Search

Deep learning (DL) can fail when there are data mismatches between training and testing data. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imaging. Therefore, mitigating effects of such data mismatches is essential for wider clinical adoption of DL powered ultrasound imaging. To mitigate the effects, ideally we need to collect a large training set at each scanner setting. However, acquiring such training sets is expensive. Another approach could be training on a subset of imaging settings, which makes the data generation less expensive. However, there will still be generalization issues. As an alternative approach that is inexpensive and generalizable, we propose to collect a large training set at a single setting and a small calibration set at each scanner setting. Then, the calibration set will be used to calibrate data mismatches by using a signals and systems perspective. We tested the proposed solution to classify two phantoms. To investigate generalizability of the proposed solution, we calibrated three types of data mismatches: pulse frequency, focus and output power mismatches. To calibrate the setting mismatches, we calculated the setting transfer functions. The CNN trained with no calibration resulted in mean classification accuracies of 55.3%, 64.4% and 70.3% for pulse frequency, focus and output power mismatches, respectively. By using the setting transfer functions, which allowed a matching of the training and testing domains, we obtained mean accuracies of 95.3%, 92.99% and 99.32%, respectively. Therefore, the incorporation of the setting transfer functions between scanner settings can provide an economical means of generalizing a DL model for specific classification tasks where scanner settings are not fixed by the operator.

Plane-wave imaging (PWI) with angular compounding has gained in popularity over recent years, because it provides high frame rates and good image properties. However, most linear arrays used in clinical practice have a pitch that is equal to than the wavelength of ultrasound. Hence, the presence of grating lobes is a concern for PWI using multiple transmit angles. The presence of grating lobes produces clutter in images and reduces the ability to observe tissue contrast. Techniques to reduce or eliminate the presence of grating lobes for PWI using multiple angles will result in improved image quality. Null subtraction imaging (NSI) is a nonlinear beamforming technique that has been explored for improving the lateral resolution of ultrasonic imaging. However, the apodization scheme used in NSI also eliminates or greatly reduces the presence of grating lobes. Imaging tasks using NSI were evaluated in simulations and physical experiments involving tissue-mimicking phantoms and rat tumors in vivo. Images created with NSI were compared with images created using traditional delay and sum (DAS) with Hann apodization and images created using a generalized coherence factor (GCF). NSI was observed to greatly reduce the presence of grating lobes in ultrasonic images, compared to DAS with Hann and GCF, while maintaining spatial resolution and contrast in the images. Therefore, NSI can provide a novel means of creating images using PWI with multiple steering angles on clinically available linear arrays while reducing the adverse effects associated with grating lobes.

Tissue characterization based on the backscatter coefficient (BSC) can be degraded by acoustic nonlinearity. Often, this degradation is due to the method used for obtaining a reference spectrum, i.e., using a planar reference in water compared to a reference phantom approach resulted in more degradation. We hypothesize that an in situ calibration approach can improve BSC estimates in the nonlinear regime compared to using the reference phantom approach. The in situ calibration target provides a reference within the medium being interrogated and, therefore, nonlinear effects would already be contained in the in situ reference signal. Simulations and experiments in phantoms and in vivo were performed. A 2 mm diameter titanium bead was embedded in the interrogated media. An L9-4/38 probe (BK Ultrasound, Peabody, MA) and an analysis bandwidth from 4.5 to 7.4 MHz were used in experiments. Radiofrequency data from the sample, bead, and reference phantoms were acquired at a quasi-linear baseline power level and at further increments of output power. Better agreement between the BSC obtained at low power compared to high power was observed for the in situ calibration compared to the reference phantom approach.

Ensuring the consistency of spectral-based quantitative ultrasound estimates in vivo necessitates accounting for diffraction, system effects, and propagation losses encountered in the tissue. Accounting for diffraction and system effects is typically achieved through planar reflector or reference phantom methods; however, neither of these is able to account for the tissue losses present in vivo between the ultrasound probe and the region of interest. In previous work, the feasibility of small titanium beads as in situ calibration targets (0.5–2 mm in diameter) was investigated. In this study, the importance of bead size for the calibration signal, the role of multiple echoes coming from the calibration bead, and sampling of the bead signal laterally through beam translation were examined. This work demonstrates that although the titanium beads naturally produce multiple reverberant echoes, time-windowing of the first echo provides the smoothest calibration spectrum for backscatter coefficient calculation. When translating the beam across the bead, the amplitude of the echo decreases rapidly as the beam moves across and past the bead. Therefore, to obtain consistent calibration signals from the bead, lateral interpolation is needed to approximate signals coming from the center of the bead with respect to the beam.

