Showing papers in "IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control in 2021"

Deep Learning for Ultrasonic Crack Characterization in NDE

Abstract: Machine learning for nondestructive evaluation (NDE) has the potential to bring significant improvements in defect characterization accuracy due to its effectiveness in pattern recognition problems. However, the application of modern machine learning methods to NDE has been obstructed by the scarcity of real defect data to train on. This article demonstrates how an efficient, hybrid finite element (FE) and ray-based simulation can be used to train a convolutional neural network (CNN) to characterize real defects. To demonstrate this methodology, an inline pipe inspection application is considered. This uses four plane wave images from two arrays and is applied to the characterization of cracks of length 1–5 mm and inclined at angles of up to 20° from the vertical. A standard image-based sizing technique, the 6-dB drop method, is used as a comparison point. For the 6-dB drop method, the average absolute error in length and angle prediction is ±1.1 mm and ±8.6°, respectively, while the CNN is almost four times more accurate at ±0.29 mm and ±2.9°. To demonstrate the adaptability of the deep learning approach, an error in sound speed estimation is included in the training and test set. With a maximum error of 10% in shear and longitudinal sound speed, the 6-dB drop method has an average error of ±1.5 mmm and ±12°, while the CNN has ±0.45 mm and ±3.0°. This demonstrates far superior crack characterization accuracy by using deep learning rather than traditional image-based sizing.

Design and Fabrication of a Novel Dual-Frequency Confocal Ultrasound Transducer for Microvessels Super-Harmonic Imaging

Abstract: Recently, super-harmonic ultrasound imaging technology has caused much attention due to its capability of distinguishing microvessels from the tissues surrounding them. However, the fabrication of a dual-frequency confocal transducer is still a challenge. In this work, 270- $\mu \text{m}$ PMN-PT single crystal 1–3 composite and 28- $\mu \text{m}$ PVDF thick film, acting as transmission layer and receiving layer, respectively, are integrated in a novel co-focusing structure. To realize delicate wave propagation control, microwave transmission line theory is introduced to design such structure. Two acoustic filter layers, 13- $\mu \text{m}$ copper layer and 39- $\mu \text{m}$ Epoxy 301 layer, are indispensable and should be added between two piezoelectric layers. Therefore, an acoustic issue can be overcome via an electrical method and the successful achievement of a dual-frequency (5 MHz/30 MHz) ultrasound transducer with a confocal distance of 8 mm can be realized. The super-harmonic ultrasound imaging experiment is conducted using this kind of device. The 3-D image of 110- $\mu \text{m}$ -diameter phantom tube injected with microbubbles can be obtained. These promising results demonstrate that this novel dual-frequency (5 MHz/30 MHz) confocal ultrasound transducer is potentially usable for microvascular medical imaging application in the future.

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

Abstract: Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium. We summarize the challenge results and present qualitative and quantitative assessments using both the initially closed CUBDL evaluation test dataset (which was crowd-sourced from multiple groups around the world) and additional in vivo breast ultrasound data contributed after the challenge was completed. As an example quantitative assessment, single plane wave images from the CUBDL Task 1 dataset produced a mean generalized contrast-to-noise ratio (gCNR) of 0.67 and a mean lateral resolution of 0.42 mm when formed with delay-and-sum beamforming, compared with a mean gCNR as high as 0.81 and a mean lateral resolution as low as 0.32 mm when formed with networks submitted by the challenge winners. We also describe contributed CUBDL data that may be used for training of future networks. The compiled database includes a total of 576 image acquisition sequences. We additionally introduce a neural-network-based global sound speed estimator implementation that was necessary to fairly evaluate the results obtained with this international database. The integration of CUBDL evaluation methods, evaluation code, network weights from the challenge winners, and all datasets described herein are publicly available (visit https://cubdl.jhu.edu for details).

Real-Time 3-D Imaging Using an Air-Coupled Ultrasonic Phased-Array

Abstract: We present an air-coupled ultrasonic imaging system based on a 40-kHz $8\times 8$ phased-array for 3-D real-time localization of multiple objects in the far-field By attaching a waveguide to the array, the effective interelement spacing is reduced to half wavelength This enables grating lobe-free transmit and receive beamforming with a uniform rectangular array of efficient low-cost transducers The system further includes custom transceiver electronics, an field programmable gate array (FPGA) system-on-chip and a PC for GPU accelerated frequency domain signal processing, consisting of matched filtering, conventional beamforming, and envelope extraction using Nvidia Compute Unified Device Architecture (CUDA) and OpenGL for visualization The uniform rectangular layout allows utilizing multiple transmit and receive methods, known from medical imaging applications Thus, the system is dynamically adaptable to maximize the frame rate or detection range One implemented method demonstrates the real-time capability by transmitting a hemispherical pulse (HP) with a single transducer to irradiate the surroundings simultaneously, whereas all transducers are used for echo reception The imaging properties, such as axial and lateral resolution, field of view and range of view, are characterized in an anechoic chamber The object localization is validated for a horizontal and vertical field of view of ±80° and a range of view of 05–3 m with 29 frames/s Using the same system, a comparison between the HP method and the dynamic transmit beamforming method, which transmits multiple sequential beamformed pulses for long-range localization, is provided

