Detection of overlapping ultrasonic echoes with deep neural networks.

Ultrasonic Pulse-Echo techniques have a significant role in monitoring the integrity of layered structures and adhesive joints along their service life. However, when acoustically measuring thin layers, the resulting echoes from two successive interfaces overlap in time, limiting the resolution that can be resolved using conventional pulse-echo techniques. Deep convolutional networks have arisen as a promising framework, providing state-of-the-art performance for various signal processing tasks. In this paper, we explore the applicability of deep networks for detection of overlapping ultrasonic echoes. The network is shown to outperform traditional algorithms in simulations for a significant range of echo overlaps, echo pattern variance and noise levels. In addition, experiments on two physical phantoms are conducted, demonstrating superiority of the network over traditional methods for layer thickness estimation.

A unified deep network for beamforming and speckle reduction in plane wave imaging: A simulation study.

Plane Wave Imaging is a fast imaging method used in ultrasound, which allows a high frame rate, but with compromised image quality when a single wave is used. In this work a learning-based approach was used to obtain improved image quality. The entire process of beamforming and speckle reduction was embedded in a single deep convolutional network, and trained with two types of simulated data. The network architecture was designed based on traditional physical considerations of the ultrasonic image formation pipe. As such, it includes beamforming with spatial matched filters, envelope detection, and a speckle reduction stage done in log-signal representation, with all stages containing trainable parameters. The approach was tested on the publicly available PICMUS datasets, achieving axial and lateral full-width-half-maximum (FWHM) resolution values of 0.22 mm and 0.35 mm respectively, and a Contrast to Noise Ratio (CNR) metric of 16.75 on the experimental datasets.

Cluster-enhanced sparse approximation of overlapping ultrasonic echoes.

Ultrasonic pulse-echo methods have been used extensively in non-destructive testing of layered structures. In acoustic measurements on thin layers, the resulting echoes from two successive interfaces overlap in time, making it difficult to assess the individual echo parameters. Over the last decade sparse approximation methods have been extensively used to address this issue. These methods employ a large dictionary of elementary functions (atoms) and attempt to select the smallest subset of atoms (sparsest approximation) that represent the ultrasonic signal accurately. In this paper we propose the cluster-enhanced sparse approximation (CESA) method for estimating overlapping ultrasonic echoes. CESA is specifically adapted to deal with a large number of signals acquired during an ultrasonic scan. It incorporates two principal algorithms. The first is a clustering algorithm, which divides a set of signals comprising an ultrasonic scan into groups of signals that can be approximated by the same set of atoms. The second is a two-stage iterative algorithm, which alternates between update of the atoms associated with each cluster, and re-clustering of the signals according to the updated atoms. Because CESA operates on clusters of signals, it achieves improved results in terms of approximation error and computation time compared with conventional sparse methods, which operate on each signal separately. The superior ability of CESA to approximate highly overlapping ultrasonic echoes is demonstrated through simulation and experiments on adhesively bonded structures.

A matching pursuit method for approximating overlapping ultrasonic echoes.

Ultrasonic pulse-echo methods have been used extensively in measuring the thickness of layered structures as well as those of thin adhesive interface layers. When acoustically measuring thin layers, the resulting echoes from two successive interfaces overlap in time, limiting the minimum thickness that can be resolved using conventional pulse-echo techniques. In this paper, we propose a method, named support matching pursuit (SMP), for resolving the individual echoes. The method is based on the concept of sparse signal approximation in an overcomplete dictionary composed of Gabor atoms (elementary functions). Although the dictionary enables highly flexible approximations, it is also overcomplete, which implies that the approximation is not unique. We propose a method for approximation in which each ultrasonic echo is principally represented by a single atom and therefore has a physical interpretation. SMP operates similarly to the sparse matching pursuit (MP) method. It iteratively improves the approximation by adding, at each iteration, a single atom to the solution set. However, our atom selection criterion utilizes the time localization nature of ultrasonic echoes, which causes portions of a multi-echo ultrasonic signal to be composed mainly from a single echo. This leads to accurate approximations in which each echo is characterized by a set of physical parameters that represent the composing ultrasonic echoes. In the current research we compare SMP to other sparse approximation methods such as MP and basis pursuit (BP). We perform simulations and experiments on adhesively bonded structures which clearly demonstrate the superior performance of the SMP method over the MP and BP methods.