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作 者:房加高[1] 邹月芬[1] 徐海[1] 张怡[1] 曹达 李鸿渊 赵如盛 刘春光 盛志洪 FANG Jia-gao;ZOU Yue-fen;XU Hai;ZHANG Yi;CAO Da;LI Hong-yuan;ZHAO Ru-sheng;LIU Chun-guang;SHENG Zhi-hong(Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, CHINA;Philips Healthcare, Nanjing 210005, Jiangsu, CHINA)
机构地区:[1]南京医科大学第一附属医院放射科,江苏南京210029 [2]飞利浦医疗器械公司,江苏南京210005
出 处:《海南医学》2018年第10期1408-1410,共3页Hainan Medical Journal
摘 要:目的探讨飞利浦去金属伪影算法(O-MAR)在去除腰椎内固定术后金属植入物伪影中的价值。方法收集2017年1~7月在南京医科大学第一附属医院实施腰椎内固定术的30例患者。行腰椎CT扫描后,使用常规迭代算法(i Dose4)和去金属伪影迭代算法(O-MAR)分别对扫描后数据重建。对比两组重建图像上金属伪影面积、噪声值、信噪比、图像质量等参数,观察O-MAR去除伪影的效果。结果 O-MAR在轴位、矢状位和冠状位上金属伪影面积分别比i Dose4减少91.9%、87.3%和88.9%,差异均具有显著统计学意义(P<0.01);两组重建算法的背景噪声值差异无统计学意义,但是O-MAR信噪比和图像质量评分均明显好于i Dose4,差异具有统计学意义(P<0.05)。结论使用飞利浦O-MAR技术可以有效去除腰椎金属植入物伪影,虽然无法抑制背景噪声,但可以提高图像信噪比,改善图像质量。Objective To explore the value of Philips O-MAR(metal artifacts reduction) iterative algorithm in CT metal artifacts reduction of implants after lumbar internal fixation. Methods A total of 30 patients undergoing lumbar vertebrate internal fixation in the First Affiliated Hospital of Nanjing Medical University between January 2017 and July 2017 were enrolled. After the lumbar CT scanning, the post-scan data were reconstructed by two iterative algorithm methods—i Dose4 and O-MAR. Four parameters including the area of metal artifacts, noise level, signal-to-noise ratio(SNR) and image quality were compared between the two algorithms. Results The area of metal artifacts on images of O-MAR reduced 91.9%, 87.3%, 88.9% respectively on axial, sagittal and coronal dimension compared with i Dose4,which were statistically different(P〈0.01). Although there were no significant difference between the two algorithms in background noise level(P〈0.05), the SNR and image quality improved significantly on O-MAR than on i Dose4(P〈0.05). Conclusion Philips O-MAR iterative algorithm can effectively remove artifacts of the lumbar metal implant. Although O-MAR could not restrain the background noise, it can be used for improving the SNR and image quality.
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