Mini-COVIDNet: Efficient Lightweight Deep Neural Network for Ultrasound Based Point-of-Care Detection of COVID-19

Abstract: Lung ultrasound (US) imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a lightweight mobile friendly efficient deep learning model for detection of COVID-19 using lung US images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other lightweight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires a training time of only 24 min. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung US imaging plausible on a mobile platform. Deployment of these lightweight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other lightweight networks. The developed lightweight models are available at https://github.com/navchetan-awasthi/Mini-COVIDNet .

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images

Abstract: The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid optoacoustic ultrasound (OPUS) combination, images retrieved by one modality may further be used to improve the reconstruction accuracy of the other. In this regard, image segmentation plays a major role as it can aid improving the image quality and quantification abilities by facilitating modeling of light and sound propagation through the imaged tissues and surrounding coupling medium. Here, we propose an automated approach for surface segmentation in whole-body mouse OPUS imaging using a deep convolutional neural network (CNN). The method has shown robust performance, attaining accurate segmentation of the animal boundary in both optoacoustic and pulse-echo ultrasound images, as evinced by quantitative performance evaluation using Dice coefficient metrics.

Automated Defect Detection From Ultrasonic Images Using Deep Learning

Abstract: Nondestructive evaluation (NDE) is a set of techniques used for material inspection and defect detection without causing damage to the inspected component. One of the commonly used nondestructive techniques is called ultrasonic inspection. The acquisition of ultrasonic data was mostly automated in recent years, but the analysis of the collected data is still performed manually. This process is thus very expensive, inconsistent, and prone to human errors. An automated system would significantly increase the efficiency of analysis, but the methods presented so far fail to generalize well on new cases and are not used in real-life inspection. Many of the similar data analysis problems were recently tackled by deep learning methods. This approach outperforms classical methods but requires lots of training data, which is difficult to obtain in the NDE domain. In this work, we train a deep learning architecture EfficientDet to automatically detect defects from ultrasonic images. We showed how some of the hyperparameters can be tweaked in order to improve the detection of defects with extreme aspect ratios that are common in ultrasonic images. The proposed object detector was trained on the largest dataset of ultrasonic images that was so far seen in the literature. In order to collect the dataset, six steel blocks containing 68 defects were scanned with a phased-array probe. More than 4000 VC-B-scans were acquired and used for training and evaluation of EfficientDet. The proposed model achieved 89.6% of mean average precision (mAP) during fivefold cross validation, which is a significant improvement compared to some similar methods that were previously used for this task. A detailed performance overview for each of the folds revealed that EfficientDet-D0 successfully detects all of the defects present in the inspected material.

Automatic Detection of Atherosclerotic Plaque and Calcification From Intravascular Ultrasound Images by Using Deep Convolutional Neural Networks

Abstract: Atherosclerosis is the major cause of cardiovascular diseases (CVDs). Intravascular ultrasound (IVUS) is a common imaging modality for diagnosing CVDs. However, an efficient analyzer for IVUS image segmentation is required for assisting cardiologists. In this study, an end-to-end deep-learning convolutional neural network was developed for automatically detecting media–adventitia borders, luminal regions, and calcified plaque in IVUS images. A total of 713 grayscale IVUS images from 18 patients were used as training data for the proposed deep-learning model. The model is constructed using the three modified U-Nets and combined with the concept of cascaded networks to prevent errors in the detection of calcification owing to the interference of pixels outside the plaque regions. Three loss functions (Dice, Tversky, and focal loss) with various characteristics were tested to determine the best setting for the proposed model. The efficacy of the deep-learning model was evaluated by analyzing precision–recall curve. The average precision (AP), Dice score coefficient, precision, sensitivity, and specificity of the predicted and ground truth results were then compared. All training processes were validated using leave-one-subject-out cross-validation. The experimental results showed that the proposed deep-learning model exhibits high performance in segmenting the media–adventitia layers and luminal regions for all loss functions, with all tested metrics being higher than 0.90. For locating calcified tissues, the best result was obtained when the focal loss function was applied to the proposed model, with an AP of 0.73; however, the prediction efficacy was affected by the proportion of calcified tissues within the plaque region when the focal loss function was employed. Compared with commercial software, the proposed method exhibited high accuracy in segmenting IVUS images in some special cases, such as when shadow artifacts or side vessels surrounded the target vessel.

