One-Sided Acoustic Trap for Cell Immobilization Using 30-MHz Array Transducer

Ultra-High Frequency Self-Focusing Ultrasonic Sensors With Half-Concave Geometry for Visualization of Mouse Brain Atrophy

Ultra-high frequency (>100 MHz) acoustic waves feature biocompatibility and high sensitivity and allow biomedical imaging and acoustic tweezers. Primarily, excellent spatial resolution and broad bandwidth at ultra-high frequency is the goal for pathological research and cell selection at the cellular level. Here, we propose an efficient approach to visualize mouse brain atrophy by self-focused ultrasonic sensors at ultra-high frequency with ultra-broad bandwidth. The numerical models of geometry and theoretically predicted acoustic parameters for half-concave piezoelectric elements are calculated by the differential method, which agrees with measured results (lateral resolution: 24 μm, and bandwidth: 115% at -6 dB). Compared with the brain slices of 2-month-old mouse, the atrophy visualization of the 6-month-old mouse brain was realized by C-mode imaging with an acoustic microscopy system, which is a potential prospect for diagnosis and treatment of Alzheimer's disease (AD) combined with neuroscience. Meanwhile, the acoustic properties of the brain slices were quantitatively measured by the acoustic microscopy. These encouraging results demonstrate the promising application for high-resolution imaging in vitro biological tissue with ultra-high frequency self-focusing ultrasonic sensors.

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network

The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient adjustment, and discomfort during use. This paper presents a novel approach for diagnosing diabetes using high-frequency ultrasound (HFU) and a convolutional neural network (CNN). This method is based on the observation that glucose in red blood cells (RBCs) forms glycated hemoglobin (HbA1c) and accumulates on its surface. The study incubated RBCs with different glucose concentrations, collected acoustic reflection signals from them using a custom-designed 90-MHz transducer, and analyzed the signals using a CNN. The CNN was applied to the frequency spectra and spectrograms of the signal to identify correlations between changes in RBC properties owing to glucose concentration and signal features. The results confirmed the efficacy of the CNN-based approach with a classification accuracy of 0.98. This non-invasive diagnostic technology using HFU and CNN holds promise for in vivo diagnosis without the need for blood collection.

Phase-Optimized Multi-Step Phase Acoustic Metasurfaces for Arbitrary Multifocal Beamforming

Focused ultrasound featuring non-destructive and high sensitivity has attracted widespread attention in biomedical and industrial evaluation. However, most traditional focusing techniques focus on the design and improvement of single-point focusing, neglecting the need to carry more dimensions of multifocal beams. Here we propose an automatic multifocal beamforming method, which is implemented using a four-step phase metasurface. The metasurface composed of four-step phases improves the transmission efficiency of acoustic waves as a matching layer and enhances the focusing efficiency at the target focal position. The change in the number of focused beams does not affect the full width at half maximum (FWHM), revealing the flexibility of the arbitrary multifocal beamforming method. Phase-optimized hybrid lenses reduce the sidelobe amplitude, and excellent agreement is observed between the simulation and experiments for triple-focusing beamforming metasurface lenses. The particle trapping experiment further validates the profile of the triple-focusing beam. The proposed hybrid lens can achieve flexible focusing in three dimensions (3D) and arbitrary multipoint, which may have potential prospects for biomedical imaging, acoustic tweezers, and brain neural modulation.

Fabrication, Acoustic Characterization and Phase Reference-Based Calibration Method for a Single-Sided Multi-Channel Ultrasonic Actuator

The ultrasonic actuator can be used in medical applications because it is label-free, biocompatible, and has a demonstrated history of safe operation. Therefore, there is an increasing interest in using an ultrasonic actuator in the non-contact manipulation of micromachines in various materials and sizes for therapeutic applications. This research aims to design, fabricate, and characterize a single-sided transducer array with 56 channels operating at 500 kHz, which provide benefits in the penetration of tissue. The fabricated transducer is calibrated using a phase reference calibration method to reduce position misalignment and phase discrepancies caused by acoustic interaction. The acoustic fields generated by the transducer array are measured in a 300 mm × 300 mm × 300 mm container filled with de-ionized water. A hydrophone is used to measure the far field in each transducer array element, and the 3D holographic pattern is analyzed based on the scanned acoustic pressure fields. Next, the phase reference calibration is applied to each transducer in the ultrasonic actuator. As a result, the homogeneity of the acoustic pressure fields surrounding the foci area is improved, and the maximum pressure is also increased in the twin trap. Finally, we demonstrate the capability to trap and manipulate micromachines with acoustic power by generating a twin trap using both optical camera and ultrasound imaging systems in a water medium. This work not only provides a comprehensive study on acoustic actuators but also inspires the next generation to use acoustics in medical applications.

