21 July 2017 Dynamic re-weighted total variation technique and statistic Iterative reconstruction method for x-ray CT metal artifact reduction
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042043 (2017) https://doi.org/10.1117/12.2281570
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
Abstract
Over the years, the X-ray computed tomography (CT) has been successfully used in clinical diagnosis. However, when the body of the patient to be examined contains metal objects, the image reconstructed would be polluted by severe metal artifacts, which affect the doctor’s diagnosis of disease. In this work, we proposed a dynamic re-weighted total variation (DRWTV) technique combined with the statistic iterative reconstruction (SIR) method to reduce the artifacts. The DRWTV method is based on the total variation (TV) and re-weighted total variation (RWTV) techniques, but it provides a sparser representation than TV and protects the tissue details better than RWTV. Besides, the DRWTV can suppress the artifacts and noise, and the SIR convergence speed is also accelerated. The performance of the algorithm is tested on both simulated phantom dataset and clinical dataset, which are the teeth phantom with two metal implants and the skull with three metal implants, respectively. The proposed algorithm (SIR-DRWTV) is compared with two traditional iterative algorithms, which are SIR and SIR constrained by RWTV regulation (SIR-RWTV). The results show that the proposed algorithm has the best performance in reducing metal artifacts and protecting tissue details.
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Chengtao Peng, Bensheng Qiu, Cheng Zhang, Changyu Ma, Gang Yuan, Ming Li, "Dynamic re-weighted total variation technique and statistic Iterative reconstruction method for x-ray CT metal artifact reduction", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042043 (21 July 2017); doi: 10.1117/12.2281570; https://doi.org/10.1117/12.2281570
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