Corrosion detection is a critical problem in many research areas. Guided wave tomography provides a powerful tool to estimate the remaining thickness of corroded structures. This article introduces an ultrasonic quantitative tomography method called fast inversion tomography (FIT) for corrosion mapping on plate-like structures. FIT consists of offline training and online inversion. The offline training stage uses the supervised descent method (SDM) to generate a series of average descent directions iteratively by minimizing the waveform misfit function between the fixed initial models and training examples. The minimization of the misfit function is equivalent to solving the linear least-squares problem. In the online inversion stage, we reconstruct the velocity map of testing examples using the learned descent directions in an iterative manner. Then, we convert the velocity map into the thickness map using the dispersive characteristics of a specific guided wave mode. The performance of this technique is evaluated using synthetic datasets which include both training and testing examples with different corrosion depths and shapes on an aluminum plate. We also compare the reconstruction accuracy and computation efficiency between FIT and time/frequency-domain full-waveform inversion. The results indicate that FIT exhibits great performance in the problem of quantitative corrosion imaging.

The convenience, safety, and affordability of ultrasound imaging make it a vital non-invasive diagnostic technique for examining soft tissues. However, significant differences in acoustic impedance between the skull and soft tissues hinder the successful application of traditional ultrasound for brain imaging. In this study, we propose a physics-embedded neural network with deep learning based full waveform inversion (PEN-FWI), which can achieve reliable quantitative imaging of brain tissues. The network consists of two fundamental components: forward convolutional neural network (FCNN) and inversion sub-neural network (ISNN). The FCNN explores the nonlinear mapping relationship between the brain model and the wavefield, replacing the tedious wavefield calculation process based on the finite difference method. The ISNN implements the mapping from the wavefield to the model. PEN-FWI includes three iterative steps, each embedding the FCNN into the ISNN, ultimately achieving tomography from wavefield to brain models. Simulation and laboratory tests indicate that PEN-FWI can produce high-quality imaging of the skull and soft tissues, even starting from a homogeneous water model. PEN-FWI can achieve excellent imaging of clot models with constant uniform distribution of velocity, randomly Gaussian distribution of velocity, and irregularly shaped randomly distributed velocity. Robust differentiation can also be achieved for brain slices of various tissues and skulls, resulting in high-quality imaging. The imaging time for a horizontal cross-sectional image of the brain is only 1.13 seconds. This algorithm can effectively promote ultrasound-based brain tomography and provide feasible solutions in other fields.

As one of the important vital signs, blood pressure is an indicator of cardiovascular risk assessment. Central arterial pressure has a strong pathophysiological relationship with target organ damage and cardiovascular disease. Arterial blood pressure monitoring has high predictive value for cardiovascular and cerebrovascular events. In this work, the ultrasonic piezoelectric transducer array we designed and optimized differs from conventional rigid ultrasound probes. The transducer array is compact in size (39 mm×37 mm×0.71 mm), stretchable and flexible, which can attach seamlessly to human skin. Arterial blood pressure monitoring based on the flexible ultrasonic transducer array has advantages over existing techniques, such as it is insensitive to motion and allows continuous blood pressure detection without any injury or vascular occlusion. The flexible transducer array has been used to achieve continuous blood pressure detection of the human carotid artery in specific excitation and phased array focused emission mode. Compared with the medical central arterial pressure detector, the correctness of the flexible array for measuring blood pressure was verified. In addition to systolic and diastolic blood pressure values, the blood pressure waveform is abundant in physical information about arterial elasticity and heart function, and it has great potential for application in hospital and home environments.