Virtual Extended-Range Tomography (VERT): Contact-free realistic ultrasonic bone imaging

Ultrasound tomography generally struggles to reconstruct high-contrast and/or extended-range problems. A prime example is site-specific in-vivo bone imaging, crucial for accurately assessing the risk of life-threatening fractures, which are preventable given accurate diagnosis and treatment. In this type of problem, two main obstacles arise: (a) an external region prohibits access to the region of interest (ROI), and (b) high contrast exists between the two regions. These challenges impede existing algorithms -- including bent-ray tomography (BRT), known for its robustness, speed, and reasonable short-range resolution. We propose Virtual Extended-Range Tomography (VERT), which tackles these challenges through (a) placement of virtual transducers directly on the ROI, facilitating (b) rapid initialisation before BRT inversion. In-silico validation against BRT with and without a-priori information shows superior resolution and robustness -- while maintaining or even improving speed. These improvements are drastic where the external region is much larger than the ROI. Additional validation against the practically impossible -- BRT directly on the ROI -- demonstrates that VERT is approaching the resolution limit. The capability to solve high-contrast extended-range tomography problems without prior knowledge about the ROI's interior has many implications. VERT has the potential to unlock site-specific in-vivo bone imaging for assessing fracture risk, potentially saving millions of lives globally. In other applications, VERT may replace classical BRT to yield improvements in resolution, robustness and speed -- especially where the ROI does not cover the entire imaging array. For even higher resolution, VERT offers a reliable starting background to complement algorithms with less robustness and high computational costs.

Efficient generation of realistic guided wave signals for reliability estimation

Across non-destructive testing (NDT) and structural health monitoring (SHM), accurate knowledge of the systems' reliability for detecting defects, such as Probability of Detection (POD) analysis is essential to enabling widespread adoption. Traditionally this relies on access to extensive experimental data to cover all critical areas of the parametric space, which becomes expensive, and heavily undermines the benefit such systems bring. In response to these challenges, reliability estimation based on numerical simulation emerges as a practical solution, offering enhanced efficiency and cost-effectiveness. Nevertheless, precise reliability estimation demands that the simulated data faithfully represents the real-world performance. In this context, a numerical framework tailored to generate realistic signals for reliability estimation purposes is presented here, focusing on the application of guided wave SHM for pipe monitoring. It specifically incorporates key characteristics of real signals: random noise and coherent noise caused by the imbalance in transducer performance within guided wave monitoring systems. The effectiveness of our proposed methodology is demonstrated through a comprehensive comparative analysis between simulation-generated signals and experimental signals both individually and statistically. Furthermore, to assess the reliability of a guided wave system in terms of the inspection range for pipe monitoring, a series of POD analyses using simulation-generated data were conducted. The comparison of POD curves derived from ideal and realistic simulation data underscores the necessity of considering coherent noise for accurate POD curve calculations. Moreover, the POD analysis based on realistic simulation-generated data provides a quantitative estimation of the inspection range with more details compared to the current industry practice.