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作 者:黄钟杰[1] 刘源[1] 肖芝豹[1] 曹楚瑜[1] 陈健伟[1]
机构地区:[1]汕头大学医学院第一附属医院放射科
出 处:《中国医学计算机成像杂志》2013年第1期79-83,共5页Chinese Computed Medical Imaging
基 金:2012年度汕头市医疗重点科技计划项目资助(汕府科2012[113])~~
摘 要:目的:探讨宝石CT能谱成像(GSI)在减除脊柱金属植入物伪影的应用价值;同时研究应用能谱曲线寻找最佳keV值的可行性。方法:对6例脊柱金属植入术患者行宝石CTGSI扫描,并以10keV为间隔重建11组单能量(Mono)图像质量的客观指标。通过比较140kVp(QC)和各级别Mono图像的客观指标伪影指数(AI)和对比噪声比(CNR)、软组织和骨质以及金属植入物图像质量的主观评分(4分法,1分图像最优,4分图像最差),寻找GSI去金属伪影最佳成像条件。并以最佳图像为对照,比较利用能谱曲线寻得最佳Mono图像的质量和诊断效能。结果:AI在90-140keV时减低,在100-130keV达最低;CNR在100~120keV时减低且最低。能谱成像技术在90-140keV对软组织、骨质及金属植入物诊断效能提高,在110-130keV对软组织观察最优,在110-140keV对骨质及植入金属观察最优。利用能谱曲线寻得最佳单能量成像条件为100-120keV,范围落在普通方法确定的电压值区间内,其图像均能满足诊断。结论:宝石能谱CTGSI在高电压区可有效减除脊柱金属植入物伪影,最佳成像区间为100-120keV。利用能谱曲线可快速有效获得最佳单能量成像。Purpose: To assess the clinical application value of metallic artifacts reduction with single source dual energy CT gemstone spectra imaging (GSI) in patients with spine metal implanted. And to study the feasibility of finding optimal keV by GSI. Methods: Six patients with spine metal implanted were underwent single source dual energy CT scan. The 140kVp (QC) images and 11 groups monochromic (Mono) image with an interval of 10 keV were obtained. The quality and diagnosis utility of QC and Mono images were assessed by comparing artifacts index(AI), contrast-to-noise ratio (CNR) and subjective scores (4 grades, 1 represent the best, 4 represent the worst) including soft tissue, bone and metal. To analyze the utility of finding optimal range of keV by spetral HU curve. ResuLts: AI decreased in 90-140keV, and the optimal range was 100-130keV. CNR decreased in 100-120keV, and the optimal range was 100-120keV. The utility of diagnosis of soft tissue, bone and metal improved in 90-140keV, and the optimal range was 110- 130keV. There was no difference to search optimal keV between spectral HU curve and ordinary method. Conclusions: Single source dual energy CT GSI contributes to metallic artifacts reduction at high keV. The optimal range of keV was 100keV-120keV. It was a valid and recommended method to abtain optimal keVwith GSI curve.
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