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

Holographic Acoustic Tweezers for 5-DoF Manipulation of Nanocarrier Clusters toward Targeted Drug Delivery

Acoustic tweezers provide unique capabilities in medical applications, such as contactless manipulation of small objects (e.g., cells, compounds or living things), from nanometer-sized extracellular vesicles to centimeter-scale structures. Additionally, they are capable of being transmitted through the skin to trap and manipulate drug carriers in various media. However, these capabilities are hindered by the limitation of controllable degrees of freedom (DoFs) or are limited maneuverability. In this study, we explore the potential application of acoustical tweezers by presenting a five-DoF contactless manipulation acoustic system (AcoMan). The system has 30 ultrasound transducers (UTs) with single-side arrangement that generates active traveling waves to control the position and orientation of a fully untethered nanocarrier clusters (NCs) in a spherical workspace in water capable of three DoFs translation and two DoFs rotation. In this method, we use a phase modulation algorithm to independently control the phase signal for 30 UTs and manipulate the NCs’ positions. Phase modulation and switching power supply for each UT are employed to rotate the NCs in the horizontal plane and control the amplitude of power supply to each UT to rotate the NCs in the vertical plane. The feasibility of the method is demonstrated by in vitro and ex vivo experiments using porcine ribs. A significant portion of this study could advance the therapeutic application such a system as targeted drug delivery.

Recent Advancements in Ultrasound Transducer: From Material Strategies to Biomedical Applications

Ultrasound is extensively studied for biomedical engineering applications. As the core part of the ultrasonic system, the ultrasound transducer plays a significant role. For the purpose of meeting the requirement of precision medicine, the main challenge for the development of ultrasound transducer is to further enhance its performance. In this article, an overview of recent developments in ultrasound transducer technologies that use a variety of material strategies and device designs based on both the piezoelectric and photoacoustic mechanisms is provided. Practical applications are also presented, including ultrasound imaging, ultrasound therapy, particle/cell manipulation, drug delivery, and nerve stimulation. Finally, perspectives and opportunities are also highlighted.

Automated estimation of cancer cell deformability with machine learning and acoustic trapping

Cell deformability is a useful feature for diagnosing various diseases (e.g., the invasiveness of cancer cells). Existing methods commonly inflict pressure on cells and observe changes in cell areas, diameters, or thickness according to the degree of pressure. Then, the Young’s moduli (i.e., a measure of deformability) of cells are estimated based on the assumption that the degrees of the changes are inversely proportional to Young’s moduli. However, manual measurements of the physical changes in cells are labor-intensive, and the subjectivity of the operators can intervene during this step, thereby causing considerable uncertainty. Further, because the shapes of cells are nonuniform, we cannot ensure the assumption for linear correlations of physical changes in cells with their deformability. Therefore, this study aims at measuring non-linear elastic moduli of live cells (degrees of cell deformability) automatically by employing conventional neural networks (CNN) and multilayer perceptrons (MLP) while preserving (or enhancing) the accuracy of the manual methods. First, we obtain photomicrographs of cells on multiple pressure levels using single-beam acoustic tweezers, and then, we suggest an image preprocessing method for emphasizing changes in cell areas on the photomicrographs. The CNN model is trained to measure the ratios of the cell area change at each pressure level. Then, we apply the multilayer perceptron (MLP) to learn the correlations of the cell area change ratios according to the pressure levels with cell deformability. The accuracy of the CNN was evaluated using two types of breast cancer cells: MDA-MB-231 (invasive) and MCF-7 (noninvasive). The MLP was assessed using five different beads (Young’s moduli from 0.214 to 9.235 kPa), which provides standardized reference data of the non-linear elastic moduli of live cells. Finally, we validated the practicality of the proposed system by examining whether the non-linear elastic moduli estimated by the proposed system can distinguish invasive breast cancer cells from noninvasive ones.

3D Acoustic Manipulation of Living Cells and Organisms Based on 2D Array

Flexible manipulation techniques for living cells and organisms are extremely useful tools for fundamental biomedical and life science research. Acoustic tweezers, which permit non-contact, label-free manipulation, are particularly suited to micromanipulation tasks as they provide a large acoustic radiation force and can be applied in various media. Here, we describe the design and fabrication of a 3 MHz, 64-element (8 8), 2D planar ultrasound array that realizes the multidimensional translation, rotation, orientation, and levitation of living cells and organisms. The focusing vortex and twin fields are generated using the holographic acoustic elements framework method. We demonstrate that the eggs and larvae of brine shrimp can be translated along a preset trajectory by controlling the central position of the vortex. By multiplexing counterclockwise vortices, clockwise vortices, and twin trap fields in a time sequence, the rotation direction of the shrimp eggs can be switched in real time, while non-spherical larvae can be reoriented. Moreover, the reflection of the acoustic beam can lift eggs and larvae from the bottom of the culture dish and further manipulate them in the vertical and horizontal directions. Additionally, we present quantitative analyses of the relationships between the shrimp-egg rotation frequency and the focal depth, topological charge of the vortex, and excitation voltage. These results indicate that acoustic tweezers based on 2D matrix arrays can realize complex and selective manipulation of living cells and organisms, thereby demonstrating their value for advancing research in the fields of cell assembly, tissue engineering, and micro-robot driving.

