目的：评价单能量去金属伪影技术（SEMAR）对颅内动脉瘤栓塞及夹闭术后金属伪影和灌注参数的影响。方法：55例颅内动脉瘤栓塞或夹闭术后患者，均行320排CTA+灌注一站式检查。扫描后首先用第三代适应性迭代降噪算法（AIDR 3D）单独重建成No-SEMAR组，再用SEMAR加AIDR 3D算法重建成SEMAR组。分别对比两组金属伪影周围的噪声、对比噪声比（CNR）、伪影指数（AI）、伪影的最大直径、图像质量主观评分和灌注参数。结果：与No-SEMAR组相比，SEMAR组噪声、伪影最大直径、AI明显减低（P<0.01），图像质量主观评分和CNR明显增高（P<0.01）。两组得到的灌注参数脑血流量（CBF）、脑血容量（CBV）、平均通过时间（MTT）线性相关性良好（P<0.01）。结论：SEMAR技术可以明显减少颅内动脉瘤金属植入物的伪影，能在不影响脑CT灌注参数的情况下，提高图像质量。
Abstract
Objective: To evaluate the effect of a single-energy metal artifact reduction(SEMAR) technique for metallic implantation materials artifacts reduction and perfusion parameters in brain imaging. Methods: Computed tomography angiography(CTA) and CT perfusion were performed on 55 patients with implanted metallic materials in the brain. All CT examinations were performed on a 320-row CT scanner. No-SEMAR images were reconstructed with iterative reconstruction alone, and SEMAR images were reconstructed with iterative reconstruction plus the SEMAR algorithm. Noise(N-measured on the image with the most pronounced artifact), contrast-to-noise ratio(CNR), artifact index(AI), maximum diameter of artifact, image quality score, and cerebral perfusion parameters were compared between the two groups. Two radiologists visually evaluated the metallic artifacts using a four-point scale and a double blind method. Results: Compared with No-SEMAR images, noise, artifact diameter, and artifact index were reduced significantly after images were reconstructed using the SEMAR algorithm(P<0.01). The subjective image quality score and contrast-to-noise ratio increased significantly(P<0.01). The perfusion parameters(blood flow, blood volume, and mean transit time) obtained between the No-SEMAR and SEMAR groups exhibited linear correlation(P<0.01). Conclusion: The SEMAR algorithm significantly reduces artifact from implanted metallic materials in the brain and can potentially increase CT image quality without influencing cerebral blood perfusion parameters.
关键词
颅内动脉瘤 /
体层摄影术 /
X线计算机
Key words
Intracranial aneurysm /
Tomography, X-ray computed
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基金
浙江省医药卫生科研项目（2018KY155）；宁波市科技局科技惠民项目（2016C51013）；宁波市自然科学基金项目（2016A610140）；宁波市医疗卫生品牌学科项目（PPXK2018-04）。
