Journal of X-Ray Science and Technology - Volume 32, issue 6

Authors: Deng, Zihan | Wang, Zhisheng | Lin, Legeng | Jiang, Demin | Cui, Junning | Wang, Shunli

Article Type: Research Article

Abstract: BACKGROUND: Recent studies have explored layered correction strategies, employing a slice-by-slice approach to mitigate the prominent limited-view artifacts present in reconstructed images from high-pitch helical CT scans. However, challenges persist in determining the angles, quantity, and sequencing of slices. OBJECTIVE: This study aims to explore the optimal slicing method for high pitch helical scanning 3D reconstruction. We investigate the impact of slicing angle, quantity, order, and model on correction effectiveness, aiming to offer valuable insights for the clinical application of deep learning methods. METHODS: In this study, we constructed and developed a series of data-driven slice …correction strategies for 3D high pitch helical CT images using slice theory, and conducted extensive experiments by adjusting the order, increasing the number, and replacing the model. RESULTS: The experimental results indicate that indiscriminately augmenting the number of correction directions does not significantly enhance the quality of 3D reconstruction. Instead, optimal reconstruction outcomes are attained by aligning the final corrected slice direction with the observation direction. CONCLUSIONS: The data-driven slicing correction strategy can effectively solve the problem of artifacts in high pitch helical scanning. Increasing the number of slices does not significantly improve the quality of the reconstruction results, ensuring that the final correction angle is consistent with the observation angle to achieve the best reconstruction quality. Show more

Keywords: Computer tomography, 3D medical image restoration, slice correction strategy, multi-view slicing, experimental verification, deep learning

DOI: 10.3233/XST-240128

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 6, pp. 1535-1552, 2024

Authors: Fahad, Muhammad | Zhang, Tao | khan, Sajid Ullah | Albanyan, Abdullah | Siddiqui, Fazeela | Iqbal, Yasir | Zhao, Xin | Geng, Yanzhang

Article Type: Research Article

Abstract: BACKGROUND: Airport security is still a main concern for assuring passenger safety and stopping illegal activity. Dual-energy X-ray Imaging (DEXI) is one of the most important technologies for detecting hidden items in passenger luggage. However, noise in DEXI images, arising from various sources such as electronic interference and fluctuations in X-ray intensity, can compromise the effectiveness of object identification. OBJECTIVE: To address the challenge of noise interference in DEXI, this study aims to develop and validate a robust denoising technique using the Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT). METHODS: The proposed method targets …and removes background and Poisson noise in DEXI images, improving object recognition accuracy. During the denoising process, images are decomposed into several subbands, and thresholding techniques are applied to minimize noise while preserving important information. The images are then reconstructed to provide a cleaner and more accurate depiction of scanned objects. RESULTS: Experimental results demonstrate the effectiveness of the DWT and SWT-based denoising strategy in preserving critical data while suppressing noise in DEXI. The performance of the denoising technique is quantified using Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE). The proposed system achieved an average PSNR of 35.23 and an MSE of 19.52 for 256×256 DEXI images, and an average PSNR of 36.01 and an MSE of 16.29 for 512×512 DEXI images. CONCLUSION: The results highlight the achievement of the proposed approach in enhancing the quality of DEXI for improved security screening, demonstrating its potential application in airport security systems. Show more

Keywords: Discrete wavelet transform, stationary wavelet transform, dual energy X-ray imaging, background noise, image fusion, Poisson noise

DOI: 10.3233/XST-240227

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 6, pp. 1553-1570, 2024