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In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the $-6$dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

In the next generation of ultrasound imaging systems, Capacitive micromachined ultasonic transducer (CMUT) based on microelectromechanical systems (MEMS) is a promising research direction of transducers, which has wide application prospects. In this paper, based on the study of three imaging methods, including classical phased array (CPA) imaging, classical synthetic aperture (CSA) imaging and phased subarray (PSA) imaging, several different imaging schemes are designed for linear CMUT array, after that the performances of these imaging schemes are compared and analyzed. The effects of the three imaging methods are verified and analyzed based on the linear CMUT array. Through analysis, it is found that the image quality of the classical phased array imaging method is the best, the imaging quality of the above three imaging methods can be effectively improved by adopting the amplitude apodization and dynamic focusing method. The research results in this paper will provide theoretical basis and application reference for the design of ultrasonic imaging system based on linear CMUT array in the future.

In this paper, a new technique for solving the two-dimensional inverse scattering problem for ultrasound inverse imaging is presented. Reconstruction of a two-dimensional object is accomplished using an iterative algorithm which combines the conjugate gradient (CG) method and a neural network (NN) approach. The neural network technique is used to exploit knowledge of the statistical characteristics of the object to enhance the performance of the conjugate gradient method. The results for simulations show that the CGNN algorithm is more accurate than the CG method and, in addition, convergence occurs more rapidly. For the CGNN algorithm, approximately 50% fewer iterations are needed to obtain the inverse solution for a signal-to-noise ratio (SNR) of 50 dB. For a smaller SNR of 35 dB, the CGNN method is not as accurate, but it still gives reasonable results.

The purpose of this study was to investigate the morphological and mechanical properties of the transverse carpal ligament (TCL) in patients with carpal tunnel syndrome (CTS). Thickness and stiffness of the TCL in eight female CTS patients and eight female control subjects were examined using ultrasound imaging modalities. CTS patients had a 30.9% thicker TCL than control subjects. There was no overall difference in TCL stiffness between the two groups, but the radial TCL region was significantly stiffer than the ulnar region within the CTS group and such a regional difference was not found for the controls. The increased thickness and localized stiffness of the TCL for CTS patients may contribute to CTS symptoms due to reduction in carpal tunnel space and compliance. Advancements in ultrasound technology provide a means of understanding CTS mechanisms and quantifying the morphological and mechanical properties of the TCL in vivo.

Introduction: MyotonPRO technology is a clinically accessible tool for objectively monitoring the biomechanical and viscoelastic properties of musculoskeletal tissues. This study aimed to identify the level of accuracy needed for recording from specified measurement sites on the soleus muscle and plantar fascia (PF), which are important for postural control and gait.

Methods: In 20 healthy adults, MyotonPRO measurements were taken 1 cm above and below a standardized point on the soleus and PF. Ultrasound scans were also taken at each site to measure subcutaneous tissue thickness to aid interpretation of MyotonPRO findings.

Results: Biomechanical and viscoelastic properties of the soleus and PF, measured using the MyotonPRO, were not affected by the change in the recording site. Subcutaneous tissue thickness above the PF changed significantly ($p$ < 0.001) by 0.61 cm when moving distally by 2 cm, whilst above the soleus, these did not differ significantly ($p$ = 0.175) when moving distally by 2 cm.

Conclusion: Changing the recording site 1 cm above or below the standardized site did not affect either the MyotonPRO results (soleus and PF) or ultrasound results for soleus, whilst this same distance affected ultrasound measurements of subcutaneous tissue thickness above the PF significantly. Moving away from the musculoskeletal junction reduces the need for precision in relocating the recording site for measuring soleus. These findings allow for quicker and easier measurement of soleus or PF using the MyotonPRO.

Objective. The aim of this paper is to show an algorithm for the automatic computer-based tracing (ACT) of common carotid artery (CCA) in longitudinal B-mode ultrasound images characterized by four main features: (i) user-independence; (ii) suitability to normal and pathological images; (iii) robustness to noise; and (iv) independent of ultrasound OEM scanner.

Methods. Three hundred longitudinal B-mode images (100 normal CCAs, 100 CCAs with increased intima-media thickness, 60 stable plaques, and 40 echolucent plaques) were acquired using three different (GE, Siemens, and Biosound) OEM ultrasound image scanners. The algorithm processed each image to delineate the region of interest containing the CCA. Output of the algorithm are three segmentation lines representing (a) distal (far) and (b) near adventitia layers, and (c) lumen of the CCA. Three operators qualitatively scored the ACTs.

Results. The CCA was correctly automatically traced in all the 300 B-mode images. The performance was independent on the image scanner used to acquire the image or on the type of the CCA (healthy versus pathologic). Eight ACTs out of 300 received a poor score after visual inspection due to an automated adventitia tracing that did not correctly follow the CCA wall in a small portion of the image.

