Search query: ghz ultrasound neural

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abstract: Emergent trends in the device development for neural prosthetics have focused on establishing stimulus localization, improving longevity through immune compatibility, reducing energy re quirements, and embedding active control in the devices. Ultrasound stimulation can single handedly address several of these challenges. Ultrasonic stimulus of neurons has been studied extensively from 100 kHz to 10 MHz, with high penetration but less localization. In this paper, a chip scale device consisting of piezoelectric Aluminum Nitride ultrasonic transducers was engineered to deliver gigahertz (GHz) ultrasonic stimulus to the human neural cells. These devices provide a path towards complementary metal oxide semiconductor (CMOS) integration towards fully controllable neural devices. At GHz frequencies, ultrasonic wavelengths in water are a few microns and have an absorption depth of 10 20 um This confinement of energy can be used to control stimulation volume within a single neuron. This paper is the first proof of concept study to demonstrate that GHz ultrasound can stimulate neurons in vitro By utilizing optical calcium imaging, which records calcium ion flux indicating occurrence of an action potential, this paper demonstrates that an application of a nontoxic dosage of GHz ultrasonic waves \ge 0.05\frac{W}{c{m}{2} 0.05 W c m 2 caused an average normalized fluorescence intensity recordings >1.40 for the calcium transients. Electrical effects due to chip scale ultrasound delivery was discounted as the sole mechanism in stimulation, with effects tested at a 0.01 statistical significance amongst all intensities and con trol groups. Ionic transients recorded optically were confirmed to be mediated by ion channels and experimental data suggests an insignificant thermal contributions to stimulation, with a predicted increase of 0.03 o C for $1.2\frac{W}{c{m}{2}\cdot 1.2 W c m 2 This paper paves the experimental framework to further explore chip scale axon and neuron specific neural stimulation, with future applications in neural prosthetics, chip scale neural engineering, and extensions to different tissue and cell types.
author_names: Priyadarshini Balasubramanian

Ashutosh Kumar Singh

Chris Xu

Amit Lal
title: GHz Ultrasonic Chip Scale Device Induces Ion Channel Stimulation in Human Neural Cells
venue: Scientific Reports
year: 2020

abstract: A novel neural stimulation system is demonstrated that is capable of delivering tunable ultrasonic stimulus at the cellular level and additionally providing feedback information towards dosage regulation. GHz aluminum nitride ultrasound transducers provide continuous wave stimulation to in vitro, retinoic acid differentiated SH SY5Y neuroblastoma cells at 1.05 GHz. Results show stimulation of optically recorded calcium ion transients in SH SY5Y cells, with during stimulation increases in FIFo of 1.5 0.328, differing from pre stimulation with p 0.01. The ultrasonic pulse echo return signal of the ultrasound transducer is used to estimate and tune the ultrasound dosage using transducer design, input stimulus, and interface medium. Results and theory suggest that higher acoustic impedance cell culture medium and scaffolds will allow for delivery of higher ultrasonic intensity to neural cells. Differences in echo return amplitudes for various interface medium and input stimulus amplitudes are reported at $a$ 0.01.
author_names: Priyadarshini Balasubramanian

Amit Lal
title: Cellular Localization and Dosage Regulation of Neural Stimulation Enabled by 1.05 GHz Ultrasonics
venue: 2018 IEEE International Ultrasonics Symposium (IUS)
year: 2018

abstract: Abstract Failures in high voltage insulators can cause significant injuries for the consumers, since these piece of equipment play a fundamental role for the transmission lines operation. Insulator inspection and diagnosis techniques are widely researched, varying mainly regarding to the data collection method, as example: ultrasound, thermal imaging, leakage current, corona, electric field and RF signals. In this article, the RF signals radiated by the insulators were used to classify them into three levels of pollution: "light" "medium" and "heavy" For this, we used the standard IEC 60815 1 as reference, which takes into account the ESDD and NSDD indices. The proposed classification method was based on the wavelet transform, for signal processing, and artificial neural network for the classification. The success rate was approximately 95% in the 1.2 1.4 GHz frequency range.
author_names: Pedro H V Rocha

