Radiofrequency Images Research Articles

Can carbonation depth be measured in a nondestructive way? High-frequency quantitative ultrasound imaging for cement paste

Determining the carbonation depth is of paramount importance in assessing the durability of cementitious materials as the carbonation process alters their near-surface physicochemical properties. This paper introduces a novel quantitative ultrasound (QUS) technique capable of nondestructively identifying the carbonation depth, regardless of the tortuosity of the carbonation front. A 2.5 MHz phased array transducer was employed to construct beamformed radio frequency images and measure two microstructure-dependent parameters – spectral slope (SS) and spectral intercept (SI) – based on backscatter and attenuation coefficients. The change in obtained SS and SI were then visualized as QUS images, which displayed the geometry of the carbonation front, correlating with images constructed using a phenolphthalein solution (1.6 mm difference in depth). Mineral compositions and porosities were analyzed to account for microstructural changes in each layer. This study opens an opportunity for nondestructive detection of carbonation depth, overcoming the limitations in conventional methods or regression-based approaches.

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound.

Ultrafast ultrasound has recently emerged as an alternative to traditional focused ultrasound. By virtue of the low number of insonifications it requires, ultrafast ultrasound enables the imaging of the human body at potentially very high frame rates. However, unaccounted for speed-of-sound variations in the insonified medium often result in phase aberrations in the reconstructed images. The diagnosis capability of ultrafast ultrasound is thus ultimately impeded. Therefore, there is a strong need for adaptive beamforming methods that are resilient to speed-of-sound aberrations. Several of such techniques have been proposed recently but they often lack parallelizability or the ability to directly correct both transmit and receive phase aberrations. In this article, we introduce an adaptive beamforming method designed to address these shortcomings. To do so, we compute the windowed Radon transform of several complex radio-frequency images reconstructed using delay-and-sum. Then, we apply to the obtained local sinograms weighted tensor rank-1 decompositions and their results are eventually used to reconstruct a corrected image. We demonstrate using simulated and in-vitro data that our method is able to successfully recover aberration-free images and that it outperforms both coherent compounding and the recently introduced SVD beamformer. Finally, we validate the proposed beamforming technique on in-vivo data, resulting in a significant improvement of image quality compared to the two reference methods.

RCVNet: A bird damage identification network for power towers based on fusion of RF images and visual images

The technology for identifying birds around power towers using cameras alone is still susceptible to environmental interference. This paper proposes a new bird damage recognition network, RCVNet, which addresses this issue by fusing radio-frequency (RF) images and visual images. The network employs a feature layer fusion approach that accurately identifies bird damages in the monitoring area. Initially, RCVNet takes a group of RF and visual images as input. Then, through a series of convolutional neural networks (CNNs), birds are identified and located. To overcome challenges in recognizing small targets, several improved modules such as cross-supervised fusion network (CSF-net), posture deformable convolution (PDF), small-target attention fusion mechanism (SAFM), and Tiny-YOLOHead are introduced throughout RCVNet, improving surface information utilization and small feature retention rates. Finally, a bird damage discrimination strategy is developed based on the recognition outcomes of birds. As there is currently no public dataset available for RCVNet training, a new bird dataset called CRB2022, which includes RF and visual images, was gathered. Through large-scale experiments utilizing these methods, RCVNet effectively identifies birds, achieving a mean average precision of 79.34% and a mean average recall of 83.29%. Additionally, the discrimination rate of the utilized strategy can reach up to 98%.

Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images

Quantitative ultrasound is of significant relevance to the clinical diagnosis of benign and malignant breast lesions. Owing to desmoplastic reactions in the tumor and malignant cells invading the surrounding tissues, the irregular boundary of the lesion is a significant indication of malignancy. Complexity measured using entropy based on ultrasound radio frequency (RF) data is investigated to characterize the disease; however, the performance is reduced due to the slowly varying components in the data. In the present study, adoption of small-window entropy (SWE) based on adaptively decomposed ultrasound RF images is proposed for the characterization of breast lesion tissues. First, down-sampled, dilated and cropped RF (DDC_RF) images are obtained from the original ultrasonic RF data. Subsequently, a fast multivariate empirical mode decomposition algorithm is employed to adaptively decompose the DDC_RF images into intrinsic mode functions (IMFs). The ring regions of interest that are the areas surrounding the tumors are determined from all individual IMF images and their different combinations to calculate the SWEs. The assessment is performed using the Open Access Series of Breast Ultrasonic Data (OASBUD), which includes original RF data and histological assessment results of 48 benign and 48 malignant lesions. The receiver operating curve (ROC) and the area under the ROC (AUC) values are calculated to assess the ability to diagnose breast lesions. The results show that the SWEs based on the combinations of IMFs 1–3, IMFs 1,2, IMFs 1–4, and IMFs 1,3 provided the AUC values of 0.88, 0.87, 0.86, and 0.85, respectively, which are greater than 0.84, obtained from the undecomposed RF images. It is concluded that the SWE based on the adaptively decomposed ultrasound RF images is an improved characteristic for distinguishing between benign and malignant breast lesions, and it is suggested that it may serve as a basis for computer-assisted breast cancer ultrasound diagnosis.

Complex Convolutional Neural Networks for Ultrafast Ultrasound Imaging Reconstruction From In-Phase/Quadrature Signal.

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultrahigh frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast ultrasound imaging. Most of these approaches have been performed on radio frequency (RF) signals. However, in- phase/quadrature (I/Q) digital beamformers are now widely used as low-cost strategies. In this work, we used complex convolutional neural networks for reconstruction of ultrasound images from I/Q signals. We recently described a convolutional neural network architecture called ID-Net, which exploited an inception layer designed for reconstruction of RF diverging-wave ultrasound images. In the present study, we derive the complex equivalent of this network, i.e., complex-valued inception for diverging-wave network (CID-Net) that operates on I/Q data. We provide experimental evidence that CID-Net provides the same image quality as that obtained from RF-trained convolutional neural networks, i.e., using only three I/Q images, CID-Net produces high-quality images that can compete with those obtained by coherently compounding 31 RF images. Moreover, we show that CID-Net outperforms the straightforward architecture that consists of processing real and imaginary parts of the I/Q signal separately, which thereby indicates the importance of consistently processing the I/Q signals using a network that exploits the complex nature of such signals.

Ultrasound strain mapping of the mouse Achilles tendon during passive dorsiflexion

Immediately prior to inserting into bone, many healthy tendons experience impingement from nearby bony structures. However, super-physiological levels of impingement are implicated in insertional tendinopathies. Unfortunately, the mechanisms underlying the connection between impingement and tendon pathology remain poorly understood, in part due to the shortage of well-characterized animal models of impingement at clinically relevant sites. As a first step towards developing a model of excessive tendon impingement, the objective of this study was to characterize the mechanical strain environment in the mouse Achilles tendon insertion under passive dorsiflexion and confirm that – like humans – mice experience impingement of the tendon insertion from the calcaneus (heel bone) in dorsiflexed ankle positions. Based on previous work in humans, we hypothesized that during dorsiflexion, the mouse Achilles tendon insertion would experience high levels of transverse compressive strain due to calcaneal impingement. A custom-built loading platform was used to apply passive dorsiflexion, while an ultrasound transducer positioned over the Achilles tendon captured radiofrequency images. A non-rigid image registration algorithm was then used to map the transverse compressive strain based on the acquired ultrasound image sequences. Our results demonstrate that during passive dorsiflexion, transverse compressive strains were produced throughout the Achilles tendon, with significantly larger strain magnitudes at the tendon insertion than at the midsubstance. Furthermore, there was increasing transverse compressive strain observed within the Achilles tendon as a function of increasing dorsiflexion angle. This study enhances our understanding of the unique mechanical loading environment of the Achilles tendon under physiologically relevant conditions.

RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization

Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used sensors, radar has usually been considered as a robust and cost-effective solution even in adverse driving scenarios, e.g., weak/strong lighting or bad weather. Instead of considering to fuse the unreliable information from all available sensors, perception from pure radar data becomes a valuable alternative that is worth exploring. In this paper, we propose a deep radar object detection network, named RODNet, which is cross-supervised by a camera-radar fused algorithm without laborious annotation efforts, to effectively detect objects from the radio frequency (RF) images in real-time. First, the raw signals captured by millimeter-wave radars are transformed to RF images in range-azimuth coordinates. Second, our proposed RODNet takes a sequence of RF images as the input to predict the likelihood of objects in the radar field of view (FoV). Two customized modules are also added to handle multi-chirp information and object relative motion. Instead of using human-labeled ground truth for training, the proposed RODNet is cross-supervised by a novel 3D localization of detected objects using a camera-radar fusion (CRF) strategy in the training stage. Finally, we propose a method to evaluate the object detection performance of the RODNet. Due to no existing public dataset available for our task, we create a new dataset, named CRUW, which contains synchronized RGB and RF image sequences in various driving scenarios. With intensive experiments, our proposed cross-supervised RODNet achieves 86% average precision and 88% average recall of object detection performance, which shows the robustness to noisy scenarios in various driving conditions.

Research on Efficient Retrieval Technology of Optical Characteristics of Image Resources in Digital Library

In order to improve the intelligence of digital library image resources, this paper proposes an efficient retrieval technology of digital library image resources based on deep learning and semantic feature extraction, which uses infrared and radio frequency transverse scanning technology to scan the front cover and back cover of digital library image resources, and divides the text and image regions of scanned digital library image resources infrared and radio frequency images. In the infrared and radio frequency transmission space, the collected scanned image resources of digital library are subjected to semantic diversification feature decomposition and feature separation, and the binary semantic ontology information features of digital library image resources are extracted. Combined with the image semantic feature extraction method, the convergence control in the process of extracting the structural features of digital library image resources is realized, and the digital library image resources are retrieved based on the extracted library resource information feature components. Simulation results show that this method has higher precision and shorter retrieval time, which improves the retrieval efficiency of digital library.

Temporal Super-resolution of Ultrasound Imaging Using Matrix Completion.

The temporal super-resolution of the dynamic ultrasound imaging, a means to observe rapid heart movements, is considered an important subject in medical diagnosis of cardiac conditions. Here, a new technique based on the acquisition scheme using the matrix completion (MC) theory is offered for the temporal super-resolution of the two-dimensional (2D) and three-dimensional (3D) ultrasound imaging. MC mentions the problem of completing a low-rank matrix when only a subset of its elements can be observed. Here, the lower scan lines are acquired. Whereby, the proposed method uses temporal and spatial information of the radio frequency (RF) image sequences for the reconstruction of skipped RF lines. This is performed using the construction of the MC images and then reconstruction of them by the MC theory. The results of the proposed method are compared with the compressive sensing (CS) reconstruction methods. The qualitative and quantitative evaluations of 2D and 3D data demonstrate that in the proposed method, which uses the spatial and temporal relation of RF images and the MC theory, the reconstruction is more accurate, and the reconstruction error is lower. The computational complexity of this method is very low. It also does not require hardware adjustments. Therefore, it can be easily implemented in current ultrasound-imaging devices with the frame-rate enhancement. For instance, the frame rate up to two times the original sequence is feasible using the proposed methods, while root mean square error is decreased by about 35% and 30% for 2D and 3D data, respectively, compared with the CS reconstruction method.

An Iterative Method for Estimating Nonlinear Elastic Constants of Tumor in Soft Tissue from Approximate Displacement Measurements.

Objectives Various elastography techniques have been proffered based on linear or nonlinear constitutive models with the aim of detecting and classifying pathologies in soft tissues accurately and noninvasively. Biological soft tissues demonstrate behaviors which conform to nonlinear constitutive models, in particular the hyperelastic ones. In this paper, we represent the results of our steps towards implementing ultrasound elastography to extract hyperelastic constants of a tumor inside soft tissue. Methods Hyperelastic parameters of the unknown tissue have been estimated by applying the iterative method founded on the relation between stress, strain, and the parameters of a hyperelastic model after (a) simulating the medium's response to a sinusoidal load and extracting the tissue displacement fields in some instants and (b) estimating the tissue displacement fields from the recorded/simulated ultrasound radio frequency signals and images using the cross correlation-based technique. Results Our results indicate that hyperelastic parameters of an unidentified tissue could be precisely estimated even in the conditions where there is no prior knowledge of the tissue, or the displacement fields have been approximately calculated using the data recorded by a clinical ultrasound system. Conclusions The accurate estimation of nonlinear elastic constants yields to the correct cognizance of pathologies in soft tissues.

