IEEE transactions on ultrasonics, ferroelectrics, and frequency control最新文献

The need for continuous monitoring of cardiorespiratory activity, blood pressure, bladder, muscle motion analysis, and more, is pushing for research and development of wearable ultrasound devices. In this context, there is a critical need for highly configurable, energy-efficient wearable ultrasound systems with wireless access to raw data and long battery life. Previous exploratory works have primarily relied on bulky commercial research systems or custom-built prototypes with limited and narrowly-focused field applicability. This paper presents TINYPROBE, a novel multi-modal wearable ultrasound platform. TINYPROBE integrates a 32-channel ultrasound RX/TX frontend, including TX beamforming (64 V_pp excitations, 16 delay profiles) and analog-to-digital conversion (up to 30 Msps, 10 bit), with a WiFi link (21.6 Mbps, UDP), for wireless raw data access, all in a compact (57 × 35 × 20 mm) and lightweight (35 g) design. Employing advanced power-saving techniques and optimized electronics design, TINYPROBE achieves a power consumption of < 1W for imaging modes (32 ch., 33 Hz) and < 1.3W for high-PRF Doppler mode (2 ch., 1400 Hz). This results in a state-of-the-art power efficiency of 44.9 mW/Mbps for wireless US systems, ensuring multi-hour operation with a compact 500 mAh Li-Po battery. We validate TINYPROBE as a versatile, general-purpose wearable platform in multiple in-vivo imaging scenarios, including muscle and bladder imaging, and blood flow velocity measurements.

Carotid atherosclerotic plaques are a major complication associated with type II diabetes, and carotid ultrasound is commonly used for diagnosing carotid vascular disease. In primary hospitals, less experienced ultrasound physicians often struggle to consistently capture standard carotid images and identify plaques. To address this issue, we propose a novel approach, the long-short memory-based detection network (LSMD), for carotid artery detection in ultrasound video streams, facilitating the identification and localization of critical anatomical structures and plaques. This approach models short- and long-distance spatiotemporal features through Short-term Temporal Aggregation (STA) and Long-term Temporal Aggregation (LTA) modules, effectively expanding the temporal receptive field with minimal delay and enhancing the detection efficiency of carotid anatomy and plaques. Specifically, we introduce memory buffers with a dynamic updating strategy to ensure extensive temporal receptive field coverage while minimizing memory and computation costs. The proposed model was trained on 80 carotid ultrasound videos and evaluated on 50, with all videos annotated by physicians for carotid anatomies and plaques. The trained LSMD was evaluated for performance on the validation and test sets using the single-frame image-based Single Shot Multi-box Detector (SSD) algorithm as a baseline. The results show that the precision, recall, Average Precision at IoU = 0.50 (AP₅₀), and mean Average Precision (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline (p < 0.001) respectively, while the model's inference latency reaches 6.97ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69ms on an edge computing device (Jetson Orin Nano). These findings demonstrate that LSMD can accurately localize carotid anatomy and plaques with real-time inference, indicating its potential for enhancing diagnostic accuracy in clinical practice.

The ultrasound backscatter coefficient is a frequency-dependent quantity intrinsic to biological tissues that can be recovered from backscattered radiofrequency signals, granted acquisitions on a reference phantom are available under the same system's settings. A phantom-free backscatter coefficient estimation method is proposed based on Gaussian-shaped approximation of the point spread function (electronics and piezoelectric characteristics of the scanner's probe) and the effective medium theory combined with the structure factor model, albeit the proposed approach is amenable to other models. Meanwhile, the total attenuation due to intervening tissues is refined from its theoretical value, which is based on reported average behaviors of tissues, while allowing correction for diffraction due to the probe's geometry. The reference phantom method adapted to a similar approach except for the Gaussian approximation is also presented. The proposed phantom-free and reference phantom methods were compared on ten COVID-19 positive patients and twelve control subjects with measures on femoral veins and arteries. In this context, red blood cells are viewed as scatterers that form aggregates increasing the backscatter under the COVID-19 inflammatory condition. The considered model comprises five parameters, including the mean aggregate size estimated according to polydispersity of aggregates' radii, and anisotropy of their shape. The mean aggregate size over the two proposed methods presented an intraclass correlation coefficient of 0.964 for consistency. The aggregate size presented a significant difference between the two groups with either two methods, despite the confounding effect of the maximum Doppler velocity within the blood vessel and its diameter.