Edson Guedes da Costa

Alexandre Jean Rene Serres

George Victor Rocha Xavier

J E B Peixoto

R L Lins
title: Inspection in overhead insulators through the analysis of the irradiated RF spectrum
year: 2019

abstract: In this article, two novel deep learning methods are proposed for displacement estimation in ultrasound elastography (USE) Although convolutional neural networks (CNNs) have been very successful for displacement estimation in computer vision, they have been rarely used for USE. One of the main limitations is that the radio frequency (RF) ultrasound data, which is crucial for precise displacement estimation, has vastly different frequency characteristics compared with images in computer vision. Top rank CNN methods used in computer vision applications are mostly based on a multilevel strategy, which estimates finer resolution based on coarser ones. This strategy does not work well for RF data due to its large high frequency content. To mitigate the problem, we propose modified pyramid warping and cost volume network (MPWC Net) and RFMPWC Net, both based on PWC Net, to exploit information in RF data by employing two different strategies. We obtained promising results using networks trained only on computer vision images. In the next step, we constructed a large ultrasound simulation database and proposed a new loss function to fine tune the network to improve its performance. The proposed networks and well known optical flow networks as well as state of the art elastography methods are evaluated using simulation, phantom, and in vivo data. Our two proposed networks substantially outperform current deep learning methods in terms of contrast to noise ratio (CNR) and strain ratio (SR) Also, the proposed methods perform similar to the state of the art elastography methods in terms of CNR and have better SR by substantially reducing the underestimation bias.
author_names: Ali Kafaei Zad Tehrani

Hassan Rivaz
title: Displacement Estimation in Ultrasound Elastography Using Pyramidal Convolutional Neural Network
venue: IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
year: 2020

abstract: We introduce an optimized physics informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate. PINNs are neural networks that can combine data and physics in the learning process by adding the residuals of a system of Partial Differential Equations to the loss function. Our PINN is supervised with realistic ultrasonic surface acoustic wave data acquired at a frequency of 5 MHz. The ultrasonic surface wave data is represented as a surface deformation on the top surface of a metal plate, measured by using the method of laser vibrometry. The PINN is physically informed by the acoustic wave equation and its convergence is sped up using adaptive activation functions. The adaptive activation function uses a scalable hyperparameter in the activation function, which is optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The usage of adaptive activation function significantly improves the convergence, notably observed in the current study. We use PINNs to estimate the speed of sound of the metal plate, which we do with an error of 1\ and then, by allowing the speed of sound to be space dependent, we identify and characterize the crack as the positions where the speed of sound has decreased. Our study also shows the effect of sub sampling of the data on the sensitivity of sound speed estimates. More broadly, the resulting model shows a promising deep neural network model for ill posed inverse problems.
author_names: Khemraj Shukla

Patricio Clark di Leoni

James L Blackshire

Daniel Murray Sparkman

George Em Karniadakis
title: Physics informed neural network for ultrasound nondestructive quantification of surface breaking cracks
venue: ArXiv
year: 2020

abstract: Multichannel electrophysiological sensors and stimulators, especially those used for studying the nervous system, are most commonly based on monolithic microelectrode arrays. Such architecture limits the spatial flexibility of individual electrode placement, posing constraints for scaling to a large number of nodes, particularly across non contiguous locations. We describe the design and fabrication of sub millimeter size electronic microchips "Neurograins" which autonomously perform neural sensing or electrical microstimulation, with emphasis on their wireless networking and powering. An ~1 GHz electromagnetic transcutaneous link to an external telecom hub enables bidirectional communication and control at the individual neurograin level. The link operates on a customized time division multiple access (TDMA) protocol designed to scale up to 1000 neurograins. The system is demonstrated as a cortical implant in a small animal (rat) model with anatomical limitations restricting the implant to 48 neurograins. We suggest that the neurograin approach can be generalized to overcome many scalability issues for wireless sensors and actuators as implantable microsystems.
author_names: Jihun Lee

Vincent W Leung

Ah-Hyoung Lee

Jiannan Huang

Peter M Asbeck

Patrick P Mercier

Steven Shellhammer

Lawrence E Larson

Farah Laiwalla

Arto V Nurmikko
title: Wireless Ensembles of Sub mm Microimplants Communicating as a Network near 1 GHz in a Neural Application
year: 2020

abstract: Accurate tracking of tissue motion is critically important for several ultrasound elastography methods. In this study, we investigate the feasibility of using three published convolution neural network (CNN) models built for optical flow (hereafter referred to as CNN based tracking) by the computer vision community for breast ultrasound strain elastography. Elastographic datasets produced by finite element and ultrasound simulations were used to retrain three published CNN models: FlowNet CSS, PWC Net, and LiteFlowNet. After retraining, the three improved CNN models were evaluated using computer simulated and tissue mimicking phantoms, and in vivo breast ultrasound data. CNN based tracking results were compared with two published two dimensional (2D) speckle tracking methods: coupled tracking and GLobal Ultrasound Elastography (GLUE) methods. Our preliminary data showed that, based on the Wilcoxon rank sum tests, the improvements due to retraining were statistically significant (p 0.05) for all three CNN models. We also found that the PWC Net model was the best neural network model for data investigated, and its overall performance was on par with the coupled tracking method. CNR values estimated from in vivo axial and lateral strain elastograms showed that the GLUE algorithm outperformed both the retrained PWC Net model and the coupled tracking method, though the GLUE algorithm exhibited some biases. The PWC Net model was also able to achieve approximately 45 frames/second for 2D speckle tracking data investigated.
author_names: Bo Peng