Quantitative ultrasound and machine learning for assessment of steatohepatitis in a rat model.

To develop a machine learning model based on quantitative ultrasound (QUS) parameters to improve classification of steatohepatitis with shear wave elastography in rats by using histopathology scoring as the reference standard. This study received approval from the institutional animal care committee. Sixty male Sprague-Dawley rats were either fed a standard chow or a methionine- and choline-deficient diet. Ultrasound-based radiofrequency images were recorded in vivo to generate QUS and elastography maps. Random forests classification models and a bootstrap method were used to identify the QUS parameters that improved the classification accuracy of elastography. Receiver-operating characteristic analyses were performed. For classification of not steatohepatitis vs borderline or steatohepatitis, the area under the receiver-operating characteristic curve (AUC) increased from 0.63 for elastography alone to 0.72 for a model that combined elastography and QUS techniques (p < 0.001). For detection of liver steatosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.70, 0.65, and 0.69 to 0.78, 0.78, and 0.75 (p < 0.001). For detection of liver inflammation grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, ≤ 2 vs 3, respectively, the AUCs increased from 0.58, 0.77, and 0.78 to 0.66, 0.84, and 0.87 (p < 0.001). For staging of liver fibrosis grades 0 vs ≥ 1, ≤ 1 vs ≥ 2, and ≤ 2 vs ≥ 3, respectively, the AUCs increased from 0.79, 0.92, and 0.91 to 0.85, 0.98, and 0.97 (p < 0.001). QUS parameters improved the classification accuracy of steatohepatitis, liver steatosis, inflammation, and fibrosis compared to shear wave elastography alone. • Quantitative ultrasound and shear wave elastography improved classification accuracy of liver steatohepatitis and its histological features (liver steatosis, inflammation, and fibrosis) compared to elastography alone. • A machine learning approach based on random forest models and incorporating local attenuation and homodyned-K tissue modeling shows promise for classification of nonalcoholic steatohepatitis. • Further research should be performed to demonstrate the applicability of this multi-parametric QUS approach in a human cohort and to validate the combinations of parameters providing the highest classification accuracy.

Investigation of out-of-plane motion artifacts in 2D noninvasive vascular ultrasound elastography

Ultrasound noninvasive vascular elastography (NIVE) has shown its potential to measure strains of carotid arteries to predict plaque instability. When two-dimensional (2D) strain estimation is performed, either in longitudinal or cross-sectional view, only in-plane motions are considered. The motions in elevation direction (i.e. perpendicular to the imaging plane), can induce estimation artifacts affecting the accuracy of 2D NIVE. The influence of such out-of-plane motions on the performance of axial strain and axial shear strain estimations has been evaluated in this study. For this purpose, we designed a diseased carotid bifurcation phantom with a 70% stenosis and an in vitro experimental setup to simulate orthogonal out-of-plane motions of 1 mm, 2 mm and 3 mm. The Lagrangian speckle model estimator (LSME) was used to estimate axial strains and shears under pulsatile conditions. As anticipated, in vitro results showed more strain estimation artifacts with increasing magnitudes of motions in elevation. However, even with an out-of-plane motion of 2.0 mm, strain and shear estimations having inter-frame correlation coefficients higher than 0.85 were obtained. To verify findings of in vitro experiments, a clinical LSME dataset obtained from 18 participants with carotid artery stenosis was used. Deduced out-of-plane motions (ranging from 0.25 mm to 1.04 mm) of the clinical dataset were classified into three groups: small, moderate and large elevational motions. Clinical results showed that pulsatile time-varying strains and shears remained reproducible for all motion categories since inter-frame correlation coefficients were higher than 0.70, and normalized cross-correlations (NCC) between radiofrequency (RF) images were above 0.93. In summary, the performance of LSME axial strain and shear estimations appeared robust in the presence of out-of-plane motions (<2 mm) as encountered during clinical ultrasound imaging.