Wearable ultrasound has been widely developed for long-term, continuous imaging without the need for bulky system manipulation and repeated manual locating. To potentially lead to more accurate and reliable imaging monitoring, this work presents the design, fabrication, and evaluation of a novel high-frequency wearable ultrasound array belt (WUAB) for small animal echocardiography. The fabrication process involved precise dicing technology for a λ-pitch design. The 20 MHz WUAB consists of two matching layers, piezoelectric composite with 128 channels, customized flexible circuit substrate, acoustic backing layer, and customized belt structure with designed end tip and insertion point for wearability. The resulting WUAB demonstrates sensitivity of -5.69 ± 2.5 dB and fractional bandwidth of 57 ± 5 %. In vivo experiments on rat model showed expected echocardiography and B-mode images of rat heart. These results represent significant promise for future longitudinal studies in small animals and real-time physiological monitoring.

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave transmissions at high frame rates. However, low signal-to-noise ratio of plane waves causes low image quality. Adaptive beamformers have been proposed to suppress noise energy to achieve higher image quality accompanied by increasing computational complexity. Deep learning (DL) leverages powerful hardware capabilities to enable rapid implementation of noise suppression at the cost of flexibility. To enhance the applicability of DL-based methods, in this work, we propose a deep power-aware tunable (DPT) weighting (i.e., postfilter) for delay-and-sum (DAS) beamforming to improve UMI by enhancing plane wave images. The model, called Yformer is a hybrid structure combining convolution and Transformer. With the DAS beamformed and compounded envelope image as input, Yformer can estimate both noise power and signal power. Furthermore, we utilize the obtained powers to compute pixel-wise weights by introducing a tunable noise control factor, which is tailored for improving the quality of different UMI applications. In vivo experiments on the rat brain demonstrate that Yformer can accurately estimate the powers of noise and signal with the structural similarity index (SSIM) higher than 0.95. The performance of the DPT weighting is comparable to that of superior adaptive beamformer in uPDI with low computational cost. The DPT weighting was then applied to four different datasets of ULM, including public simulation, public rat brain, private rat brain, and private rat liver datasets, showing excellent generalizability using the model trained by the private rat brain dataset only. In particular, our method indirectly improves the resolution of liver ULM from 25.24 μm to 18.77 μm by highlighting small vessels. In addition, the DPT weighting exhibits more details of blood vessels with faster processing, which has the potential to facilitate the clinical applications of high-quality UMI.

Super-resolution ultrasound (SRUS) visu-Microvascular Phantom alises microvasculature beyond the ultrasound diffraction limit (wavelength (λ)/2) by localising and tracking spatially isolated microbubble contrast agents. SRUS phantoms typically consist of simple tube structures, where diameter channels below 100 μm are not available. Furthermore, these phantoms are generally fragile and unstable, have limited ground truth validation, and their simple structure limits the evaluation of SRUS algorithms. To aid SRUS development, robust and durable phantoms with known and physiologically relevant microvasculature are needed for repeatable SRUS testing. This work proposes a method to fabricate durable microvascular phantoms that allow optical gauging for SRUS validation. The methodology used a microvasculature negative print embedded in a Polydimethylsiloxane to fabricate a microvascular phantom. Branching microvascular phantoms with variable microvascular density were demonstrated with optically validated vessel diameters down to ~60 μm(λ/5.8; λ=~350 μm ). SRUS imaging was performed and validated with optical measurements. The average SRUS error was 15.61 μm(λ/22) with a standard deviation error of 11.44 μm. The average error decreased to 7.93 μm(λ/44) once the number of localised microbubbles surpassed 1000 per estimated diameter. In addition, the less than 10% variance of acoustic and optical properties and the mechanical toughness of the phantoms measured a year after fabrication demonstrated their long-term durability. This work presents a method to fabricate durable and optically validated complex microvascular phantoms which can be used to quantify SRUS performance and facilitate its further development.

