Injectable Mechanophore Nanoparticles for Deep-Tissue Mechanochemical Dynamic Therapy.

Photodynamic therapy (PDT) and sonodynamic therapy (SDT), using nonionizing light and ultrasound to generate reactive oxygen species, offer promising localized treatments for cancers. However, the effectiveness of PDT is hampered by inadequate tissue penetration, and SDT largely relies on pyrolysis and sonoluminescence, which may cause tissue injury and imprecise targeting. To address these issues, we have proposed a mechanochemical dynamic therapy (MDT) that uses free radicals generated from mechanophore-embedded polymers under mechanical stress to produce reactive oxygen species for cancer treatment. Yet, their application in vivo is constrained by the bulk form of the polymer and the need for high ultrasound intensities for activation. In this study, we developed injectable, nanoscale mechanophore particles with enhanced ultrasound sensitivity by leveraging a core-shell structure comprising silica nanoparticles (NPs) whose interfaces are linked to polymer brushes by an azo mechanophore moiety. Upon focused ultrasound (FUS) treatment, this injectable NP generates reactive oxygen species (ROS), demonstrating promising results in both an in vitro 4T1 cell model and an in vivo mouse model of orthotopic breast cancers. This research offers an alternative therapy technique, integrating force-responsive azo mechanophores and FUS under biocompatible conditions.

Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound.

A transfer function approach was recently demonstrated to mitigate data mismatches at the acquisition level for a single ultrasound scanner in deep learning (DL)-based quantitative ultrasound (QUS). As a natural progression, we further investigate the transfer function approach and introduce a machine-to-machine (M2M) transfer function, which possesses the ability to mitigate data mismatches at a machine level. This ability opens the door to unprecedented opportunities for reducing DL model development costs, enabling the combination of data from multiple sources or scanners, or facilitating the transfer of DL models between machines. We tested the proposed method utilizing a SonixOne machine and a Verasonics machine with an L9-4 array and an L11-5 array. We conducted two types of acquisitions to obtain calibration data: stable and free-hand, using two different calibration phantoms. Without the proposed method, the mean classification accuracy when applying a model on data acquired from one system to data acquired from another system was 50%, and the mean average area under the receiver operator characteristic (ROC) curve (AUC) was 0.405. With the proposed method, mean accuracy increased to 99%, and the AUC rose to the 0.999. Additional observations include the choice of the calibration phantom led to statistically significant changes in the performance of the proposed method. Moreover, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view. Lastly, the proposed method proved effective when a different transducer was used in the test machine.

A Radiological Clip Design Using Ultrasound Identification to Improve Localization.

OBJECTIVE: We demonstrate the use of ultrasound to receive an acoustic signal transmitted from a radiological clip designed from a custom circuit. This signal encodes an identification number and is localized and identified wirelessly by the ultrasound imaging system. METHODS: We designed and constructed the test platform with a Teensy 4.0 microcontroller core to detect ultrasonic imaging pulses received by a transducer embedded in a phantom, which acted as the radiological clip. Ultrasound identification (USID) signals were generated and transmitted as a result. The phantom and clip were imaged using an ultrasonic array (Philips L7-4) connected to a Verasonics™ Vantage 128 system operating in pulse inversion (PI) mode. Cross-correlations were performed to localize and identify the code sequences in the PI images. RESULTS: USID signals were detected and visualized on B-mode images of the phantoms with up to sub-millimeter localization accuracy. The average detection rate across 30,400 frames of ultrasound data was 98.1%. CONCLUSION: The USID clip produced identifiable, distinguishable, and localizable signals when imaged. SIGNIFICANCE: Radiological clips are used to mark breast cancer being treated by neoadjuvant chemotherapy (NAC) via implant in or near treated lesions. As NAC progresses, available marking clips can lose visibility in ultrasound, the imaging modality of choice for monitoring NAC-treated lesions. By transmitting an active signal, more accurate and reliable ultrasound localization of these clips could be achieved and multiple clips with different ID values could be imaged in the same field of view.

