Enhanced Limited-Angle CT Imaging for Defect Detection in Wind Turbine Blade Trailing-Edge Bonding Regions

Radiographic imaging is a crucial nondestructive testing (NDT) technique widely studied for wind turbine blade inspection. However, practical inspections are often constrained by equipment layout, blade geometry, and environmental factors, limiting the acquisition of projection data to finite angles, which renders traditional full-angle imaging methods impractical. Due to incomplete data collection, reconstructed images frequently exhibit artifacts, blurring, and loss of edge information, which impacts precise defect detection and quantitative analysis. Developing limited-angle imaging algorithms suitable for the rear-edge core joint area of wind turbine blades has therefore become a critical topic in this field. This study proposes a novel limited-angle imaging algorithm, termed SART-TV-DMPI (simultaneous algebraic reconstruction technique with total variation regularization and dynamic multi-threshold prior information). The method integrates dynamic multi-threshold segmentation, adaptive total variation regularization, and region-specific prior knowledge to enhance image quality and defect detectability under limited-angle conditions.