Ultrasound for the Brain: A Review of Physical and Engineering Principles, and Clinical Applications

Abstract: The emergence of new ultrasound technologies has improved our understanding of the brain functions and offered new opportunities for the treatment of brain diseases. Ultrasound has become a valuable tool in preclinical animal and clinical studies as it not only provides information about the structure and function of brain tissues but can also be used as a therapy alternative for brain diseases. High-resolution cerebral flow images with high sensitivity can be acquired using novel functional ultrasound and super-resolution ultrasound imaging techniques. The noninvasive treatment of essential tremors has been clinically approved and it has been demonstrated that the ultrasound technology can revolutionize the currently existing treatment methods. Microbubble-mediated ultrasound can remotely open the blood–brain barrier enabling targeted drug delivery in the brain. More recently, ultrasound neuromodulation received a great amount of attention due to its noninvasive and deep penetration features and potential therapeutic benefits. This review provides a thorough introduction to the current state-of-the-art research on brain ultrasound and also introduces basic knowledge of brain ultrasound including the acoustic properties of the brain/skull and engineering techniques for ultrasound. Ultrasound is expected to play an increasingly important role in the diagnosis and therapy of brain diseases.

Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging

Abstract: Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used focused line scan mode. Conventional adaptive beamformers can improve imaging quality at the cost of additional computation. In this article, we propose to use a deep neural network (DNN) to enhance the performance of PWI while maintaining a high frame rate. In particular, the PWI response from a single point target is used as the network input, while the focused scan response from the same point serves as the desired output, which is the main contribution of this method. To evaluate the performance of the proposed method, simulations, phantom experiments and in vivo studies are conducted. The delay-and-sum (DAS), the coherence factor (CF), a previously proposed deep learning-based method and the DAS with focused scan are used for comparison. Numerical metrics, including the contrast ratio (CR), the contrast-to-noise ratio (CNR), and the speckle signal-to-noise ratio (sSNR), are used to quantify the performance. The results indicate that the proposed method can achieve superior resolution and contrast performance. Specifically, the proposed method performs better than the DAS in all metrics. Although the CF provides a higher CR, its CNR and sSNR are much lower than those of the proposed method. The overall performance is also better than that of the previous deep learning method and at the same level with focused scan performance. Additionally, in comparison with the DAS, the proposed method requires little additional computation, which ensures high temporal resolution. These results validate that the proposed method can achieve a high imaging quality while maintaining the high frame rate associated with PWI.

Surface Acoustic Wave Resonators With Hetero Acoustic Layer (HAL) Structure Using Lithium Tantalate and Quartz

Abstract: This article reports a new concept of surface-acoustic-wave (SAW) resonator, which uses shear horizontal (SH) wave confined in a thin LiTaO3 (LT) layer supported by a quartz (Qz) substrate. The LT layer is 35–50°YX LT, and the quartz substrate is 35–60°Y90°X Qz. A negative temperature coefficient of frequency (TCF) of the SH SAW in the LT layer is compensated by the quartz substrate, which shows a wide range of positive TCF depending on the crystalline orientation. Excellent TCFs of 2 and −10 ppm/°C were measured for the series and parallel resonance frequencies, respectively. The strong confinement of the SH SAW in the LT layer results in the best level of resonance characteristics ever reported. The measured impedance ratio reached 84 dB. On the other hand, spurious waves other than the SH SAW are not confined in the LT layer due to the unique properties of quartz, which results in spurious-free characteristics throughout a wide frequency range.

Deep Learning Analysis of Ultrasonic Guided Waves for Cortical Bone Characterization

Abstract: Ultrasonic guided waves (UGWs) propagating in the long cortical bone can be measured via the axial transmission method. The characterization of long cortical bone using UGW is a multiparameter inverse problem. The optimal solution of the inverse problem often includes a complex solving process. Deep neural networks (DNNs) are essentially powerful multiparameter predictors based on universal approximation theorem, which are suitable for solving parameter predictions in the inverse problem by constructing the mapping relationship between UGW and cortical bone material parameters. In this study, we investigate the feasibility of applying the multichannel crossed convolutional neural network (MCC-CNN) to simultaneously estimate cortical thickness and bulk velocities (longitudinal and transverse). Unlike the multiparameter estimation in most previous studies, the technique mentioned in this work avoids solving a multiparameter optimization problem directly. The finite-difference time-domain (FDTD) method is performed to obtain the simulated UGW array signals for training the MCC-CNN. The network that is exclusively trained on simulated data sets can predict cortical parameters from the experimental UGW data. The proposed method is confirmed by using FDTD simulation signals and experimental data obtained from four bone-mimicking plates and from ten ex vivo bovine cortical bones. The estimated root-mean-squared error (RMSE) in the simulated test data for the longitudinal bulk velocity ( ${V}_{L}$ ), transverse bulk velocity ( ${V}_{T}$ ), and cortical thickness ( Th ) is 97 m/s, 53 m/s, and 0.089 mm, respectively. The predicted RMSE in the bone-mimicking phantom experiments for ${V}_{L\|}$ , ${V}_{T\|}$ , and Th is 120 m/s, 80 m/s, and 0.14 mm, respectively. The experimental dispersion trajectories are matched with the theoretical dispersion curves calculated by the predicted parameters in ex vivo bovine cortical bone experiments. Our proposed method demonstrates a feasible approach for the accurate evaluation of long cortical bones based on UGW.