High-Resolution Power Doppler Using Null Subtraction Imaging.

To improve the spatial resolution of power Doppler (PD) imaging, we explored null subtraction imaging (NSI) as an alternative beamforming technique to delay-and-sum (DAS). NSI is a nonlinear beamforming approach that uses three different apodizations on receive and incoherently sums the beamformed envelopes. NSI uses a null in the beam pattern to improve the lateral resolution, which we apply here for improving PD spatial resolution both with and without contrast microbubbles. In this study, we used NSI with three types of singular value decomposition (SVD)-based clutter filters and noise equalization to generate high-resolution PD images. An element sensitivity correction scheme was also proposed as a crucial component of NSI-based PD imaging. First, a microbubble trace experiment was performed to evaluate the resolution improvement of NSI-based PD over traditional DAS-based PD. Then, both contrast-enhanced and contrast free ultrasound PD images were generated from the scan of a rat brain. The cross-sectional profile of the microbubble traces and microvessels were plotted. FWHM was also estimated to provide a quantitative metric. Furthermore, iso-frequency curves were calculated to provide a resolution evaluation metric over the global field of view. Up to six-fold resolution improvement was demonstrated by the FWHM estimate and four-fold resolution improvement was demonstrated by the iso-frequency curve from the NSI-based PD microvessel images compared to microvessel images generated by traditional DAS-based beamforming. A resolvability of [Formula: see text] was measured from the NSI-based PD microvessel image. The computational cost of NSI-based PD was only increased by 40 percent over the DAS-based PD.

Grating lobe mitigation on large-pitch arrays using null subtraction imaging.

Null Subtraction Imaging (NSI) is a novel beamforming technique that can produce B-mode images resulting in high spatial resolution and low computational cost compared to other beamforming techniques. Previous work has demonstrated that in addition to a beam pattern with a narrow main lobe and low side lobes, NSI can also reduce or mitigate grating lobes, which can appear when the array pitch is larger than one half the wavelength of the transmitted pulse. These grating lobes can result in imaging artifacts that produce clutter and lower contrast. By lowering grating lobe levels, a larger pitch array could be used, which could allow arrays with a larger field of view while maintaining a standard element count. This could have important benefits for specific applications such as ultrasonic abdominal imaging. Experiments were conducted to examine the feasibility of using NSI with large pitch, wide field-of-view arrays. Grating lobe reduction was measured against array pitch, DC offset, and f-number. Experiments were conducted on wire targets and contrast targets in a phantom and results were further verified in vivo by imaging the liver of a rabbit. The results demonstrated that NSI was able to reduce grating lobe brightness by up to 45 dB compared to delay-and-sum (DAS) beamforming when using planewave transmissions, reduce the generalized contrast-to-noise ratio (gCNR) of grating lobe regions from 0.60 to 0.08, and maintain a similar speckle quality to DAS. The gCNR of anechoic regions also improves, increasing from 0.09 to 0.15 on an array with a pitch of 5 wavelengths. Due to significant grating lobe level reduction, NSI shows potential to improve image quality over DAS on a large pitch, wide field-of-view array.

In Vivo Validation of an In Situ Calibration Bead as a Reference for Backscatter Coefficient Calculation.

OBJECTIVE: The study described here was aimed at assessing the capability of quantitative ultrasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantification of fatty liver disease. We evaluated the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation. METHODS: Traditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmission losses through tissues. To address this issue, we used an in situ Ti bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N = 21). Specifically, mammary tumors were grown in rabbits and when a tumor reached ≥1 cm in size, a 2 mm Ti bead was implanted in the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with an L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenuation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead. RESULTS: For ESD estimation, the 95% confidence interval between measurements with and without the pork belly layer was 6.0, 27.4 using the in situ bead and 114, 135.1 with the external reference phantom. For EAC estimation, the 95% confidence intervals were -8.1, 0.5 for the bead and -41.5, -32.2 for the phantom. These results indicate that the in situ bead method has reduced bias in QUS estimates because of intervening tissue losses. CONCLUSION: The use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings.

