One-sided ultrasonic non-destructive evaluation (UNDE) uses ultrasound signals to investigate and inspect structures that are only accessible from one side. A widely used reconstruction technique in UNDE is the synthetic aperture focusing technique (SAFT). SAFT produces fast reconstruction and reasonable images for simple structures. However, for large complex structures, SAFT reconstructions suffer from noise and artifacts. To resolve some of the drawbacks of SAFT, an ultrasonic model-based iterative reconstruction (MBIR) algorithm, a method based on Bayesian estimation, was proposed that showed significant enhancement over SAFT in reducing noise and artifacts. In this paper, we build on previous investigations of the use of MBIR reconstruction on ultrasound data by proposing a spatially varying prior-model to account for artifacts from deeper regions and a 3D regularizer to account for correlations between scans from adjacent regions. We demonstrate that the use of the new prior model in MBIR can significantly improve reconstructions compared to SAFT and the previously proposed MBIR technique.
Hani Almansouri, Singanallur Venkatakrishnan, Dwight Clayton, Yarom Polsky, Charles Bouman, Hector Santos-Villalobos, "Ultrasonic Model-Based Iterative Reconstruction with Spatially Variant Regularization for One-Sided Non-Destructive Evaluation" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XVI, 2018, pp 103-1 - 1036, https://doi.org/10.2352/ISSN.2470-1173.2018.15.COIMG-103