Full-Matrix Phase Shift Migration Method for Transcranial Ultrasonic Imaging

Abstract: A spectrum-domain method, called full-matrix phase shift migration (FM-PSM), is presented for transcranial ultrasound phase correction and imaging with ideal synthetic aperture focusing technology. The simulated data obtained using the pseudospectral time-domain method are used to evaluate the feasibility of the method. The experimental data measured from a 3-D printed skull phantom are used to evaluate the algorithm performance in terms of resolution, contrast-to-noise ratio (CNR), and eccentricity comparing with the classical ray-tracing delay and sum (DAS) method. In wire imaging experiment, FM-PSM has a lateral resolution of 0.22 mm and ray-tracing DAS has a lateral resolution of 0.24 mm measured at −6-dB drop using a transducer with a center frequency of 6.25 MHz. In cylinder imaging experiment, FM-PSM has a CNR of 2.14 and ray-tracing DAS has a CNR of 1.82, which illustrates about 17% improvement. For a ${J}$ -element array and an output image with pixels ${M} \times {N}$ (lateral $\times $ axial), the computational cost of the DAS is of ${O}{(}{J} \times {M}^{{2}}\times {N}^{{2}}{)}$ ; on the contrary, it reduces to ${O}{(}{J} \times {M} \times {N}^{{2}}{)}$ with the proposed FM-PSM. The results suggest that FM-PSM is an efficiency method for transcranial ultrasonic imaging.

Variational Formulation of Unsupervised Deep Learning for Ultrasound Image Artifact Removal

Abstract: Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.

In Vivo Motion Correction in Super-Resolution Imaging of Rat Kidneys

Abstract: Super-resolution (SR) imaging has the potential of visualizing the microvasculature down to the 10- $\mu \text{m}$ level, but motion induced by breathing, heartbeats, and muscle contractions are often significantly above this level. This article, therefore, introduces a method for estimating tissue motion and compensating for this. The processing pipeline is described and validated using Field II simulations of an artificial kidney. In vivo measurements were conducted using a modified bk5000 research scanner (BK Medical, Herlev, Denmark) with a BK 9009 linear array probe employing a pulse amplitude modulation scheme. The left kidney of ten Sprague-Dawley rats was scanned during open laparotomy. A 1:10 diluted SonoVue contrast agent (Bracco, Milan, Italy) was injected through a jugular vein catheter at 100 $\mu \text{l}$ /min. Motion was estimated using speckle tracking and decomposed into contributions from the heartbeats, breathing, and residual motion. The estimated peak motions and their precisions were: heart: axial— $7.0~\pm ~0.55~\mu \text{m}$ and lateral— $38~\pm ~2.5~\mu \text{m}$ , breathing: axial— $5~\pm ~0.29~\mu \text{m}$ and lateral— $26~\pm ~1.3~\mu \text{m}$ , and residual: axial—30 $\mu \text{m}$ and lateral—90 $\mu \text{m}$ . The motion corrected microbubble tracks yielded SR images of both bubble density and blood vector velocity. The estimation was, thus, sufficiently precise to correct shifts down to the 10- $\mu \text{m}$ capillary level. Similar results were found in the other kidney measurements with a restoration of resolution for the small vessels demonstrating that motion correction in 2-D can enhance SR imaging quality.

Histogram Matching for Visual Ultrasound Image Comparison

Abstract: The widespread development of new ultrasound image formation techniques has created a need for a standardized methodology for comparing the resulting images. Traditional methods of evaluation use quantitative metrics to assess the imaging performance in specific tasks, such as point resolution or lesion detection. Quantitative evaluation is complicated by unconventional new methods and nonlinear transformations of the dynamic range of data and images. Transformation-independent image metrics have been proposed for quantifying task performance. However, clinical ultrasound still relies heavily on visualization and qualitative assessment by expert observers. We propose the use of histogram matching to better assess differences across image formation methods. We briefly demonstrate the technique using a set of sample beamforming methods and discuss the implications of such image processing. We present variations of histogram matching and provide code to encourage the application of this method within the imaging community.