Efficient Sensitivity Based Reconstruction Technique to Accomplish Breast Hyperelastic Elastography.

Hyperelastic models have been acknowledged as constitutive equations which reliably model the nonlinear behaviors observed from soft tissues under various loading conditions. Among them, the Mooney-Rivlin, Yeoh, and polynomial models have been proved capable of accurately modeling responses of breast tissues to applied compressions. Hyperelastic elastography technique takes advantage of the disparities between hyperelastic parameters of varied tissues and the change in hyperelastic parameters in pathological processes. The precise reconstruction of hyperelastic parameters of a completely unknown pathology in the breast in a noninvasive and nondestructive way using the ultrasound elastography has been scrutinized in this paper. In the ultrasound elastography, tissue displacement field is extracted from radio frequency signals or images recorded using the ultrasound medical imaging system; hence the exact displacement field might not be obtained. Our results indicate that the parameters estimated by manipulating the iterative sensitivity-matrix based method converge to tissue's real hyperelastic parameters providing appropriate parameters are assigned to the hypothetical hyperelastic and regularization parameters. Iterative methods have therefore been proposed to compute proper hypothetical hyperelastic and regularization parameters. Accurate estimates of hyperelastic parameters of obscure breast pathology have been achieved even from imprecise measurements of displacements induced in the tissue by the ramp excitation.

Effects of tissue mechanical and acoustic anisotropies on the performance of a cross-correlation-based ultrasound strain imaging method

The anisotropic mechanical properties (mechanical anisotropy) and view-dependent ultrasonic backscattering (acoustic anisotropy) of striated muscle due to the underlying myofiber arrangement have been well documented, but whether they impact on ultrasound strain imaging (USI) techniques remains unclear. The aim of this study was therefore to investigate the performance of a cross-correlation-based two-dimensional (2D) USI method in anisotropic media under controlled quasi-static compression in silico and in vitro. First, synthetic pre- and post-deformed 2D radiofrequency images of anisotropic phantoms were simulated in two scenarios to examine the individual effect of the mechanical and acoustic anisotropies on strain estimation. In the first scenario, the phantom was defined to be transversely isotropic with the scatterer amplitudes following a zero-mean Gaussian distribution, while in the second scenario, the phantom was defined to be mechanically isotropic with Gaussian distributed scatterer amplitudes correlated along the principal directions of pre-defined fibers. These two anisotropies were then jointly incorporated into the ultrasound image simulation model with additional depth-dependent attenuation. Three imaging planes—the fiber plane with the fiber direction perpendicular to the ultrasound beam (TISperp_fb), the fiber plane with the fiber direction parallel to the beam (TISpara), and the transverse fiber plane (TISperp_cfb)—were studied. The absolute relative error (ARE) of the lateral strain estimates in TISperp_fb (20.99 ± 15.65%) was much higher than that in TISperp_cfb (4.14 ± 3.17%). The ARE in TISpara was unavailable owing to the large spatial extent of false peaks. The effect of tissue anisotropy on the performance of the 2D USI was further confirmed in an in vitro porcine skeletal muscle phantom. The best in-plane strain quality was again shown in TISperp_cfb (elastographic signal-to-noise ratio, or SNRe: >25 dB), whereas the most unreliable strain estimates were found as expected in TISpara (SNRe: <10 dB). The strain filter explained the effect of the mechanical anisotropy and required the underlying strain to be within an optimal range for estimation. Sonographic SNR (SNRs) was found to be altered by the acoustic anisotropy and was much lower in TISpara (~10 dB) than in TISperp_fb (~50 dB) in vitro, which affected the accuracy of the strain estimation. Speckle size showed no evident impact on strain estimation but requires further examination.

