Abstract:
Background High-resolution industrial computed tomography (CT) is crucial for the non-destructive testing (NDT) of critical components, particularly in the aerospace industry where high-density materials are common. The Rhodotron accelerator, with its micro-focus capability, offers a hardware advantage for achieving high spatial resolution over traditional linear accelerators. However, its potential is severely hampered when inspecting large, high-density workpieces. The strong X-ray attenuation leads to projection data with a very low signal-to-noise ratio (SNR), causing conventional reconstruction algorithms to either produce noisy images or oversmooth critical details, thereby limiting the system’s effective resolution.
Purpose This study aims to develop and validate a reconstruction algorithm capable of overcoming the low-SNR challenge inherent in Rhodotron CT scans of high-density objects. The primary objective is to achieve high-resolution, high-fidelity image reconstruction that effectively suppresses noise while preserving the fine structural edges essential for accurate defect detection.
Methods A novel iterative algorithm, termed Projection Onto Convex Sets regularized by Bilateral Total Variation (POCS-BTV), is proposed. The algorithm integrates BTV, a regularizer known for its superior edge-preservation properties, into the POCS framework to constrain the solution during iterations. The performance of POCS-BTV was evaluated against the Simultaneous Iterative Reconstruction Technique (SIRT), POCS-TV, and POCS-RTV algorithms. The evaluation involved two stages: a simulation experiment using a Shepp-Logan phantom with added Poisson-Gaussian noise to mimic low-SNR conditions, and a physical experiment using a 70 mm diameter high-strength steel wire rope phantom scanned by a 9 MeV Rhodotron accelerator CT system.
Results In the simulation experiment, the POCS-BTV algorithm demonstrated superior quantitative performance, achieving a Peak Signal-to-Noise Ratio (PSNR) of 30.76 and a Structural Similarity Index (SSIM) of 0.8405, which were significantly better than the comparison algorithms. In the real data experiment, visual analysis of the reconstructed images showed that POCS-BTV successfully resolved the fine gaps between individual steel wires. This was in stark contrast to other methods, which suffered from structural aliasing and blurred edges due to noise.
Conclusions The POCS-BTV algorithm effectively unlocks the high-resolution potential of the Rhodotron accelerator hardware, even in challenging low-SNR scenarios. By achieving an optimal balance between noise suppression and detail preservation, it provides a robust and reliable solution for the precision NDT of critical high-density industrial components, demonstrating significant value for practical engineering applications.
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