摘要
建立了一套针对由金属伪影造成的CT图像质量退化的恢复算法。利用Non-Local前置滤波(Non-Local Pre-filter,NL-PF)对原始CT图像进行全局滤波,从而有效地滤除原始图像中的噪声并对射线状金属伪影进行了平滑,其后配合最大互信息量分割算法(Mutual Information Maximized Segmentation,MIMS)从图像中分割出伪影成份,并利用其周围非伪影部分的像素对伪影类像素进行插值处理得到一个称之为"伪组织"类的图像。最后,通过融合"伪组织"图像的sinogram和原始CT图像的sinogram,得到校正的sinogram并采用滤波反投影重建算法完成金属伪影的CT校正图像。利用所提出的方法可以对含有金属伪影的CT图像进行有效伪影消除,其中射线状伪影消除效果显著。另外,此方法还可以锐化器官轮廓,避免了临床上由于金属伪影导致的放射治疗效果下降。实验表明,金属伪影消除算法可以有效地消除高密度物体造成的金属伪影,从而提高临床诊断和治疗的效果提供技术支持。
To develop a corrective method in which the distorted segments in sinogram are identified and interpolated using non distorted neighbor projections,to reduce distorted tomography metal artifacts caused by high-density objects.First,the Non-Local Pre-Filter （NL-PF） reduces the noise content and smoothes streak artifacts in CT image.Next,the filtered image is segmented into several regions by Mutual Information Maximized Segmentation （MIMS）.Then the artifacts class is converted to the CT number with the surrounding material,called ＂artifact-tissue＂ class,and after that an ＂artifact-tissue sinogram＂ is produced using forward projection method.A final image is reconstructed by the filtered back-projection from appropriately combination of original sinogram and artifacts-tissue sinogram.Phantoms studies show that metal artifacts in CT image can be eliminated effectively.Furthermore,this proposed method improves the ability of organ contours detection.And,this feature can be applied to improve the per- formance of radiation therapy.These studies demonstrate that the proposed method can effectively reduce CT metal artifacts caused by high-density.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第24期168-171,共4页
Computer Engineering and Applications
基金
国家重点基础研究发展规划(973)No.2003CB716101~~
