Ultrasonic scattering measurements of a live single cell at 86 MHz

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.

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.

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.

Opto-mechanical oscillator in a nanoliter droplet

Droplets are very simple physical systems, whereby surface tension shapes liquids into ideal opto-mechanical devices. This has recently enabled low-viscosity liquid samples to serve as miniature acoustic resonators harnessing optical generation of bulk vibrations, capillaries, or surface waves. Uniquely, a simple room-temperature pendant droplet can be activated as a hypersound-laser emitter when illuminated by a free-space, low-power visible laser thanks to stimulated Brillouin scattering of optical and acoustic whispering-gallery modes. Here, we demonstrate continuous operation of a liquid polymer opto-mechanical resonator and characterize its quality factor and long-term frequency stability. Our results point to the feasibility of all-liquid micro-mechanical oscillators working in the 50-100 MHz range. The stimulated generation of high-quality surface waves on nanoliter droplets gives momentum to new optical schemes for characterization of material viscous-elastic properties, laboratory investigation of atmospheric phenomena, and mass sensing for direct analysis of biological fluids based on ultrasound-hypersound coherent generation and detection.

Label-free analysis of the characteristics of a single cell trapped by acoustic tweezers

Single-cell analysis is essential to understand the physical and functional characteristics of cells. The basic knowledge of these characteristics is important to elucidate the unique features of various cells and causative factors of diseases and determine the most effective treatments for diseases. Recently, acoustic tweezers based on tightly focused ultrasound microbeam have attracted considerable attention owing to their capability to grab and separate a single cell from a heterogeneous cell sample and to measure its physical cell properties. However, the measurement cannot be performed while trapping the target cell, because the current method uses long ultrasound pulses for grabbing one cell and short pulses for interrogating the target cell. In this paper, we demonstrate that short ultrasound pulses can be used for generating acoustic trapping force comparable to that with long pulses by adjusting the pulse repetition frequency (PRF). This enables us to capture a single cell and measure its physical properties simultaneously. Furthermore, it is shown that short ultrasound pulses at a PRF of 167 kHz can trap and separate either one red blood cell or one prostate cancer cell and facilitate the simultaneous measurement of its integrated backscattering coefficient related to the cell size and mechanical properties.

Spectrum analysis for assessing red blood cell aggregation using high-frequency ultrasound array transducer

The purpose of this study is to investigate a spectrum analysis technique for detecting and monitoring red blood cell (RBC) aggregation using a high-frequency array transducer. To assess the feasibility of this approach, the backscattered radio-frequency signal from non-aggregated and aggregated RBC samples with two hematocrit levels were acquired by using a 30-MHz linear array transducer and analyzed in frequency domain. Three parameters such as spectral slope, midband fit and Y intercept were extracted in a static condition. Fresh porcine blood was used and degrees of aggregation were changed by diluting plasma concentration. From the experiments, it was demonstrated that the spectral slope related to a size of scatterer progressively declined as the level of aggregation increased; its mean values at hematocrit of 40% were 1.10 and -0.22 dB/MHz for RBCs suspended in isotonic phosphate buffered saline and solution with 70% plasma concentrations, respectively. For the midband fit and Y intercept, the mean values were increased by 9.1 and 46.4 dB, respectively. These results indicated that the spectrum analysis technique is useful for monitoring RBC aggregation and can be potentially developed for assessing aggregation in clinical applications.