Acoustic communication has been gaining traction as an alternative communication method in nontraditional media, such as underwater or through tissue. Acoustic propagation is known to be a nonlinear phenomenon; nonlinear propagation of acoustic waves in soft tissues at biomedical frequencies and intensities has been widely demonstrated. However, the effects of acoustic nonlinearity on communication performance in biological tissues have not yet been examined. In this work, nonlinear propagation of a communication signal in soft tissues is analyzed. The relationship between communication parameters (signal amplitude, bandwidth, and center frequency) and nonlinear distortion of the communication signal propagating in soft tissues with different acoustic properties is investigated. Simulated experiments revealed that, unlike linear channels, bit error rates increase as signal amplitude and bandwidth increase. Linear and decision feedback equalizers fail to address the increased error rates. When tissue properties and transmission parameters can be estimated, receivers based on maximum likelihood sequence estimation approach the performance of an ideal receiver in an ideal additive white Gaussian noise channel.

Focused ultrasound (FUS) therapies induce therapeutic effects in localized tissues using either temperature elevations or mechanical stresses caused by an ultrasound wave. During an FUS therapy, it is crucial to continuously monitor the position of the FUS beam in order to correct for tissue motion and keep the focus within the target region. Toward the goal of achieving real-time monitoring for FUS therapies, we have developed a method for the real-time visualization of an FUS beam using ultrasonic backscatter. The intensity field of an FUS beam was reconstructed using backscatter from an FUS pulse received by an imaging array and then overlaid onto a B-mode image captured using the same imaging array. The FUS beam visualization allows one to monitor the position and extent of the FUS beam in the context of the surrounding medium. Variations in the scattering properties of the medium were corrected in the FUS beam reconstruction by normalizing based on the echogenicity of the coaligned B-mode image. On average, normalizing by echogenicity reduced the mean square error between FUS beam reconstructions in nonhomogeneous regions of a phantom and baseline homogeneous regions by 21.61. FUS beam visualizations were achieved, using a single diagnostic imaging array as both an FUS source and an imaging probe, in a tissue-mimicking phantom and a rat tumor in vivo with a frame rate of 25–30 frames/s.

Quantitative ultrasound (QUS) was used to classify rabbits that were induced to have liver disease by placing them on a fatty diet for a defined duration and/or periodically injecting them with CCl(4). The ground truth of the liver state was based on lipid liver percents estimated via the Folch assay and hydroxyproline concentration to quantify fibrosis. Rabbits were scanned ultrasonically in vivo using a SonixOne scanner and an L9–4/38 linear array. Liver fat percentage was classified based on the ultrasonic backscattered radio-frequency (RF) signals from the livers using either QUS or a 1D convolutional neural network (CNN). Use of QUS parameters with linear regression and canonical correlation analysis (CCA) demonstrated that the QUS parameters could differentiate between livers with lipid levels above or below 5%. However, the QUS parameters were not sensitive to fibrosis. The CNN was implemented by analyzing raw RF ultrasound signals without using separate reference data. The CNN output the classification of liver as either above or below a threshold of 5% fat level in the liver. The CNN outperformed the classification utilizing the QUS parameters combine with a support vector machine (SVM) in differentiating between low and high lipid liver levels, i.e., accuracies of 74% versus 59% on the testing data. Therefore, while the CNN did not provide a physical interpretation of the tissue properties, e.g., attenuation of the medium or scatterer properties, the CNN had much higher accuracy in predicting fatty liver state and did not require an external reference scan.

