Dr. Mohamed E. Eldib Profile

Dr. Mohamed E. Eldib

at Univ of California Irvine

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Ring artifact and non-uniformity correction method for improving XACT imaging

Mohamed Elsayed Eldib, Siqi Wang, Liangzhong Xiang

Proceedings Volume 11595, 1159552 (2021) https://doi.org/10.1117/12.2580848

KEYWORDS: Nonuniformity corrections, Ultrasonography, Signal to noise ratio, Sensors, Acoustics, X-ray imaging, Transducers, Spatial resolution, Quantitative analysis

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X-ray induced acoustic computed tomography (XACT) as a novel imaging modality has shown great potential in applications ranging from biomedical imaging to nondestructive testing. Improve the signal to noise ratio and removing the artifacts are major challenges in XACT imaging. We introduce an efficient non-uniformity correction method for the ultrasound ring transducer. Non-uniformity appears as ring artifacts in the reconstructed image when all sensor elements have a simultaneous time-variant response during the acquisition. Also, non-uniformity causes a contrast change effect in the reconstructed image when some defective sensor elements have a different response than the neighbors over the whole scan time. These two types of non-uniformity appear as horizontal or vertical strip patterns in sinogram domain based on the reason. The proposed method is a sinogram-based-algorithm, which is based on estimating a correction vector and localizing the abnormalities to compensate for the non-uniformity response. We applied the proposed algorithm on simulation data. The results have shown that the proposed method can greatly reduce the ring artifacts and also correct the distortion comes from sensor elements non-uniform response. Despite the great reduction of artifacts, the proposed method does not compromise the original spatial resolution and contrast after using the interpolation. The quantitative analysis has shown a great improvement in the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the normalized absolute error (NAE) has been reduced by 77–80 %.

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