Hybrid Strategy to Simulate 3-D Nonlinear Radio-Frequency Ultrasound Using a Variant Spatial PSF

There are several simulators for medical ultrasound (US) applications that can fully compute the nonlinear propagation on the transmitted pulse and the corresponding radio-frequency (RF) images. Creanuis is one recent model used to generate nonlinear RF images; however, the time requirements are long compared with linear models using a convolution strategy. In this paper, we describe an approach using convolution coupled with nonlinear information to create a pseudoacoustic tool that is able to quickly generate realistic US images. Several point-spread functions (PSFs) are computed with Creanuis. These PSFs are extracted at different depths in order to take into account variation in the resolution and apparition of harmonics during propagation. One convolution is then conducted for each PSF to generate a set of nonlinear raw RF images. The final image is obtained by merging these raw images using a PSF-weighting function. This hybrid Creanuis strategy was extended to 2-D, 2-D +t , 3-D, and 3-D +t images for both linear and phased-array geometries. We validated h-Creanuis using the mean deviation between the proposed images and those created using Creanuis and examined their statistical distributions. The mean deviations of Creanuis and h-Creanuis are below 2.5% for fundamental and second-harmonic images. The 3-D +t images obtained demonstrate the correct motion characteristics for speckle in sequences of both fundamental and second-harmonic images.

Heterogeneous Tissue Characterization Using Ultrasound: A Comparison of Fractal Analysis Backscatter Models on Liver Tumors

Assessment of tumor tissue heterogeneity via ultrasound has recently been suggested as a method for predicting early response to treatment. The ultrasound backscattering characteristics can assist in better understanding the tumor texture by highlighting the local concentration and spatial arrangement of tissue scatterers. However, it is challenging to quantify the various tissue heterogeneities ranging from fine to coarse of the echo envelope peaks in tumor texture. Local parametric fractal features extracted via maximum likelihood estimation from five well-known statistical model families are evaluated for the purpose of ultrasound tissue characterization. The fractal dimension (self-similarity measure) was used to characterize the spatial distribution of scatterers, whereas the lacunarity (sparsity measure) was applied to determine scatterer number density. Performance was assessed based on 608 cross-sectional clinical ultrasound radiofrequency images of liver tumors (230 and 378 representing respondent and non-respondent cases, respectively). Cross-validation via leave-one-tumor-out and with different k-fold methodologies using a Bayesian classifier was employed for validation. The fractal properties of the backscattered echoes based on the Nakagami model (Nkg) and its extend four-parameter Nakagami-generalized inverse Gaussian (NIG) distribution achieved best results—with nearly similar performance—in characterizing liver tumor tissue. The accuracy, sensitivity and specificity of Nkg/NIG were 85.6%/86.3%, 94.0%/96.0% and 73.0%/71.0%, respectively. Other statistical models, such as the Rician, Rayleigh and K-distribution, were found to not be as effective in characterizing subtle changes in tissue texture as an indication of response to treatment. Employing the most relevant and practical statistical model could have potential consequences for the design of an early and effective clinical therapy.

Ultrasound strain mapping of Achilles tendon compressive strain patterns during dorsiflexion

Heel lifts are commonly prescribed to patients with Achilles tendinopathy, yet little is known about the effect on tendon compressive strain. The purposes of the current study were to (1) develop a valid and reliable ultrasound elastography technique and algorithm to measure compressive strain of human Achilles tendon in vivo, (2) examine the effects of ankle dorsiflexion (lowering via controlled removal of a heel lift and partial squat) on compressive strain of the Achilles tendon insertion and (3) examine the relative compressive strain between the deep and superficial regions of the Achilles tendon insertion. All tasks started in a position equivalent to standing with a 30mm heel lift. An ultrasound transducer positioned over the Achilles tendon insertion was used to capture radiofrequency images. A non-rigid image registration-based algorithm was used to estimate compressive strain of the tendon, which was divided into 2 regions (superficial, deep). The bland-Altman test and intraclass correlation coefficient were used to test validity and reliability. One-way repeated measures ANOVA was used to compare compressive strain between regions and across tasks. Compressive strain was accurately and reliably (ICC>0.75) quantified. There was greater compressive strain during the combined task of lowering and partial squat compared to the lowering (P=.001) and partial squat (P<.001) tasks separately. There was greater compressive strain in the deep region of the tendon compared to the superficial for all tasks (P=.001). While these findings need to be examined in a pathological population, heel lifts may reduce tendon compressive strain during daily activities.