Tomographic reconstruction is used extensively in medicine, non-destructive testing and geology. In an ideal situation where measurements are taken at all angles around an object, known as full view configuration, a full reconstruction of the object can be produced. One of the major issues faced in tomographic imaging is when measurements cannot be taken freely around the object under inspection. This may be caused by the size and geometry of the object or difficulty accessing from particular directions. The resulting limited view transducer configuration leads to a large deterioration in image quality, thus it is very beneficial to employ a compensation algorithm. At present, the most effective compensation algorithms require a large amount of computing power or a bespoke case-by case approach, often with numerous arbitrary constants which must be tuned for a specific application. This work proposes a machine learning based approach to perform the limited view compensation. The model is based around an autoencoder architecture. It is trained on an artificial dataset, taking advantage of the ability to generate arbitrary limited view images given a full view input. The approach is evaluated on ten laser-scanned corrosion maps and the results compared to positivity regularisation - a limited view compensation algorithm similar in the speed of execution and generalisation potential. The algorithms are compared for root mean squared error (RMSE) across the image, and maximum absolute error (MAE). Furthermore, they are visually compared for subjective quality. Compared to the conventional algorithm, the ML-based approach improves on the MAE in eight out of the ten cases. The conventional approach performs better on mean RMSE, which is explained by the ML outputting inaccurate background level, which is not a critical ability. Most importantly, the visual inspection of outputs shows the ML approach reconstructs the images better, especially in the case of irregular corrosion patches. Compared to limited view images, the ML method improves both the RMSE and MAE by 41%.

Super-resolution ultrasound (SRUS) has evolved significantly with the advent of Ultrasound Localization Microscopy (ULM). This technique enables sub-wavelength resolution imaging using microbubble contrast agents. Initially confined to 2D imaging, ULM has progressed towards volumetric approaches, allowing for comprehensive three-dimensional visualization of microvascular networks. This review explores the technological advancements and challenges associated with volumetric ULM, focusing on key aspects such as transducer design, acquisition speed, data processing algorithms, or integration into clinical practice. We discuss the limitations of traditional 2D ULM, including dependency on precise imaging plane selection and compromised resolution in microvasculature quantification. In contrast, volumetric ULM offers enhanced spatial resolution and allowed motion correction in all direction, promising transformative insights into microvascular pathophysiology. By examining current research and future directions, this review highlights the potential of volumetric ULM to contribute significantly to diagnostic across various medical conditions, including cancers, arteriosclerosis, strokes, diabetes, and neurodegenerative diseases.

This study presents a comprehensive dispersion analysis and characterization of guided surface acoustic waves (SAWs) in 30% scandium aluminum nitride (ScAlN) alloy thin films on sapphire (SoS). The solidly mounted platform, which supports the fundamental Rayleigh and Sezawa SAW modes, offers mechanical robustness and high electromechanical coupling (k²_t), while maintaining high confinement of the acoustic modes. Numerical modeling, coupled with experimental results, showcases the characteristics of focusing interdigitated transducers (FIDTs) for injecting acoustic energy into piezoelectric etch-defined acoustic waveguides and highlights their advantages over conventional uniform aperture transducers. Identity mapping of boundary conditions significantly reduces degrees of freedom in modeling energy injection into acoustic waveguides. The theory of Gaussian beams in optics is applied to the FIDTs to model the physical response of the transducers accurately and emphasize their high-intensity focusing nature. This work also demonstrates the ability of FIDTs to facilitate phononic devices and phononic integrated circuit applications in slow-on-fast piezoelectric platforms.

For ultrasound localization microscopy, the localization of microbubbles (MBs) is an essential part to obtain super-resolved maps of the vasculature. This paper analyzes the impact of image discretization and patch size on the precision of different MB localization methods to reconcile different observations from previous studies, provide an estimate of feasible localization precision, and derive guidelines for an optimal parameter selection. For this purpose, images of MBs were simulated with Gaussian point-spread functions (PSF) of varying width parameter σ at randomly generated subpixel positions, and Rician distributed noise was added. Four localization methods were tested on patches of different sizes (number of pixels N × N): Gaussian fit, radial symmetry method, calculation of center of mass, and peak detection. Additionally, the Cramér-Rao lower bound (CRLB) for the given estimation problem was calculated. Our results show that the localization precision is strongly influenced by the ratio of the PSF width parameter σ to the pixel size Δ, as well as the patch size N. The best parameter combination depends on the localization method. Generally, very small σ/Δ ratios as well as large σ/Δ ratios in combination with small N lead to performance degradation. The Gaussian fit as representative of a model-based fit comes close to the CRLB and always performs best for the σ/Δ ratios given by image discretization if N is adapted to the PSF. To achieve good results with the Gaussian fit and the radial symmetry method, a good rule of thumb is to set the pixel sizes Δ ≤ σ/0.6 and the patch sizes N ≥ 2σ/Δ + 3.