Conclusions. The proposed algorithm is robust in ACTs of CCA since it is independent of scanner and normal/abnormal wall. This approach could constitute a general basis for a completely automated segmentation procedure.

Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes $7\times 15$, $15\times 7$, $7\times 3$, and $3\times 7$ pixels, and two noncentered areas with sizes $7\times 3$ pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex ($31.2\%\pm 14.4\%$) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images ($18.2\%\pm 11.8\%$; $p<0.001$). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.

Monitoring the fetal growth and diagnosing any possible abnormality plays a vital role in ensuring the healthy growth of a fetus. Certain health issues like Hyperthermia, Premature Rupture of Membranes (PROM) and Intrauterine Growth Restriction (IUGR) has to be diagnosed early. A pilot study comprising of 27 pregnant and 2 non-pregnant subjects was conducted to check the effectiveness of Thermal imaging in predicting the fetal growth. The heat dissipated by the fetus to the maternal abdominal wall is acquired as a surface thermal distribution. These images were processed qualitatively and quantitatively for better understanding. There was a consistent higher thermal pattern for pregnant women. A more pronounced temperature pattern is notable in the umbilical region that correlates with gestation age. However, as thermal pattern varies with age, gestation period and BMI, it is advisable to track the same person and compare the images for better assessment. This pilot study justifies the need for more elaborate study in building a database for classification and interpretation of thermogram to detect fetal abnormality with reduced human interpretation.

A new nonlinear beamformer named Double-Stage Delay Multiply and Sum (DS-DMAS) has recently been proposed as a variant of the Delay Multiply and Sum (DMAS) algorithm. DS-DMAS expands DMAS into a summation of multiple terms and considers this summation as Delay and Sum (DAS). In order to address the shortage of DAS, DS-DMAS replaced the DAS with DMAS. However, the construction of the new signal in the DS-DMAS algorithm still employs the DAS method. While DAS is a well-established and reliable method, its output is solely dependent on the signal amplitude. Therefore, signal similarity-based methods such as the Coherence Factor (CF) and the Sign Coherence Factor (SCF) have been proposed to weigh the DAS output and optimize its performance. Taking this into consideration, we incorporated the CF and SCF to weigh each newly generated signal in DS-DMAS, resulting in the Coherence Factor-based Double-Stage Delay Multiply and Sum (DS-DMAS-CF) and the Sign Coherence Factor-based Double-Stage Delay Multiply and Sum (DS-DMAS-SCF) approaches. Our focus is primarily on comparing the performance of DS-DMAS-CF and DS-DMAS-SCF. The results indicate that DS-DMAS-SCF exhibits better noise suppression capabilities compared to DS-DMAS-CF.

Piezocomposite design for dedicated ultrasonic imaging applications requires precise homogenization models for predicting the electromechanical characteristics of the new material. Thus, several homogenization models have been developed. As part of this work, we applied several analytical homogenization models for piezocomposite of 2–2 and 1–3 connectivities. To validate these analytical models, a comparative study was made between various models and experimental measurements. As a result, these homogenized electromechanical properties are effectively used for the calculation and comparison of electroacoustic response for typical transducers aimed at ultrasound imaging applications. An optimal design of transducer aimed at ultrasound imaging applications is proposed as a dedicated imaging performance index, elaborated through a trade-off between sensitivity and bandwidth.

Robotic systems are being applied to medical interventions as they increase the operational accuracy. The proposed autonomous and ultrasound guided 5-DOF parallel robot can achieve such accuracy for needle biopsies, which particularly demand precise needle positioning and insertion. In this paper, the robot’s mechanical design, system identifications, and the design of its controller are explained. A torque computed controller with gravity compensation and friction models, yielding a 0.678mm RMS position error for the needle tip, was used. A novel method was used for 3D space calibration of the images for detecting the volume of interest in the biopsy procedure by a multipoint crosswire phantom with parallel threads. The calibration technique had a validation RMS error of 0.03mm.

We propose a fast 2D full-waveform forward and inverse scattering solver to reconstruct ultrasonic speed and attenuation properties. The forward method is based on the volume integral equation, accelerated by the extended Born approximation and biconjugate-gradient fast Fourier transform method. The inverse method is based on the extended contrast source inversion. The research results show that our proposed method can fully unravel multiple scattering effects and achieve sub-wavelength resolutions. Numerical experiments indicate that the efficiency and robustness of the forward and inverse methods.

We describe a non-parametric pixel appearance probability model to represent local image information. It allows an optimal image analysis framework that integrates low- and high-level stages to substantially improve overall accuracy of object reconstruction. In this framework, feature detection would be an overall consequence rather than an intermediate result. The pixel appearance probability model is a probability density function obtained by grid quantization. A grid is found by a genetic algorithm and a local refinement algorithm. The density values in each cell of the grid are computed by smoothing neighboring cells. We apply the pixel appearance probability model to represent features of echocardiographic images. We illustrate the substantially improved performance on left ventricle surface reconstruction due to the proposed model.