Model-Based Iterative Reconstruction for One-Sided Ultrasonic Non-Destructive Evaluation

TL;DR

This paper introduces a model-based iterative reconstruction algorithm for one-sided ultrasonic non-destructive evaluation, improving image quality and artifact reduction over traditional methods like SAFT, especially for large datasets.

Contribution

The paper presents a novel MBIR algorithm that incorporates detailed forward modeling and joint reconstruction techniques for enhanced UNDE imaging.

Findings

MBIR outperforms SAFT in simulated and experimental tests.

Enhanced forward model reduces artifacts and improves resolution.

Joint reconstruction effectively handles large data sets.

Abstract

One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques with linear models have been proposed which can improve on SAFT, but they tend to make simplifying assumptions that do not address how to obtain reconstructions from large real data sets. In this paper, we propose a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems. To further reduce some of the artifacts in the results, we enhance the forward model to account for the transmitted beam profile, the occurrence of direct arrival signals, and the correlation between scans from adjacent regions. Next, we combine the forward model with a spatially variant prior model to account for the attenuation of deeper regions. We also present an algorithm to jointly reconstruct measurements from large data sets. Finally, using simulated and extensive experimental data, we show MBIR results and demonstrate how we can improve over SAFT as well as existing regularized inversion techniques.