In vivo validation of an in situ calibration bead as a reference for backscatter coefficient calculation.

Objectives: The study aims to assess the capability of Quantitative Ultrasound (QUS) based on the backscatter coefficient (BSC) for classifying disease states, such as breast cancer response to neoadjuvant chemotherapy and quantifying fatty liver disease. We evaluate the effectiveness of an in situ titanium (Ti) bead as a reference target in calibrating the system and mitigating attenuation and transmission loss effects on BSC estimation. Methods: Traditional BSC estimation methods require external references for calibration, which do not account for ultrasound attenuation or transmission losses through tissues. To address this issue, we use an in situ titanium (Ti) bead as a reference target, because it can be used to calibrate the system and mitigate the attenuation and transmission loss effects on estimation of the BSC. The capabilities of the in situ calibration approach were assessed by quantifying consistency of BSC estimates from rabbit mammary tumors (N=21). Specifically, mammary tumors were grown in rabbits and when a tumor reached 1 cm or greater in size, a 2-mm Ti bead was implanted into the tumor as a radiological marker and a calibration source for ultrasound. Three days later, the tumors were scanned with a L-14/5 38 array transducer connected to a SonixOne scanner with and without a slab of pork belly placed on top of the tumors. The pork belly acted as an additional source of attenuation and transmission loss. QUS parameters, specifically effective scatterer diameter (ESD) and effective acoustic concentration (EAC), were calculated using calibration spectra from both an external reference phantom and the Ti bead. Results: For ESD estimation, the 95% confidence interval between measurements with and without the pork belly layer was (6.0,27.4) using the in situ bead and (114, 135.1) with the external reference phantom. For EAC estimation, the 95% confidence interval were (-8.1, 0.5) for the bead and (-41.5, -32.2) for the phantom. These results indicate that the in situ bead method shows reduced bias in QUS estimates due to intervening tissue losses. Conclusions: The use of an in situ Ti bead as a radiological marker not only serves its traditional role but also effectively acts as a calibration target for QUS methods. This approach accounts for attenuation and transmission losses in tissue, resulting in more accurate QUS estimates and offering a promising method for enhanced disease state classification in clinical settings.

Spectral-based Quantitative Ultrasound Imaging Processing Techniques: Comparisons of RF Versus IQ Approaches.

Quantitative ultrasound (QUS) is an imaging technique which includes spectral-based parameterization. Typical spectral-based parameters include the backscatter coefficient (BSC) and attenuation coefficient slope (ACS). Traditionally, spectral-based QUS relies on the radio frequency (RF) signal to calculate the spectral-based parameters. Many clinical and research scanners only provide the in-phase and quadrature (IQ) signal. To acquire the RF data, the common approach is to convert IQ signal back into RF signal via mixing with a carrier frequency. In this study, we hypothesize that the performance, that is, accuracy and precision, of spectral-based parameters calculated directly from IQ data is as good as or better than using converted RF data. To test this hypothesis, estimation of the BSC and ACS using RF and IQ data from software, physical phantoms and in vivo rabbit data were analyzed and compared. The results indicated that there were only small differences in estimates of the BSC between when using the original RF, the IQ derived from the original RF and the RF reconverted from the IQ, that is, root mean square errors (RMSEs) were less than 0.04. Furthermore, the structural similarity index measure (SSIM) was calculated for ACS maps with a value greater than 0.96 for maps created using the original RF, IQ data and reconverted RF. On the other hand, the processing time using the IQ data compared to RF data were substantially less, that is, reduced by more than a factor of two. Therefore, this study confirms two things: (1) there is no need to convert IQ data back to RF data for conducting spectral-based QUS analysis, because the conversion from IQ back into RF data can introduce artifacts. (2) For the implementation of real-time QUS, there is an advantage to convert the original RF data into IQ data to conduct spectral-based QUS analysis because IQ data-based QUS can improve processing speed.