The use of wireless implanted medical devices (IMDs) is growing because they facilitate monitoring of patients at home and during normal activities, reduce the discomfort of patients, and reduce the likelihood of infection associated with trailing wires. Currently, radio frequency (RF) electromagnetic waves are the most commonly used method for communicating wirelessly with IMDs. However, due to the restrictions on the available bandwidth and the employable power, data rates of RF-based IMDs are limited to 267 kb/s. Considering standard definition video streaming requires data rates of 1.2 Mb/s and high definition requires 3 Mb/s, it is not possible to use the RF electromagnetic communications for high data rate communication applications such as video streaming. In this work, an alternative method that utilizes ultrasonic waves to relay information at high data rates is introduced. An advanced quadrature amplitude modulation (QAM) modem with phase-compensating, sparse decision feedback equalizer (DFE) is tailored to realize the full potential of the ultrasonic channel through biological tissues. The proposed system is tested in a variety of scenarios, including both simulations with finite impulse response (FIR) channel models, and real physical transmission experiments with ex vivo beef liver and pork chop samples as well as in situ rabbit abdomen. Consequently, the simulations demonstrated that video-capable data rates can be achieved with millimeter-sized transducers. Real physical experiments confirmed data rates of 6.7, 4.4, 4, and 3.2 Mb/s through water, ex vivo beef liver, ex vivo pork chop, and in situ rabbit abdomen, respectively.

In response to the shortage of ventilators caused by the COVID-19 pandemic, many organizations have designed low-cost emergency ventilators. Many of these devices are pressure-cycled pneumatic ventilators, which are easy to produce but often do not include the sensing or alarm features found on commercial ventilators. This work reports a low-cost, easy-to-produce electronic sensor and alarm system for pressure-cycled ventilators that estimates clinically useful metrics such as pressure and respiratory rate and sounds an alarm when the ventilator malfunctions. A low-complexity signal processing algorithm uses a pair of nonlinear recursive envelope trackers to monitor the signal from an electronic pressure sensor connected to the patient airway. The algorithm, inspired by those used in hearing aids, requires little memory and performs only a few calculations on each sample so that it can run on nearly any microcontroller., Comment: Open-source hardware and software: https://rapidalarm.github.io/

The ultrasonic attenuation coefficient (ACE) can be used to classify tissue state. Pulse-echo spectral-based attenuation estimation techniques, such as the spectral-log-difference method (SLD), account for beam diffraction effects using a reference phantom having a sound speed close to the sound speed of the sample. Methods like SLD assume linear propagation of ultrasound and do not account for potential acoustic nonlinear distortion of the backscattered power spectra in both sample and reference. In this study, the ACE of a sample was computed and compared using the SLD with two independent references (high attenuating and low attenuating phantoms but with similar B/A values) and over several pressure levels. Both numerical and physical tissue-mimicking phantoms were used in the study. The results indicated that the biases in ACE increased when using a reference having low attenuation, whereas the high attenuating reference produced more consistent ACE. Furthermore, increments in ACE vs input pressure were correlated to the log-ratio of Gol'dberg numbers between the sample and reference ([Formula: see text] in simulations and [Formula: see text] in experiments). Therefore, the results suggest that to reduce bias in ACE using spectral-based methods, both the sound speed and the Gol'dberg number of the reference phantom should be matched to the sample.

Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL powered biomedical ultrasound imaging is that acquiring large and diverse datasets is expensive in clinical settings, which is a requirement for successful DL implementation. Hence, there is a constant need for developing data-efficient DL techniques to turn DL powered biomedical ultrasound imaging into reality. In this work, we develop a data-efficient deep learning training strategy, which we named \textit{Zone Training}. In \textit{Zone Training}, we propose to divide the complete field of view of an ultrasound image into multiple zones associated with different regions of a diffraction pattern and then, train separate DL networks for each zone. The main advantage of \textit{Zone Training} is that it requires less training data to achieve high accuracy. In this work, three different tissue-mimicking phantoms were classified by a DL network. The results demonstrated that \textit{Zone Training} required a factor of 2-5 less training data to achieve similar classification accuracies compared to a conventional training strategy., 9 pages, 8 figures. Submitted to IEEE TUFFC

