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In this article, an ultra‐wideband (UWB) monostatic radar based microwave imaging (MWI) approach is proposed to detect the presence and localize the position of breast tumor in women (usually greater than 45 years of age). For this purpose, a "C" shaped elliptically polarized DRA (dielectric resonator antenna) with an impedance bandwidth of 7.3 GHz (i.e., 3.4–10.7 GHz) covering the UWB has been proposed as a sensor to record the backscattered S parameter signals from the breast tissues. This is done by rotating the proposed DRA in monostatic radar configuration around the breast phantom with an interval of 30° in elevation (0–180) and azimuthal planes (0–360), respectively. The recorded "S" parameter data is then processed using beam‐forming algorithms namely delay and sum (DAS), improved delay and sum (IDAS), delay multiply and sum (DMAS) to form 2D images of the breast tumor and locate its position (using MATLAB R 2013a). The design and simulation of the entire set up that includes the C shaped UWB DRA & Bio media (breast phantom) is done using CST MWS V'17. The fabrication of the proposed DRA is carried out using wet etching and water jet cutting process. The physical model of the Bio media is prepared using gelatin (skin), petroleum‐jelly (fat), and wheat‐flour (tumor). The validation of antenna radiation parameters is done using a VNA (vector network analyzer) model no. E5063A and good agreement between the simulated and measured results is observed. The proposed C shaped DRA is used in a monostatic radar based configuration to collect "S" parameters and the data is processed in a GPU to reconstruct a 2D image of the scanned breast phantom area. The size and detected location of tumor using beam‐forming is also close to the actual physical location of tumor with an error of 6.2% from the actual tumor location. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is an important modality used in medical imaging. One of the significant stages in the ultrasound imaging is the beamforming process. This article proposes a new technique for reducing the overall computational time of adaptive linear ultrasound imaging. The method uses the discrete cosine transform‐based reconstruction for missing data imputation. The novelty of the paper is that we do not need to beam‐form the total scan lines, so the time of image construction can be saved significantly. In other words, a fraction of the total scan lines is selected for beamforming and the others are assumed to have values as Not‐a‐Number (NaN). The proposed reconstruction technique tries to assign appropriate values to the NaN ones. We applied the proposed method to the simulated and experimental radio frequency (RF) datasets for resolution and contrast evaluation. Results showed that the proposed technique is near to the minimum variance (MV) method in terms of resolution and contrast, and has less computational time for image formation compared to the MV. As some quantitative examples in some experiments we have formed only 50% and 33% of the total lines and reconstructed the rest, then we have been able to increase the frame rate twice and three times, respectively, which can be very useful in many applications, especially in echocardiography imaging. In addition, since the execution time of the reconstruction algorithm is not very significant, we were also able to increase the speed by two and three times while achieving an error of less than 10% compared to the case of using all image lines. [ABSTRACT FROM AUTHOR]

Guided-wave structural health monitoring (SHM) systems with piezoelectric sensors are investigated for localisation of barely visible impact damage in CFRP plates under vibration and different thermal conditions. A single baseline set is used in a delay-and-sum algorithm with temperature correction for damage localisation in a large temperature range. Damage localisation is also demonstrated under transient thermal conditions, with signals recorded while the temperature is rapidly decreased. Damage severity due to successive impact events is studied under constant temperature. Damage is also localised when the plate is subjected to random vibration. [ABSTRACT FROM AUTHOR]

Reconstruction of ultrasound data from single plane wave Radio Frequency (RF) data is a challenging task. The traditional Delay and Sum (DAS) method produces an image with low resolution and contrast, if employed with RF data from only a single plane wave. A Coherent Compounding (CC) method that reconstructs the image by coherently summing the individual DAS images was proposed to enhance the image quality. However, CC relies on a large number of plane waves to accurately sum the individual DAS images, hence it produces high quality images but with low frame rate that may not be suitable for time-demanding applications. Therefore, there is a need for a method that can create a high quality image with higher frame rates. Furthermore, the method needs to be robust against the input transmission angle of the plane wave. To reduce the method's dependence on the input angle, we propose to unify the RF data at different angles by learning a linear data transformation from different angled data to a common, 0 ° data. We further propose a cascade of two independent neural networks to reconstruct an image, similar in quality to CC, by making use of a single plane wave. The first network, denoted as "PixelNet", is a fully Convolutional Neural Network (CNN) which takes in the transformed time-delayed RF data as input. PixelNet learns optimal pixel weights that get element-wise multiplied with the single angle DAS image. The second network is a conditional Generative Adversarial Network (cGAN) which is used to further enhance the image quality. Our networks were trained on the publicly available PICMUS and CPWC datasets and evaluated on a completely separate, CUBDL dataset obtained from different acquisition settings than the training dataset. The results thus obtained on the testing dataset, demonstrate the networks' ability to generalize well on unseen data, with frame rates better than the CC method. This paves the way for applications that require high-quality images reconstructed at higher frame rates. • A linear data transformation method to unify RF data across different plane waves. • A two cascaded neural network was designed to be trained with low amounts of data. • Method shows high generalizability on test data not included in training. • Images similar to coherent compounding method are obtained at 5x higher frame rate. • Higher frame rate could be used in applications like functional ultrasound imaging. [ABSTRACT FROM AUTHOR]

Dynamic receive beamforming on a Delta–Sigma modulator’s low-resolution output bit-stream suffers from a severely increased noise floor, rendering the promising method infeasible. This paper addresses the mechanism behind the increased noise floor. By using multirate theory and bifrequency analysis, we show that the increased noise floor is caused by aliasing/imaging of the powerful quantization noise from specific frequency regions according to the focus depth. We analyze both the conventional delay-and-sum method and the previously proposed insert zero compensation method. For the latter technique, we show that the alias/image bands have the first-order suppression because of a zero at the origin. This finding leads to the proposal of an architecture combining a cascaded integrator-comb filter with the insert zero compensation method. Backed by simulations of beamformed Delta–Sigma modulated sequences and point spread functions, we compare this architecture to different compensation methods and the ideal Delta–Sigma beamformer. The proposed technique is shown to have low implementation cost and achieve levels of dynamic range comparable to those of the ideal Delta–Sigma beamformer. [ABSTRACT FROM PUBLISHER]

• Combination of tensor completion and STA is proposed to improve the frame rate. • In the proposed method, only a limited number of elements are excited for emission. • Tensor completion is used for optimizing image quality. • By using this technique, the STA runtime is reduced significantly. • Our results confirm a close image quality of the proposed method to the original STA. One of the most important methods in medical ultrasound imaging is the synthetic transmit aperture (STA). Despite the image quality improvement in the STA, this method suffers from several limitations, including a limited data acquisition rate and an increase in the overall time to form a single frame. Tensor completion (TC) is a powerful technique that uses rank minimization to recover missing information from a low-rank tensor. This paper provides a novel random synthetic transmit aperture (RSTA) method based on using only a randomly selected part (a fraction) of the linear array elements in the transmit mode to increase the data acquisition rate and then applying the tensor completion (TC) to improve the image quality. By the proposed method, as it is not necessary to transmit all elements sequentially, the data acquisition rate is improved and the overall time for creating an image is also significantly reduced. We investigated the proposed idea by using several simulated and experimental phantoms. Results showed that the proposed method could increase the data acquisition rate up to three times with the image quality difference of less than 6% compared to the original STA method. [ABSTRACT FROM AUTHOR]