Histotripsy Ablation Alters the Tumor Microenvironment and Promotes Immune System Activation in a Subcutaneous Model of Pancreatic Cancer

Abstract: Pancreatic cancer is a significant cause of cancer-related deaths in the United States with an abysmal five-year overall survival rate that is under 9%. Reasons for this mortality include the lack of late-stage treatment options and the immunosuppressive tumor microenvironment. Histotripsy is an ultrasound-guided, noninvasive, nonthermal tumor ablation therapy that mechanically lyses targeted cells. To study the effects of histotripsy on pancreatic cancer, we utilized an in vitro model of pancreatic adenocarcinoma and compared the release of potential antigens following histotripsy treatment to other ablation modalities. Histotripsy was found to release immune-stimulating molecules at magnitudes similar to other nonthermal ablation modalities and superior to thermal ablation modalities, which corresponded to increased innate immune system activation in vivo . In subsequent in vivo studies, murine Pan02 tumors were grown in mice and treated with histotripsy. Flow cytometry and rtPCR were used to determine changes in the tumor microenvironment over time compared to untreated animals. In mice with pancreatic tumors, we observed significantly increased tumor-progression-free and general survival, with increased activation of the innate immune system 24 h posttreatment and decreased tumor-associated immune cell populations within 14 days of treatment. This study demonstrates the feasibility of using histotripsy for pancreatic cancer ablation and provides mechanistic insight into the initial innate immune system activation following treatment. Further work is needed to establish the mechanisms behind the immunomodulation of the tumor microenvironment and immune effects.

Feasibility of Model-Assisted Probability of Detection Principles for Structural Health Monitoring Systems Based on Guided Waves for Fiber-Reinforced Composites

Abstract: In many industrial sectors, structural health monitoring (SHM) is considered as an addition to nondestructive testing (NDT) that can reduce maintenance effort during the lifetime of a technical facility, structural component, or vehicle. A large number of SHM methods are based on ultrasonic waves, whose properties change depending on structural health. However, the wide application of SHM systems is limited due to the lack of suitable methods to assess their reliability. The evaluation of the system performance usually refers to the determination of the probability of detection (POD) of a test procedure. Up until now, only a few limited methods exist to evaluate the POD of SHM systems, which prevents them from being standardized and widely accepted in the industry. The biggest hurdle concerning the POD calculation is the large number of samples needed. A POD analysis requires data from numerous identical structures with integrated SHM systems. Each structure is then damaged at different locations and with various degrees of severity. All of these are connected to high costs. Therefore, one possible way to tackle this problem is to perform computer-aided investigations. In this work, the POD assessment procedure established in NDT according to the Berens model is adapted to guided wave-based SHM systems. The approach implemented here is based on solely computer-aided investigations. After efficient modeling of wave propagation phenomena across an automotive component made of a carbon-fiber-reinforced composite, the POD curves are extracted. Finally, the novel concept of a POD map is introduced to look into the effect of damage position on system reliability.

Total-Focus Ultrasonic Imaging of Defects in Solids Using a PZT Piezoelectric Micromachined Ultrasonic Transducer Array

Abstract: This work demonstrates the ultrasonic imaging ability of piezoelectric micromachined ultrasonic transducer (PMUT) for the detection of defects deep in solids by total-focus imaging algorithm. The 3-MHz PMUT array uses thin-film PZT as the material for energy transformation because of its high piezoelectric coefficient. Six columns with 12 PMUT units each exhibit an acoustic pressure of 137 kPa measured in water at 1 cm away when driven by a 27-Vpp input. Butter is chosen experimentally as the coupling agent to solids because of its low noise level and short cycling down. Through multilevel processing of echoes and total-focus algorithm, the 2-D image of a graphite plant was obtained with four holes identified based on our customized impedance-matched imaging system. The 16 columns of PMUT array with an area of 5.8 mm $\times4.2$ mm exhibit the imaging ability of an area of 40 mm $\times40$ mm in the graphite plant with identified defects deep as 3 cm. These results indicate that PMUT has the detection and imaging ability for defects deep in solids, not merely surface within hundreds of micrometers, and it has the potential for 3-D imaging, especially those occasions in limited space.