A priori prediction of breast cancer response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivative and molecular subtype.

The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort of patients (n = 208) with locally advanced and earlier-stage breast cancer and combined them to best determine tumour responses with machine learning approach. Two multi-features response prediction algorithms using a k-nearest neighbour and support vector machine were developed with leave-one-out and hold-out cross-validation methods to evaluate the performance of the response prediction models. In a leave-one-out approach, the quantitative ultrasound-texture analysis based model attained good classification performance with 80% of accuracy and AUC of 0.83. Including molecular subtype in the model improved the performance to 83% of accuracy and 0.87 of AUC. Due to limited number of samples in the training process, a model developed with a hold-out approach exhibited a slightly higher bias error in classification performance. The most relevant features selected in predicting the response groups are core-to-margin, texture-derivative, and molecular subtype. These results imply that that baseline tumour-margin, texture derivative analysis methods combined with molecular subtype can potentially be used for the prediction of ultimate treatment response in patients prior to neoadjuvant chemotherapy.

Quantitative Ultrasound: Scattering Theory.

The radio-frequency ultrasound backscattered data from tissue is rich in information and can provide important information about tissue state that is not obtained through traditional B-mode imaging. To parameterize the ultrasound backscattered data, the frequency spectrum, i.e., the backscatter coefficient, can be modeled using scattering theory. Models of tissue scattering are often represented by simple discrete geometric shapes, i.e., discrete scattering model. The discrete scattering model provides important insights into how the spatial arrangement of scatterers contributes to the signal spectrum. Another competing model is the continuum scattering model. In this model, the tissue is described as a continuous tissue construct with scatterers that have a continuous impedance change from the background. The continuous model provides a form factor description of the underlying tissue scatterers such as an effective scatterer diameter. In this chapter, we will compare and contrast the two underlying tissue scattering models and how they provide insights into ultrasonic scattering from soft tissues.

Quantitative Ultrasound: Experimental Implementation.

The backscatter coefficient is a fundamental property of tissues, much like the attenuation and sound speed. From the backscatter coefficient, different scatterer properties describing the underlying tissue can be used to characterize tissue state. Furthermore, because the backscatter coefficient is a fundamental property of a tissue, estimation of the backscatter coefficient should be able to be computed with system and operator independence. To accomplish system- and operator-independent estimates of the backscatter coefficient, a calibration spectrum must be obtained at the same system settings as the settings used to scan a tissue. In this chapter, we discuss three approaches to obtaining a calibration spectrum and compare the engineering tradeoffs associated with each approach. In addition, methods for reducing deterministic noise in the backscatter coefficient spectrum are considered and implementation of these techniques is discussed.

Detection of microcalcifications using nonlinear beamforming techniques.

OBJECTIVE: Abundant research demonstrates that early detection of cancer leads to improved patient prognoses. By detecting cancer earlier, when tumors are in their primary stages, treatment can be applied before metastases have occurred. The presence of microcalcifications (MCs) is indicative of malignancy in the breast, i.e., 30-50% of all nonpalpable breast cancers detected using mammograms are based on identifying the presence of MCs. Therefore, improving the ability to detect MCs with modern imaging technology remains an important goal. Specifically, improving the sensitivity of ultrasound imaging techniques to detect MCs in the breast will provide an important role for the early detection and diagnosis of breast cancer. METHODS: In this work, a novel nonlinear beamforming technology for ultrasonic arrays is investigated for its ability to detect MCs. The beamforming technique, called null subtraction imaging (NSI), utilizes nulls in the beam pattern to create images using ultrasound. NSI provides improved lateral resolution, a reduction in side lobes, and an accentuation of bright singular targets. Therefore, it was hypothesized that the use of NSI would result in identification of more MCs in rat tumors having a speckle background. To test this hypothesis, rats with tumors were injected with Hydroxyapatite (HA) particles to mimic MCs. Ultrasound was used to scan the rat tumors and images were constructed using conventional delay and sum and using NSI beamforming. Three readers with experience in diagnostic ultrasound imaging examined the 1,344 images and scored the presence or absence of MCs. DISCUSSION: In all, 336 different tumor image slices were recorded and each reconstructed using NSI or conventional delay and sum with Hann apodization. In every image where one or MCs were detected in the Hann reconstructions, MCs were detected in the NSI images. In nine rat tumor images, one or more MCs were detected in the NSI images but not in the Hann images. CONCLUSIONS: Statistically, the results did support the hypothesis that NSI would increase the number of MCs detected in the rat tumors.