Yuhong Xian

Quan Zhang

Jingfeng Jiang
title: Neural network based Motion Tracking for Breast Ultrasound Strain Elastography: An Initial Assessment of Performance and Feasibility
venue: Ultrasonic imaging
year: 2020

abstract: The measurement of human vital signs is a highly important task in a variety of environments and applications. Most notably, the electrocardiogram (ECG) is a versatile signal that could indicate various physical and psychological conditions, from signs of life to complex mental states. The measurement of the ECG relies on electrodes attached to the skin to acquire the electrical activity of the heart, which imposes certain limitations. Recently, due to the advancement of wireless technology, it has become possible to pick up heart activity in a contactless manner. Among the possible ways to wirelessly obtain information related to heart activity, methods based on mm wave radars proved to be the most accurate in detecting the small mechanical oscillations of the human chest resulting from heartbeats. In this paper, we presented a method based on a continuous wave Doppler radar coupled with an artificial neural network (ANN) to detect heartbeats as individual events. To keep the method computationally simple, the ANN took the raw radar signal as input, while the output was minimally processed, ensuring low latency operation <1 s) The performance of the proposed method was evaluated with respect to an ECG reference "ground truth" in an experiment involving 21 healthy volunteers, who were sitting on a cushioned seat and were refrained from making excessive body movements. The results indicated that the presented approach is viable for the fast detection of individual heartbeats without heavy signal preprocessing.
author_names: Nebojsa Malesevic

Vladimir L Petrovic

Minja Belic

Christian Antfolk

Veljko R Mihajlovic

Milica M Jankovic
title: Contactless Real Time Heartbeat Detection via 24 GHz Continuous Wave Doppler Radar Using Artificial Neural Networks
venue: Sensors
year: 2020

abstract: To assist radiologists in breast cancer classification in automated breast ultrasound (ABUS) imaging, we propose a computer aided diagnosis based on a convolutional neural network (CNN) that classifies breast lesions as benign and malignant. The proposed CNN adopts a modified Inception v3 architecture to provide efficient feature extraction in ABUS imaging. Because the ABUS images can be visualized in transverse and coronal views, the proposed CNN provides an efficient way to extract multiview features from both views. The proposed CNN was trained and evaluated on 316 breast lesions (135 malignant and 181 benign) An observer performance test was conducted to compare five human reviewers' diagnostic performance before and after referring to the predicting outcomes of the proposed CNN. Our method achieved an area under the curve (AUC) value of 0.9468 with five folder cross validation, for which the sensitivity and specificity were 0.886 and 0.876, respectively. Compared with conventional machine learning based feature extraction schemes, particularly principal component analysis (PCA) and histogram of oriented gradients (HOG) our method achieved a significant improvement in classification performance. The proposed CNN achieved a >10% increased AUC value compared with PCA and HOG. During the observer performance test, the diagnostic results of all human reviewers had increased AUC values and sensitivities after referring to the classification results of the proposed CNN, and four of the five human reviewers' AUCs were significantly improved. The proposed CNN employing a multiview strategy showed promise for the diagnosis of breast cancer, and could be used as a second reviewer for increasing diagnostic reliability.
author_names: Eun Jung Choi

Younhee Choi

Hao Zhang

Gong Yong Jin

Seok-Bum Ko
title: Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning.
venue: Ultrasound in medicine biology
year: 2020

abstract: BackgroundIn this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated.MethodsThe ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared.ResultsThe artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859) This finding indicates a higher diagnostic accuracy (p 0.0434) The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5% 95.22% 80.99% and 90.31% respectively, and the performance did not significantly differ from that of the radiologists (p 0.05) The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98% p 0.026).ConclusionsCompared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.
author_names: Lei Wang

Shujian Yang

Shanshan Yang

Cheng Zhao

Guangye Tian

Yuxiu Gao

Yongjian Chen

Yun Lu
title: Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network
venue: World Journal of Surgical Oncology
year: 2019