Real-Time Volumetric Synthetic Aperture Software Beamforming of Row–Column Probe Data

Abstract: Two delay-and-sum beamformers for 3-D synthetic aperture imaging with row–column addressed arrays are presented Both beamformers are software implementations for graphics processing unit (GPU) execution with dynamic apodizations and third-order polynomial subsample interpolation The first beamformer was written in the MATLAB programming language and the second was written in C/C++ with the compute unified device architecture (CUDA) extensions by NVIDIA Performance was measured as volume rate and sample throughput on three different GPUs: a 1050 Ti, a 1080 Ti, and a TITAN V The beamformers were evaluated across 112 combinations of output geometry, depth range, transducer array size, number of virtual sources, floating-point precision, and Nyquist rate or in-phase/quadrature beamforming using analytic signals Real-time imaging defined as more than 30 volumes per second was attained by the CUDA beamformer on the three GPUs for 13, 27, and 43 setups, respectively The MATLAB beamformer did not attain real-time imaging for any setup The median, single-precision sample throughput of the CUDA beamformer was 49, 208, and 335 Gsamples/s on the three GPUs, respectively The throughput of CUDA beamformer was an order of magnitude higher than that of the MATLAB beamformer

Pulse Wave Imaging Coupled With Vector Flow Mapping: A Phantom, Simulation, and In Vivo Study

Abstract: Pulse wave imaging (PWI) is an ultrasound imaging modality that estimates the wall stiffness of an imaged arterial segment by tracking the pulse wave propagation. The aim of the present study is to integrate PWI with vector flow imaging, enabling simultaneous and co-localized mapping of vessel wall mechanical properties and 2-D flow patterns. Two vector flow imaging techniques were implemented using the PWI acquisition sequence: 1) multiangle vector Doppler and 2) a cross-correlation-based vector flow imaging (CC VFI) method. The two vector flow imaging techniques were evaluated in vitro using a vessel phantom with an embedded plaque, along with spatially registered fluid structure interaction (FSI) simulations with the same geometry and inlet flow as the phantom setup. The flow magnitude and vector direction obtained through simulations and phantom experiments were compared in a prestenotic and stenotic segment of the phantom and at five different time frames. In most comparisons, CC VFI provided significantly lower bias or precision than the vector Doppler method ( ${p} ) indicating better performance. In addition, the proposed technique was applied to the carotid arteries of nonatherosclerotic subjects of different ages to investigate the relationship between PWI-derived compliance of the arterial wall and flow velocity in vivo . Spearman’s rank-order test revealed positive correlation between compliance and peak flow velocity magnitude ( ${r}_{s} = {0.90}$ and ${p} ), while significantly lower compliance ( ${p} ) and lower peak flow velocity magnitude ( ${p} ) were determined in older (54–73 y.o.) compared with young (24–32 y.o.) subjects. Finally, initial feasibility was shown in an atherosclerotic common carotid artery in vivo . The proposed imaging modality successfully provided information on blood flow patterns and arterial wall stiffness and is expected to provide additional insight in studying carotid artery biomechanics, as well as aid in carotid artery disease diagnosis and monitoring.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

Abstract: As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation The model with $128\times256$ two fully connected layers gave the best accuracy of 87% It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics

Automatic Quantification of Subsurface Defects by Analyzing Laser Ultrasonic Signals Using Convolutional Neural Networks and Wavelet Transform

Abstract: The conventional machine learning algorithm for analyzing ultrasonic signals to detect structural defects necessarily identifies and extracts either time- or frequency-domain features manually, which has problems in reliability and effectiveness. This work proposes a novel approach by combining convolutional neural networks (CNNs) and wavelet transform to analyze the laser-generated ultrasonic signals for detecting the width of subsurface defects accurately. The novelty of this work is to convert the laser ultrasonic signals into the scalograms (images) via wavelet transform, which are subsequently utilized as the image input for the pretrained CNN to extract the defect features automatically to quantify the width of defects, avoiding the necessity and inaccuracy induced by artificial feature selection. The experimentally validated numerical model that simulates the interaction of laser-generated ultrasonic waves with subsurface defects is first established, which is further utilized to generate adequate laser ultrasonic signals for training the CNN model. A total number of 3104 data are obtained from simulation and experiments, with 2480 simulated signals for training the CNN model and the remaining 620 simulated data together with 4 experimental signals for verifying the performance of the proposed algorithm. This approach achieves the prediction accuracy of 98.5% on validation set, particularly with the prediction accuracy of 100% for the four experimental data. This work proves the feasibility and reliability of the proposed method for quantifying the width of subsurface defects and can be further expanded as a universal approach to various other defects detection, such as defect locations and shapes.