High-Level Synthesis Design of Scalable Ultrafast Ultrasound Beamformer With Single FPGA.

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. Current Xilinx FPGAs provide the capabilities of connecting up to 1024 ultrasound channels with a single FPGA and the newest JESD204B interface analog front end (AFE). This channel count is much more than the channel count needed by current linear arrays, which normally have 128 or 256 channels. 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. Multiple imaging schemes including B-mode, power Doppler and ULM were assessed to verify that the image quality was not compromised for speed.

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.

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 acquisition of 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 DL training strategy for classifying tissues based on the ultrasonic backscattered RF data, i.e., quantitative ultrasound (QUS), which we named zone training. In 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 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 zone training can require a factor of 2-3 less training data in low data regime to achieve similar classification accuracies compared to a conventional training strategy.

Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions.

Deep learning (DL) can fail when there are data mismatches between training and testing data distributions. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imaging. As a result, it is crucial to mitigate the effects of these mismatches to enable wider clinical adoption of DL-powered ultrasound imaging and tissue characterization. To address this challenge, we propose an inexpensive and generalizable method that involves collecting 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 using an L9-4 array connected to a SonixOne scanner. To investigate generalizability of the proposed solution, we calibrated three types of data mismatches: pulse frequency mismatch, focus mismatch, and output power mismatch. Two well-known convolutional neural networks (CNNs), i.e., ResNet-50 and DenseNet-201, were trained using the ultrasound radio frequency (RF) data. To calibrate the setting mismatches, we calculated the setting transfer functions. The CNNs trained without calibration resulted in mean classification accuracies of around 52%, 84%, and 85% 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 the mean accuracies of 96%, 96%, and 98%, 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.

Effects of acoustic nonlinearity on communication performance in soft tissues.

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.

Grating Lobe Reduction in Plane-Wave Imaging With Angular Compounding Using Subtraction of Coherent Signals.

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.

Ultrasonic backscatter coefficient estimation in nonlinear regime using an in situ calibration target.

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.

Identifying and overcoming limitations with in situ calibration beads for quantitative ultrasound.

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.

Noninvasive and Spatiotemporal Control of DNAzyme-Based Imaging of Metal Ions In Vivo Using High-Intensity Focused Ultrasound.

Detecting metal ions in vivo with a high spatiotemporal resolution is critical to understanding the roles of the metal ions in both healthy and disease states. Although spatiotemporal controls of metal-ion sensors using light have been demonstrated, the lack of penetration depth in tissue and in vivo has limited their application. To overcome this limitation, we herein report the use of high-intensity focused ultrasound (HIFU) to remotely deliver on-demand, spatiotemporally resolved thermal energy to activate the DNAzyme sensors at the targeted region both in vitro and in vivo. A Zn2+-selective DNAzyme probe is inactivated by a protector strand to block the formation of catalytic enzyme structure, which can then be activated by an HIFU-induced increase in the local temperature. With this design, Zn2+-specific fluorescent resonance energy transfer (FRET) imaging has been demonstrated by the new DNAzyme-HIFU probes in both HeLa cells and mice. The current method can be applied to monitor many other metal ions for in vivo imaging and medical diagnosis using metal-specific DNAzymes that have either been obtained or can be selected using in vitro selection.