Significance Biomedical application of mechanophores is the next frontier in polymer mechanochemistry. We report the concept, mechanochemical dynamic therapy (MDT), that utilizes remote, ultrasound-triggered mechanophore activation to enable anticancer activities. We selected an azo-based mechanophore to generate reactive free radicals (FRs) under the control of high-intensity focused ultrasound (HIFU), which subsequently produced ROS. We investigated two sets of in vitro mouse cancer models: 1) melanoma (B16F10) and 2) breast cancer (E0771). Inhibition of growth and decreases in viabilities of both B16F10 and E0771 were observed in correlation to the release of ROS by mechanophore activation. By circumventing the known issues in photodynamic therapy and sonodynamic therapy, we anticipate MDT to be a powerful anticancer tool complementary to other existing cancer treatments., Mechanophores are molecular motifs that respond to mechanical perturbance with targeted chemical reactions toward desirable changes in material properties. A large variety of mechanophores have been investigated, with applications focusing on functional materials, such as strain/stress sensors, nanolithography, and self-healing polymers, among others. The responses of engineered mechanophores, such as light emittance, change in fluorescence, and generation of free radicals (FRs), have potential for bioimaging and therapy. However, the biomedical applications of mechanophores are not well explored. Herein, we report an in vitro demonstration of an FR-generating mechanophore embedded in biocompatible hydrogels for noninvasive cancer therapy. Controlled by high-intensity focused ultrasound (HIFU), a clinically proven therapeutic technique, mechanophores were activated with spatiotemporal precision to generate FRs that converted to reactive oxygen species (ROS) to effectively kill tumor cells. The mechanophore hydrogels exhibited no cytotoxicity under physiological conditions. Upon activation with HIFU sonication, the therapeutic efficacies in killing in vitro murine melanoma and breast cancer tumor cells were comparable with lethal doses of H2O2. This process demonstrated the potential for mechanophore-integrated HIFU combination as a noninvasive cancer treatment platform, named “mechanochemical dynamic therapy” (MDT). MDT has two distinct advantages over other noninvasive cancer treatments, such as photodynamic therapy (PDT) and sonodynamic therapy (SDT). 1) MDT is ultrasound based, with larger penetration depth than PDT. 2) MDT does not rely on sonosensitizers or the acoustic cavitation effect, both of which are necessary for SDT. Taking advantage of the strengths of mechanophores and HIFU, MDT can provide noninvasive treatments for diverse cancer types.

Ultrafast ultrasound imaging is essential for advanced ultrasound imaging techniques such as ultrasound localization microscopy (ULM) and functional ultrasound (fUS). Current ultrafast ultrasound imaging is challenged by the ultrahigh data bandwidth associated with the radio frequency (RF) signal, and by the latency of the computationally expensive beamforming process. As such, continuous ultrafast data acquisition and beamforming remain elusive with existing software beamformers based on CPUs or GPUs. To address these challenges, the proposed work introduces a novel method of implementing an ultrafast ultrasound beamformer specifically for ultrafast plane wave imaging (PWI) on a field programmable gate array (FPGA) by using high-level synthesis. A parallelized implementation of the beamformer on a single FPGA was proposed by 1) utilizing a delay compression technique to reduce the delay profile size, which enables both run-time pre-calculated delay profile loading from external memory and delay reuse 2) vectorizing channel data fetching which is enabled by delay reuse, and 3) using fixed summing networks to reduce consumption of logic resources. Our proposed method presents two unique advantages over current FPGA beamformers: 1) high scalability that allows fast adaptation to different FPGA resources and beamforming speed demands by using Xilinx High-Level Synthesis as the development tool, and 2) allow a compact form factor design by using a single FPGA to complete the beamforming instead of multiple FPGAs. With the proposed method, a sustainable average beamforming rate of 4.83 G samples/second in terms of input raw RF sample was achieved. The resulting image quality of the proposed beamformer was compared with the software beamformer on the Verasonics Vantage system for both phantom imaging and in vivo imaging of a mouse brain.

In this work, we demonstrate a novel non-linear beamforming technique, called null-subtraction imaging (NSI), to improve spatial resolution and image contrast of ultrafast, power Doppler microvessel imaging without significantly increasing computational cost compared to traditional delay and sum (DAS). NSI is a non-linear beamforming technique that operates on receive data by subtracting envelopes of beamforming results from three apodization windows. An array transducer and Verasonics Vantage 256 system were used to capture 1600 frames of raw ultrasonic radio frequency (RF) channel data by scanning a rat brain using nine plane waves spanning angles between -4° and 4° at a pulse repetition frequency of 1000 Hz. A singular value decomposition filter was applied on the raw RF channel data to filter out the tissue signal. Coherent compounding was performed individually on the beamformed result for each apodization window. Next, the subtraction of the envelopes for each apodization was conducted to form the NSI image. A total of 1,600 frames of NSI images were accumulated to form the final image. The NSI based ultrafast power Doppler microvessel imaging provided a ten-fold improvement in spatial resolution and a three-fold improvement in image contrast compared with the traditional DAS-based ultrafast power Doppler microvessel imaging.