“HIFU Beam:” A Simulator for Predicting Axially Symmetric Nonlinear Acoustic Fields Generated by Focused Transducers in a Layered Medium

Abstract: “HIFU beam” is a freely available software tool that comprises a MATLAB toolbox combined with a user-friendly interface and binary executable compiled from FORTRAN source code ( HIFU beam . (2021). Available: http://limu.msu.ru/node/3555?language=en ). It is designed for simulating high-intensity focused ultrasound (HIFU) fields generated by single-element transducers and annular arrays with propagation in flat-layered media that mimic biological tissues. Numerical models incorporated in the simulator include evolution-type equations, either the Khokhlov–Zabolotskaya–Kuznetsov (KZK) equation or one-way Westervelt equation, for radially symmetric ultrasound beams in homogeneous and layered media with thermoviscous or power-law acoustic absorption. The software uses shock-capturing methods that allow for simulating strongly nonlinear acoustic fields with high-amplitude shocks. In this article, a general description of the software is given along with three representative simulation cases of ultrasound transducers and focusing conditions typical for therapeutic applications. The examples illustrate major nonlinear wave effects in HIFU fields including shock formation. Two examples simulate propagation in water, involving a single-element source (1-MHz frequency, 100-mm diameter, 90-mm radius of curvature) and a 16-element annular array (3-MHz frequency, 48-mm diameter, and 35-mm radius of curvature). The third example mimics the scenario of a HIFU treatment in a “water-muscle-kidney” layered medium using a source typical for abdominal HIFU applications (1.2-MHz frequency, 120-mm diameter, and radius of curvature). Linear, quasi-linear, and shock-wave exposure protocols are considered. It is intended that “HIFU beam” can be useful in teaching nonlinear acoustics; designing and characterizing high-power transducers; and developing exposure protocols for a wide range of therapeutic applications such as shock-based HIFU, boiling histotripsy, drug delivery, immunotherapy, and others.

Recent Advances in Negative Capacitance FinFETs for Low-Power Applications: A Review

Abstract: In the contemporary era of Internet-of-Things (IoT), there is an extensive search for competent devices which can operate at ultralow voltage supply. Due to the restriction of power dissipation, a reduced sub-threshold swing-based device appears to be the perfect solution for efficient computation. To counteract this issue, negative capacitance fin field-effect transistors (NC-FinFETs) came up as the next-generation platform to withstand the aggressive scaling of transistors. The ease of fabrication, process-integration, higher current driving capability, and ability to tailor the short-channel effects (SCEs) are some of the potential advantages offered by NC-FinFETs that attracted the attention of researchers worldwide. The following review emphasizes how this new state-of-art technology supports the persistence of Moore’s law and addresses the ultimate limitation of Boltzmann tyranny by offering a sub-threshold slope (SS) below 60 mV/decade. The article primarily focuses on two parts: 1) the theoretical background of negative capacitance (NC) effect and FinFET devices and 2) the recent progress done in the field of NC-FinFETs. It also highlights the crucial areas that need to be upgraded, to mitigate the challenges faced by this technology and the future prospects of such devices.

Simultaneous Noise Suppression and Incoherent Artifact Reduction in Ultrafast Ultrasound Vascular Imaging

Abstract: Ultrasound vascular imaging based on ultrafast plane wave imaging and singular value decomposition (SVD) clutter filtering has demonstrated superior sensitivity in blood flow detection. However, ultrafast ultrasound vascular imaging is susceptible to electronic noise due to the weak penetration of unfocused waves, leading to a lower signal-to-noise ratio (SNR) at larger depths. In addition, incoherent clutter artifacts originating from strong and moving tissue scatterers that cannot be completely removed create a strong mask on top of the blood signal that obscures the vessels. Herein, a method that can simultaneously suppress the background noise and incoherent artifacts is proposed. The method divides the tilted plane or diverging waves into two subgroups. Coherent spatial compounding is performed within each subgroup, resulting in two compounded data sets. An SVD-based clutter filter is applied to each data set, followed by a correlation between the two data sets to produce a vascular image. Uncorrelated noise and incoherent artifacts can be effectively suppressed with the correlation process, while the coherent blood signal can be preserved. The method was evaluated in wire-target simulations and phantom, in which around 7–10-dB SNR improvement was shown. Consistent results were found in a flow channel phantom with improved SNR by the proposed method (39.9 ± 0.2 dB) against conventional power Doppler (29.1 ± 0.6 dB). Last, we demonstrated the effectiveness of the method combined with block-wise SVD clutter filtering in a human liver, breast tumor, and inflammatory bowel disease data sets. The improved blood flow visualization may facilitate more reliable small vessel imaging for a wide range of clinical applications, such as cancer and inflammatory diseases.