Generation of spherical vortex beams to trap large particles for enhanced axial force

Recent studies have shown the possibility to manipulate small elastic spheres in 3D with a single-sided beam. Acoustical tweezers are very appealing because they provide a fine spatial control of the motion of a single particle in space. Their main limitations are due to the weak restoring axial force and improving this force is still a challenge. We show theoretically that the spherical vortex beams can trap large particles and enhance the axial force. Indeed, the special features of these unusual beams look like the bottle beams in optics. Nevertheless, their spatial complexity presupposes that they can be produced with sufficient precision. Therefore, attention is paid to the synthesis of the spherical vortices. A method based on the inverse filter method is proposed. It allows to synthesize them with a very good precision since the theoretical force is recovered experimentally with an error smaller than 10%. Then, the spherical vortices are used to trap polyethylene beads with radii between 500 and 590µm. Experiments show that the radial trap is working while no beads have been trapped in the axial direction. This failure is analyzed in detail and is shown to be mainly due to sensitivity to the properties of the materials which influences the resonance modes of the elastic sphere. This study paves the way to the optimization of acoustical tweezers for the manipulation of large objects.

Micromotor Manipulation Using Ultrasonic Active Traveling Waves

The ability to manipulate therapeutic agents in fluids is of interest to improve the efficiency of targeted drug delivery. Ultrasonic manipulation has great potential in the field of therapeutic applications as it can trap and manipulate micro-scale objects. Recently, several methods of ultrasonic manipulation have been studied through standing wave, traveling wave, and acoustic streaming. Among them, the traveling wave based ultrasonic manipulation is showing more advantage for in vivo environments. In this paper, we present a novel ultrasonic transducer (UT) array with a hemispherical arrangement that generates active traveling waves with phase modulation to manipulate a micromotor in water. The feasibility of the method could be demonstrated by in vitro and ex vivo experiments conducted using a UT array with 16 transducers operating at 1 MHz. The phase of each transducer was controlled independently for generating a twin trap and manipulation of a micromotor in 3D space. This study shows that the ultrasonic manipulation device using active traveling waves is a versatile tool that can be used for precise manipulation of a micromotor inserted in a human body and targeted for drug delivery.

Investigation of cell mechanics using single-beam acoustic tweezers as a versatile tool for the diagnosis and treatment of highly invasive breast cancer cell lines: an in vitro study

Advancements in diagnostic systems for metastatic cancer over the last few decades have played a significant role in providing patients with effective treatment by evaluating the characteristics of cancer cells. Despite the progress made in cancer prognosis, we still rely on the visual analysis of tissues or cells from histopathologists, where the subjectivity of traditional manual interpretation persists. This paper presents the development of a dual diagnosis and treatment tool using an in vitro acoustic tweezers platform with a 50 MHz ultrasonic transducer for label-free trapping and bursting of human breast cancer cells. For cancer cell detection and classification, the mechanical properties of a single cancer cell were quantified by single-beam acoustic tweezers (SBAT), a noncontact assessment tool using a focused acoustic beam. Cell-mimicking phantoms and agarose hydrogel spheres (AHSs) served to standardize the biomechanical characteristics of the cells. Based on the analytical comparison of deformability levels between the cells and the AHSs, the mechanical properties of the cells could be indirectly measured by interpolating the Young's moduli of the AHSs. As a result, the calculated Young's moduli, i.e., 1.527 kPa for MDA-MB-231 (highly invasive breast cancer cells), 2.650 kPa for MCF-7 (weakly invasive breast cancer cells), and 2.772 kPa for SKBR-3 (weakly invasive breast cancer cells), indicate that highly invasive cancer cells exhibited a lower Young's moduli than weakly invasive cells, which indicates a higher deformability of highly invasive cancer cells, leading to a higher metastasis rate. Single-cell treatment may also be carried out by bursting a highly invasive cell with high-intensity, focused ultrasound.

Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks

Single-beam acoustic tweezers (SBAT) is a widely used trapping technique to manipulate microscopic particles or cells. Recently, the characterization of a single cancer cell using high-frequency (>30 MHz) SBAT has been reported to determine its invasiveness and metastatic potential. Investigation of cell elasticity and invasiveness is based on the deformability of cells under SBAT’s radiation forces, and in general, more physically deformed cells exhibit higher levels of invasiveness and therefore higher metastatic potential. However, previous imaging analysis to determine substantial differences in cell deformation, where the SBAT is turned ON or OFF, relies on the subjective observation that may vary and requires follow-up evaluations from experts. In this study, we propose an automatic and reliable cancer cell classification method based on SBAT and a convolutional neural network (CNN), which provides objective and accurate quantitative measurement results. We used a custom-designed 50 MHz SBAT transducer to obtain a series of images of deformed human breast cancer cells. CNN-based classification methods with data augmentation applied to collected images determined and validated the metastatic potential of cancer cells. As a result, with the selected optimizers, precision, and recall of the model were found to be greater than 0.95, which highly validates the classification performance of our integrated method. CNN-guided cancer cell deformation analysis using SBAT may be a promising alternative to current histological image analysis, and this pretrained model will significantly reduce the evaluation time for a larger population of cells.