Quantitative Ultrasound (QUS) is an imaging method that has demonstrated the ability to detect the response of breast cancer to neoadjuvant chemotherapy. QUS is sensitive to tumor cell death, which is a hallmark of response. Traditionally, QUS requires an external reference material, e.g., reference phantom, to calibrate the system for backscatter coefficient based parameters. However, this approach does not account for losses in vivo and requires additional scans. To alleviate these issues, we used an in situ calibration target, i.e., a 2-mm diameter titanium bead, to obtain a reference signal. To automate the procedure to locate the bead in a B-mode image, we constructed a data mining method to distinguish the bead signal from other similar signals recorded from tumors having an embedded bead. The method was able to successfully locate the bead signal in a tumor image with good accuracy.

In our previous work, we proposed a calibration method for deep learning (DL) to mitigate the effects of acquisition-related data mismatches in the context of tissue characterization. We showed that the “setting” transfer function can transfer deep learning models between imaging settings. We now extend the calibration method to transfer deep learning models between ultrasound machines. This can lead to reduced cost of model development and also improved understanding of the issues related to the security of deep learning based algorithms in biomedical ultrasound imaging. We gathered four datasets from three different scanners: (i) a SonixOne Machine with an L9-4 transducer, (ii) a Verasonics Vantage 128 Ultrasound Machine with an L9-4 transducer using line by line acquisition, (iii) a Verasonics Vantage 128 Ultrasound Machine with an L9-4 transducer using plane wave compounding, and (iv) a Siemens Antares Ultrasound Machine with an VF10-5 transducer. We used the first dataset as training data and the other datasets as testing data. The DL algorithm learned how to classify two tissue mimicking phantoms. The classification accuracy jumped to 90% from 50% for the second dataset, 70% from 50% for the third dataset, and 61% from 56% for the fourth dataset after incorporating the calibration method. Therefore, the results confirm that a transfer function approach can be used to transfer learning models between scanners for the purpose of classifying materials based on ultrasonic backscatter.

During the treatment of breast cancer via neoadjuvant chemotherapy (NAC), radiological clips are used to track lesions. Marking lesions allows them to be located and distinguished from their surroundings post-NAC, but morphological changes to the treated regions due to NAC can affect the visibility of marking clips in ultrasound, sometimes to a degree which requires the use of alternative, less comfortable modalities to visualize the clips in preparation for procedures, such as surgical resection or biopsy. In previous work, we proposed an electronic clip design leveraging active communication with an ultrasound imaging probe, improving visibility and differentiation of clips in ultrasound. The transmitted signal in a prototype of this design was successfully localized and identified by the ultrasound system in post-processing. Presently, we refine the device design by using pseudorandom noise (PN) codes as the ultrasonic identification signals to improve localization, and adjust the system design to allow multiple clips to be active and identified with greater accuracy in the imaging field in real time. [Work supported by a grant from the NIH (R21EB030743) and a fellowship from the Cancer Center at Illinois (CST EP082021).]

In this work, we propose a novel method of implementing an ultrafast ultrasound beamformer for plane wave imaging (PWI) on a field programmable gate array (FPGA). First, a modified delay calculation method was proposed to (1) separate the transmit and receive delay, (2) reduce the size of delay profile, and (3) enable parallel beamforming by delay reuse and data vectorization. Second, a parallelized implementation of beamformer on single FPGA was proposed by (1) loading pre-calculated delay profile from external memory instead of calculating delay on run-time, (2) vectorizing channel data fetching, (3) compensating transmit and receive delays separately, and (4) using fixed summing networks to reduce consumption of logic resources. The proposed method was also highly scalable, which was demonstrated by implementing the beamformer with different beamforming rates ranging from 2.4 G to 9.6 G samples per second to three different sizes of FPGAs ranging from entry-level FPGA to high-end FPGA. The power consumption was less than 3 watts for 2.4 G samples per second beamforming rate, which demonstrates the possibility of implementing ultrafast ultrasound imaging on handheld probe. The FPGA beamformer’s results were compared with Verasonics CPU beamformer’s result to verify that the image quality was not compromised for speed.

