Computed tomography (CT) technology encounters significant limitations in performing high-precision non-destructive testing (NDT) of plate-like objects with high aspect ratios due to system structural constraints and X-ray energy limitations. Rotational computed laminography (RCL) technology enables high-resolution three-dimensional (3D) imaging of plate-like objects through adjustment of the angle between the rotation axis and X-ray beam centerline (less than 90°). However, incomplete projection data leads to aliasing artifacts and loss of edge details in reconstructed images, particularly when examining plate-like structures. These limitations compromise image quality and impair the detailed representation of target features. Conventional iterative reconstruction algorithms utilizing single a priori information, fixed a priori informations, or complex registration processes demonstrate limitations including inadequate artifact suppression, excessive edge smoothing, and high noise sensitivity. To address these challenges, this paper introduces an iterative reconstruction algorithm based on dynamically updated hybrid a priori constraints (DUHP), incorporating a dynamically updated structural self-prior (DUSSP) and a truncated adaptively weighted total variation (TAwTV) regularization term based on gradient sparsity a priori information. The proposed methodology effectively suppresses aliasing artifacts while enhancing edge detail preservation, resulting in superior image reconstruction quality.

The proposed DUHP algorithm achieves high-quality 3D reconstruction of plate-like objects by implementing a DUSSP regularization term and combining it with the TAwTV based on gradient sparsity a priori information. Initially, the algorithm extracts and updates the mask image of the target region from previous reconstruction results at a fixed frequency to establish a dynamically updated structural self-prior. This approach enhances global structural information preservation, increases the adaptability of the structural a priori information during reconstruction, and prevents error accumulation associated with fixed a priori informations. Subsequently, the TAwTV constraint adaptively optimizes local gradients, reducing the excessive smoothing effect typical of conventional TV constraints while improving edge detail reconstruction quality. The complementary interaction between these regularization terms enables DUHP to enhance aliasing artifact suppression while improving both local and global structural feature restoration in reconstructed images, ultimately enhancing visual quality and quantitative evaluation metrics. To validate the algorithm’s effectiveness, two representative circuit board models are designed for simulation experiments to assess DUHP algorithm robustness under varying structural complexities and noise conditions. The experiments examine two circuit board models: one featuring through-holes and fracture defects (model 1) and another with a more complex circuit structure (model 2). The adaptability of DUHP under different noise levels is evaluated and compared qualitatively and quantitatively against SART-TV, SART-TAwTV, and SPI-TV. Additionally, real data from RCL-scanned decoder module circuit boards are utilized in practical reconstruction experiments to evaluate the DUHP algorithm’s feasibility in real-world engineering applications.

The experimental results demonstrate that the DUHP algorithm effectively reduces aliasing artifacts and significantly improves edge detail restoration in cases of incomplete projection data. In the model 1 experiment, the DUHP algorithm produced reconstructed images with sharper edge features compared to SIRT, SART-TV, SART-TAwTV, and SPI-TV, effectively restoring internal defect structure and location (Fig. 5). In the model 2 experiment, the DUHP algorithm successfully suppressed noise while accurately recovering fine structures in high-contrast regions. The algorithm improved inter-layer consistency of the reconstructed image and maintained superior depth resolution along the xoz direction [Fig. 11(b)], enhancing the resolution of the circuit board’s complex multilayer structure (Fig. 9). The RMSE convergence curves and SSP from simulation experiments with model 1 and model 2 (Figs. 8 and 11) demonstrate the DUHP algorithm’s effectiveness in suppressing aliasing artifacts while preserving edge details, improving inter-layer consistency, and maintaining higher depth resolution along the xoz direction. The quantitative evaluation metrics RMSE, PSNR, and SSIM further confirm the reconstruction advantages of the DUHP algorithm under varying noise conditions and structural complexities (Tables 2 and 3). In the real-data experiment with the decoder module circuit board, the DUHP algorithm maintained overall structural integrity while substantially reducing artifacts in soldering areas, enhancing reconstructed result visualization quality (Fig. 13). The comparison of reconstruction times (Table 5) indicates that the DUHP algorithm achieves an optimal balance between computational efficiency and reconstruction quality. Experimental results confirm DUHP’s suitability for nondestructive testing of planar objects, effectively suppressing aliasing artifacts while preserving high-frequency edge information and maintaining high reconstruction quality across various structural complexities and noise environments.

This paper introduces an iterative reconstruction algorithm utilizing DUHP to enhance the suppression of aliasing artifacts in RCL image reconstruction while preserving high-frequency edge information, thus improving overall image quality. The algorithm’s primary innovation lies in developing a dynamic updating mechanism of DUSSP regular term combined with the adaptive gradient truncation strategy of TAwTV regular term. This approach progressively extracts and optimizes structural a priori information through iteration without requiring complex image alignment processes, while effectively preventing the accumulation of a priori errors associated with traditional parsing algorithms that extract fixed a priori information. Additionally, the dynamic updating mechanism enables DUHP to continuously refine a priori information quality during iterations, thereby enhancing final reconstruction quality and robustness. In comparison to conventional methods including SIRT, SART-TV, SART-TAwTV, and SPI-TV, DUHP demonstrates enhanced adaptability to complex structures through its dynamic updating of structural self-prior information, resulting in superior performance in both visual quality and quantitative evaluation metrics. Future research directions may explore additional adaptive parameter optimization strategies to enhance the stability and applicability of DUHP across various imaging conditions.