OBJECTIVE:To reduce metal artifacts generated using current interpolation-based method on X-ray computed tomography (CT) images, this study proposes and tests a new Poisson fusion sinogram based metal artifact reduction (FS-MAR) method. METHODS:The proposed FS-MAR method consists of (1) generatingthe prior image, (2) forward projecting this prior image and applying the Poisson blending technique to seamlessly replace the metal-affected sinogram of the original projection in the metal projection region (MPR) by the prior image projection to get the corrected metal-free sinogram, and (3) performing the filtered back projection (FBP) on the corrected sinogram and filling the metal image back to the metal-free corrected image to get the final artifact reduced image. Simulated images are calculated by taking clinical metal-free CT images as phantoms and inserting metals during the simulated projection process to get the corresponding metal-affected images by the FBP. After the simulated images are processed by the proposed MAR method, two metrics structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) are used to evaluate image quality. Finally, visual evaluation is also performed using several real clinical metal-affected images obtained from the Revision Radiology group. RESULTS:In two testing samples, using FS-MAR method yields the highest SSIM and PSNR of 0.8912 and 30.6693, respectively. Visual evaluation results on both simulated and clinical images also show that using FS-MAR method generates less image artifacts than using the interpolation-based algorithm. CONCLUSIONS:This study demonstrated that with the same prior image, applying the proposed Poisson FS-MAR method can achieve the higher image quality than using the interpolation-based algorithm.

中文翻译：

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基于泊松融合正弦图的后处理方法在CT中减少金属伪影方案

目的：为了减少使用当前基于插值的方法在 X 射线计算机断层扫描 (CT) 图像上产生的金属伪影，本研究提出并测试了一种新的基于泊松融合正弦图的金属伪影减少 (FS-MAR) 方法。方法：所提出的FS-MAR方法包括（1）生成先验图像，（2）前向投影该先验图像并应用泊松混合技术在金属投影区域（MPR）中无缝替换原始投影的金属影响正弦图) 通过先验图像投影得到校正后的无金属正弦图，以及 (3) 对校正后的正弦图进行滤波反投影 (FBP) 并将金属图像填充回无金属校正后的图像，以减少最终的伪影图片。模拟图像是以临床无金属CT图像为体模，在模拟投影过程中插入金属，通过FBP得到相应的金属影响图像。通过所提出的 MAR 方法处理模拟图像后，使用两个度量结构相似性指数 (SSIM) 和峰值信噪比 (PSNR) 来评估图像质量。最后，还使用从 Revision Radiology 小组获得的几个真实的临床金属影响图像进行视觉评估。结果：在两个测试样本中，使用 FS-MAR 方法产生的最高 SSIM 和 PSNR 分别为 0.8912 和 30.6693。模拟图像和临床图像的视觉评估结果还表明，使用 FS-MAR 方法比使用基于插值的算法产生更少的图像伪影。结论：