Ultrasound tomography is an imaging technique for the breast that can create maps of the sound speed and acoustic attenuation simultaneously. By use of wave-based methods, images of speed of sound are of high quality, but the attenuation images are of inferior quality. The Spectral Log Difference (SLD) is a technique based on the ultrasonic backscatter that can provide estimates of the attenuation coefficient slope (ACS). However, SLD requires a large amount of data to provide a low variance of the estimate. This estimate variance can be reduced through compounding, which decreases spatial resolution of SLD images. Due to the nature of tomography, the tradeoff between estimate variance of SLD and spatial resolution can be reduced by using full angular spatial compounding. Using the data from a breast tomography scanner, the results demonstrated that the SLD technique with full angular spatial compounding could generate an accurate attenuation map with low bias and variance. As a result, SLD with full spatial angular compounding can provide improved attenuation images for breast diagnostics.

In ultrasonic imaging, reduction of lateral sidelobes can result in an improved image with less distortion and fewer artifacts. In general, apodization is used to lower sidelobes in exchange for increasing the width of the main lobe, and thus decreasing lateral resolution. Null subtraction imaging (NSI) is a nonlinear image processing technique that uses different receive apodizations on copies of the same RF data to maintain low sidelobe levels while simultaneously improving lateral resolution. The images created with three different apodization functions are combined to form an image with low sidelobe levels and apparent improvements in lateral resolution compared to conventional rectangular apodization. To evaluate the performance of this technique for different imaging tasks, experiments were performed on an ATS539 phantom containing wire targets to assess lateral resolution and cylindrical anechoic and hyperechoic targets to assess contrast. NSI images were compared against rectangular apodized images and minimum variance beamformed images. In experiments, the apparent lateral resolution was observed to improve by a factor of more than $35\times $ when compared to rectangular apodization. Image quality was assessed by the estimation of lateral resolution (−6-dB receive beamwidth), main-lobe-to-sidelobe ratio, and contrast-to-noise ratio (CNR). Imaging with NSI using a focal number of 2 (f/2), the −6-dB beamwidth on receive as measured from a small wire target in the ATS phantom was $0.03\lambda $ compared to $2.79\lambda $ for rectangular apodization. Sidelobes were observed to decrease by 32.9 dB with NSI compared to rectangular apodization. However, the ability to observe the contrast of anechoic and hyperechoic targets reduced when utilizing the NSI scheme, i.e., the CNR decreased from −3.05 to −1.01 for anechoic targets and 1.65 to 0.45 for the hyperechoic targets.

In an increasing number of applications of focused ultrasound (FUS) therapy, such as opening of the blood–brain barrier or collapsing microbubbles in a tumor, elevation of tissue temperature is not involved. In these cases, real-time visualization of the field distribution of the FUS source would allow localization of the FUS beam within the targeted tissue and allow repositioning of the FUS beam during tissue motion. In this paper, in order to visualize the FUS beam in situ , a 6-MHz single-element transducer ( $f$ /2) was used as the FUS source and aligned perpendicular to a linear array which passively received scattered ultrasound from the sample. An image of the reconstructed intensity field pattern of the FUS source using bistatic beamforming was then superimposed on a registered B-mode image of the sample acquired using the same linear array. The superimposed image is used to provide anatomical context of the FUS beam in the sample being treated. The intensity field pattern reconstructed from a homogeneous scattering phantom was compared with the field characteristics of the FUS source characterized by the wire technique. The beamwidth estimates at the FUS focus using the in situ reconstruction technique and the wire technique were 1.5 and 1.2 mm, respectively. The depth-of-field estimates for the in situ reconstruction technique and the wire technique were 11.8 and 16.8 mm, respectively. The FUS beams were also visualized in a two-layer phantom and a chicken breast. The novel reconstruction technique was able to accurately visualize the field of an FUS source in the context of the interrogated medium.