Analytic Global Regularized Backscatter Quantitative Ultrasound

Abstract: Although a variety of techniques have been developed to reduce the appearance of B-mode speckle, quantitative ultrasound (QUS) aims at extracting the hidden properties of the tissue. Herein, we propose two novel techniques to accurately and precisely estimate two important QUS parameters, namely, the average attenuation coefficient and the backscatter coefficient. Both the techniques optimize a cost function that incorporates data and continuity constraint terms, which we call AnaLytical Global rEgularized BackscatteR quAntitative ultrasound (ALGEBRA). We propose two versions of ALGEBRA, namely, 1-D- and 2-D-ALGEBRA. In 1-D-ALGEBRA, the regularized cost function is formulated in the axial direction, and the QUS parameters are calculated for one line of radio frequency (RF) echo data. In 2-D-ALGEBRA, the regularized cost function is formulated for the entire image, and the QUS parameters throughout the image are estimated simultaneously. This simultaneous optimization allows 2-D-ALGEBRA to “see” all the data before estimating the QUS parameters. In both the methods, we efficiently optimize the cost functions by casting it as a sparse linear system of equations. As a result of this efficient optimization, 1-D-ALGEBRA and 2-D-ALGEBRA are, respectively, 600 and 300 times faster than optimization using the dynamic programming (DP) method previously proposed by our group. In addition, the proposed technique has fewer input parameters that require manual tuning. Our results demonstrate that the proposed ALGEBRA methods substantially outperform least-square and DP methods in estimating the QUS parameters in phantom experiments.

Effect of Curie Temperature on Ferroelectric Tunnel FET and Its RF/Analog Performance

Abstract: In this article, a numerical simulation study for the ferroelectric gate oxide tunnel field-effect transistor (Ferro-TFET) has been presented. The performance of the device is analyzed following Landau’s theory and its behavior in the temperature range of 200–300 K. A minimum subthreshold swing and maximum transconductance is obtained around the Curie temperature ( ${T}_{C}$ ) of 580 ± 10 K for the simulated device. The simulation result is supported by the simple analytical model. The temperature sensitivity analysis is studied on different analog and RF figure of merits for Ferro-TFET. In this study, the Ferro-TFET shows the remarkable result in reducing the detrimental effect of mobility degradation at high gate voltage and performance degradation at high temperatures as compared to conventional TFETs and MOSFET. Therefore, at Curie temperature, the operation of the Ferro-TFET shows the remarkable results for analog and RF applications.

Automatic Detection and Measurement of Spinous Process Curve on Clinical Ultrasound Spine Images

Abstract: The ultrasound (US) imaging technique has been applied to scoliosis assessment, and the proxy Cobb angle can be acquired on the US coronal images. The spinous process angle (SPA) is a valuable parameter to indicate 3-D deformity of spine. However, the SPA cannot be measured on US images since the spinous process (SP) is merged in the soft tissue layer and impossible to be identified on the coronal view directly. A new method based on the gradient vector flow (GVF) snake model was proposed to automatically locate SP position on the US transverse images, and the density-based spatial clustering of application with noise (DBSCAN) was used to remove the outliers out of the detected location results. With marking the SP points on the US coronal image, the SP curve was interpolated and the SPA was measured. The algorithm was evaluated on 50 subjects with various severity of scoliosis, and two raters measured the SPA on both US images and radiographs manually. The mean absolute differences (MADs) of SPAs obtained from the two modalities were 3.4° ± 2.4° and 3.6° ± 2.8° for the two raters, respectively, which were less than the clinical acceptance error (5°), and the results reported a good linear correlation ( ${r} > 0.85$ ) between the US method and radiography. It indicates that the proposed method can be a promising approach for SPA measurement using the US imaging technique.

A Prototype Therapy System for Boiling Histotripsy in Abdominal Targets Based on a 256-Element Spiral Array

Abstract: Boiling histotripsy (BH) uses millisecond-long ultrasound (US) pulses with high-amplitude shocks to mechanically fractionate tissue with potential for real-time lesion monitoring by US imaging. For BH treatments of abdominal organs, a high-power multielement phased array system capable of electronic focus steering and aberration correction for body wall inhomogeneities is needed. In this work, a preclinical BH system was built comprising a custom 256-element 1.5-MHz phased array (Imasonic, Besancon, France) with a central opening for mounting an imaging probe. The array was electronically matched to a Verasonics research US system with a 1.2-kW external power source. Driving electronics and software of the system were modified to provide a pulse average acoustic power of 2.2 kW sustained for 10 ms with a 1–2-Hz repetition rate for delivering BH exposures. System performance was characterized by hydrophone measurements in water combined with nonlinear wave simulations based on the Westervelt equation. Fully developed shocks of 100-MPa amplitude are formed at the focus at 275-W acoustic power. Electronic steering capabilities of the array were evaluated for shock-producing conditions to determine power compensation strategies that equalize BH exposures at multiple focal locations across the planned treatment volume. The system was used to produce continuous volumetric BH lesions in ex vivo bovine liver with 1-mm focus spacing, 10-ms pulselength, five pulses/focus, and 1% duty cycle.