Experimental Application of Ultrafast Imaging to Spectral Tissue Characterization

Ultrasound ultrafast imaging (UI) allows acquisition of thousands of frames per second with a sustained image quality at any depth in the field of view. Therefore, it would be ideally suited to obtain good statistical sampling of fast-moving tissues using spectral-based techniques to derive the backscatter coefficient (BSC) and associated quantitative parameters. In UI, an image is formed by insonifying the medium with plane waves steered at different angles, beamforming them and compounding the resulting radiofrequency images. We aimed at validating, experimentally, the effect of these beamforming protocols on the BSC, under both isotropic and anisotropic conditions. Using UI techniques with a linear array transducer (5–14 MHz), we estimated the BSCs of tissue-mimicking phantoms and flowing porcine blood at depths up to 35 mm with a frame rate reaching 514 Hz. UI-based data were compared with those obtained using single-element transducers and conventional focusing imaging. Results revealed that UI compounded images can produce valid estimates of BSCs and effective scatterer size (errors less than 2.2 ± 0.8 and 0.26 ± 0.2 dB for blood and phantom experiments, respectively). This work also describes the use of pre-compounded UI images (i.e., steered images) to assess the angular dependency of circulating red blood cells. We have concluded that UI data sets can be used for BSC spectral tissue analysis and anisotropy characterization.

A Closed-Form Differential Formulation for Ultrasound Spatial Calibration: Single Wall Phantom

Calibration is essential in freehand 3-D ultrasound to find the spatial transformation from the image coordinates to the sensor coordinate system. Ease of use, simplicity, precision and accuracy are among the most important factors in ultrasound calibration, especially when aiming to make calibration more reliable for day-to-day clinical use. We introduce a new mathematical framework for the simple and popular single-wall calibration phantom with a plane equation pre-determination step and the use of differential measurements to obtain accurate measurements. The proposed method provides a novel solution for ultrasound calibration that is accurate and easy to perform. This method is applicable to both radiofrequency (RF) and B-mode data, and both linear and curvilinear transducers. For a linear L14-5 transducer, the point reconstruction accuracy (PRA) of reconstructing 370 points is 0.73 ± 0.23 mm using 100 RF images, whereas the triple N-wire PRA is 0.67 ± 0.20 mm using 100 B-mode images. For a curvilinear C5-2 transducer, the PRA using the proposed method is 0.86 ± 0.28 mm on 400 points using 100 RF images, whereas N-wire calibration gives a PRA of 0.80 ± 0.46 mm using 100 B-mode images. Therefore, the accuracy of the proposed variation of the single-wall method using RF data is practically similar to the N-wire method while offering a simpler phantom with no need for accurate design and construction.

Thomson’s multitaper approach combined with coherent plane-wave compounding to reduce speckle in ultrasound imaging

In ultrasound imaging, the speckle pattern limits the image quality. Spatial and frequency compounding are commonly used to reduce speckle noise or improve the contrast. Although recent implementations can preserve a frame rate that is compatible with real-time imaging (e.g., synthetic aperture compounding), most classic compounding techniques are based on the coherent combination of several radiofrequency images from the same investigated area, which reduces the frame rate. Furthermore, Thomson’s multitaper approach aims to smooth the speckle by incoherently combining the obtained B-mode images after applying different apodization windows on the same original data. With only one acquisition, the frame rate remains high, but the spatial resolution is decreased.To improve the resolution and contrast while reducing the speckle noise, this paper proposes combining the coherent plane-wave compounding technique (CPWC) with Thomson’s multitaper method. The resulting multitaper coherent plane-wave compounding (MCPWC) takes advantage of coherent and incoherent approaches.Simulations and experimental results demonstrate that in terms of the signal-to-noise ratio, contrast, and resolution, the image quality is increased using plane wave emissions at approximately ten steering angles with three Thomson’s tapers. Outside the focal area, the lateral resolution is improved by a factor of 2, and the contrast is increased by approximately 2dB compared with images obtained using a single focalization technique and Thomson’s multitaper approach.