The emergence of in-body medical devices has provided a means of capturing physiological or diagnostic information and streaming this information outside of the body. Currently, electromagnetic-based communications make up the bulk of in-body medical device communication protocols. Traditional electromagnetic-based solutions are limited in their data rates and available power. Recently, ultrasound was investigated as a communication channel for through-tissue data transmission. To achieve real-time video streaming through tissue, data rates of ultrasound need to exceed 1 Mbps. In a previous study, we demonstrated ultrasound communications with data rates greater than 30 Mbps with two focused ultrasound transducers using a large footprint laboratory system through slabs of lossy tissues. While the form factor of the transmitter is also crucial, it is obvious that a large, focused transducer cannot fit within the size of a small in-body medical device. Several other challenges for achieving high-speed ultrasonic communication through tissue include strong reflections leading to multipath effects and attenuation. In this work, we demonstrate ultrasonic video communications using a mm-scale microcrystal transmitter with video streaming supplied by a camera connected to a Field Programmable Gate Array (FPGA). The signals were transmitted through a tissue-mimicking phantom and through the abdomen of a rabbit in vivo . The ultrasound signal was recorded by an array probe connected to a Verasonics Vantage system and decoded back to video. To improve the received signal quality, we combined the signal from multiple channels of the array probe. Orthogonal frequency division multiplexing (OFDM) modulation was used to reduce the receiver complexity under a strong multipath environment.

In this work, a system is presented for combined therapy planning, low intensity FUS treatment, and real-time therapy monitoring using a single diagnostic imaging array. First, a sonication pattern was determined by manually segmenting the treatment region from a B-mode image captured with the imaging array. To visualize the FUS therapy beam, a focused pulse excitation was transmitted and backscattered signals were used to reconstruct the intensity field of the FUS beam. The FUS beam reconstruction was overlaid onto a co-aligned B-mode image captured with the imaging array, allowing one to qualitatively monitor the position and size of the FUS beam with anatomical context from the B-mode image. Real-time beam visualizations at a frame rate of 25-30 frames per second were achieved in a rat tumor in vivo and a mock FUS therapy was planned and monitored in a tissue-mimicking.

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC requires knowledge of the attenuation coefficient slope (ACS) of tissues in the beam path between the transducer and the insonified region of interest, namely, the total attenuation. In this study, the total attenuation is calculated as the cumulative sum of values of a local attenuation map devised using full angular spatial compounding (FASC). The BSC was parameterized through the integrated backscatter coefficient (iBSC) obtaining iBSC maps. Experimental validation of the proposed approach consisted of scanning two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background. Additional iBSC maps were computed assuming an uniform ACS map of 0.5 dB/cm/MHz (which is a value assumed for soft tissues) instead of the FASC-ACS map. Finally a iBSC map was obtained using an ideal ACS map formed with ground truth ACS values and knowledge of inclusion true position. The results were comparable when using the FASC-ACS map or the ideal ACS map in term of inclusion detectability and estimation accuracy. The use of the uniform ACS map resulted in some cases with very high fractional error (>;9 dB), which highlights the relevance of accurate compensation for total attenuation. These results suggest that BSCs can be reliably estimated using total attenuation compensation from FASC-ACS maps.

The backscatter coefficient (BSC) quantifies the frequency-dependent reflectivity of tissues. Accurate estimation of the BSC is only possible with the knowledge of the attenuation coefficient slope (ACS) of the tissues under examination. In this study, the use of attenuation maps constructed using full angular spatial compounding (FASC) is proposed for attenuation compensation when imaging integrated BSCs. Experimental validation of the proposed approach was obtained using two cylindrical physical phantoms with off-centered inclusions having different ACS and BSC values than the background, and in a phantom containing an ex vivo chicken breast sample embedded in an agar matrix. With the phantom data, three different ACS maps were employed for attenuation compensation: (1) a ground truth ACS map constructed using insertion loss techniques, (2) the estimated ACS map using FASC attenuation imaging, and (3) a uniform ACS map with a value of 0.5 dBcm\protect \relax \special {t4ht=−}1MHz\protect \relax \special {t4ht=−}1, which is commonly used to represent attenuation in soft tissues. Comparable results were obtained when using the ground truth and FASC-estimated ACS maps in term of inclusion detectability and estimation accuracy, with averaged fractional error below 2.8 dB in both phantoms. Conversely, the use of the homogeneous ACS map resulted in higher levels of fractional error ( > 10 dB), which demonstrates the importance of an accurate attenuation compensation. The results with the ex vivo tissue sample were consistent with the observations using the physical phantoms, with the FASC-derived ACS map providing comparable BSC images to those formed using the ground truth ACS map and more accurate than those BSC images formed using a uniform ACS. These results suggest that BSCs can be reliably estimated using FASC when a self-consistent attenuation compensation stemming from prior estimation of an accurate ACS map is used.

Radiological clips are commonly used to track and identify lesions in breast cancer patients receiving neoadjuvant chemotherapy. Available radiological clips often suffer degradation of visibility in ultrasound during the course of treatment, thus requiring more involved localization procedures to identify regions prior to resection. Furthermore, upon localization, additional markers are placed to aid surgeons during resection. We describe a biocompatible electronic device which transmits a unique identification signal when imaged with ultrasound pulse inversion, i.e ., ultrasound identification (USID). The USID clip provides better localization and visualization of the clip compared to passive clips. Furthermore, the ultrasound systems can be programmed to produce an audible indicator of a specific clip’s presence and proximity to the probe, thereby eliminating the need for insertion of additional markers. This concept is tested using a microcontroller and a direct digital synthesis board connected to two sonomicrocrystals, a Verasonics research system, and tissue mimicking phantoms. Clips with six different USID tags were successfully visualized at 10 dB above the background signal. Localization and identification are demonstrated using pulse inversion and image processing techniques.

Ultrasonic signals have been used in biomedical applications since at least the 1940s. Underwater acoustic signals have been used in the subsea industry for ranging since the early 1900s and for SONAR imaging and communication in the decades that followed. Since the development of subsea communication systems in the 1940s, increasingly sophisticated means have be developed for transmitting information acoustically through the highly variable ocean environment. State-of-the-art acoustic communication systems leverage ultrasonic transducers to achieve data transmission rates that mirror radio-frequency (RF)-based WiFi systems on land. The increasing use of wireless implanted medical devices (IMDs) have increased interest in through-tissue communications. Restrictions on the available bandwidth and transmit signal power limit the data rates of RF-based IMDs. In this talk, we give an overview of video-capable ultrasonic wireless communication between IMDs and external devices. We emphasize the potential of such a system for improving video capsule endoscopy and describe the challenges of through-tissue acoustic communication. Building on experience with subsea-communication systems, we discuss methods to cope with attenuation, dispersion, reflection, and nonlinear propagation. Some of our recent experiments performed ex vivo with soft tissue samples and in vivo with anesthetized animal subjects will be discussed.

In this work, we propose a novel implementation of both a transmitter and receiver with field programmable gate arrays (FPGAs) to achieve real-time continuous high-definition (HD) video transmission through tissue, which can enable HD and higher frame rate wireless capsule endoscopy. We used a Texas Instruments AFE58JD48EVM 16 channel analog front end (AFE) evaluation board as the receiver connected to a Xilinx ZCU106 Zynq Ultrascale MPSoC development board in which we implemented a digital down converter (DDC), OFDM demodulator, maximum ratio combiner and low-density parity-check (LDPC) decoder. For the transmitter, we used an Analog Devices EVAL-AD9166 vector signal generator evaluation board, which has a built-in 4.3 dBm output power amplifier as a transmitter connected to another ZCU106 development board in which we implemented a LDPC encoder, OFDM modulator and digital up converter (DUC). The modulated signal was transmitted through a tissue-mimicking abdominal phantom using a 2-mm microcrystal transducer and received with a Sonic Concepts IP103 64 channel phased array at a center frequency of 3.2 MHz. We achieved the continuous transmission of up to over[OML1] 6 Mbps error free payload data rate after LDPC decoder which is used to carry HD video streams through ultrasound.

In this work, we propose a novel anodization scheme where three different apodizations are introduced to create three separate images that are then combined nonlinearly to produce better image quality, i.e., null subtraction imaging (NSI). All apodizations were on receive only. The first apodization consisted of a zero mean weighting with the left half of aperture a weight of + 1 and the right half of aperture a weight of −1. The second apodization was a DC offset version of the first apodization and the third apodization was a flipped version of the second. Images were created by combining the envelope signals of three apodizations with different weights. The images were subtracted from one another resulting in a reduction in sidelobe levels, a narrowing of the main lobe and improvement in image quality. An L14-5 array transducer and a Verasonics system were used to capture the RF channel data using seven plane waves spanning angles between −12° and 12°. Both coherent compounding and filtered incoherent compounding were used as a tradeoff between lateral resolution and contrast to noise ratio (CNR). Images were constructed from wire targets, a tissue-mimicking phantom, and rat tumors in vivo. Image quality was assessed through the CNR and intensity of sidelobes visible in the images. Images created using NSI were compared to images created using Hanning apodization with delay and sum (DAS) and a generalized coherence factor (GCF) approach. Improvements in image quality were found when using NSI compared to the other methods.