| 王公堂,谷文静,孙倩,虞刚,孙腾,李登旺.基于自适应非局部均值的CBCT投影数据去噪算法[J].光电子激光,2015,26(6):1226~1232 |
| 基于自适应非局部均值的CBCT投影数据去噪算法 |
| CBCT projection data denoising algorithm based on adaptive nonlocal means |
| 投稿时间：2015-02-10 |
| DOI： |
| 中文关键词: 锥形束计算层析(CBCT)图像去噪 非局部均值(NLM)算法 自适应性 噪声估计 |
| 英文关键词:cone-beam computed tomography (CBCT) image denoising nonlocal means (NLM ) algorithm adaptivity noise estimation |
| 基金项目:国家自然科学基金(61201441,61471226)、山东省优秀中青年科学家科研奖励基金(BS2012D X038)、山东省高等学校科研计划(J12LN23)、济南青年科技明星计划(20120109)和济南市高校自主创新(201401221)资助项目 (1.山东师范大学 物理与电子科学学院,山东 济南 250014； 2.山东大学 第二医院 肿瘤 防治中心,山东 济南 250033) |
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| 中文摘要: |
| 为了准确地获取放疗摆位信息,并减低临床患者 所接收的辐射剂量,提出了一种基于非局部均值(NLM)的锥形束计算层析(CBCT)投影数据去 噪算法。首先,计算在不同投射角度下获取的CBCT投影数据的噪声标准差、边缘信息和纹理 子块的平均梯度值的均值,确 定与该角度投影数据相适应的滤波强度值；然后,采用改进的NLM算法对投影数 据进行去噪处理；最后,经过三维重 建获得较高质量的CBCT图像。还对基于子块分割的噪声估计算法进行了改进,使其更适 用于CBCT投影数据的噪声估 计。实验结果表明,本文算法能够有效估计投影数据的噪声水平,去噪效果优于其它几种算 法,在去除噪声的同时,还能很 好地保留图像的细节信息,并可增强图像的对比度,有利于准确获取摆位信息和医生的临 床诊断。 |
| 英文摘要: |
| In order to get more accurate placement information of radiation thera py and reduce the radiation dose received by clinical patients in the hospital,a new denoising algorithm based on the noi ses in the cone-beam computed tomography (CBCT) projection data is propo sed in this paper.Firstly,the appropriate value of the filter strength is determin ed by the noise standard deviation,the edge information and the average of the average gradient values of texture sub-block s which are calculated in different angles of the projection data received before the reconstruction of the 3D CBCT images,th en the projection data are denoised by the adaptive nonlocal means (NLM) algorithm and lastly,the denoised projection data are reconstructed to obtain high quality CBCT images.In addition,a noise estimation algorithm is improved in order to make i t more suitable for the CBCT projection data.The experimental results show that the proposed algorithm can estimate the noise level of the projection data,the new algorithm is also superior to other methods,it can reduce the noises,keep the details and enhance the contrast of the images effectively,so the proposed algorithm is beneficial to acquiring the accurate placement information and diagnosing in the clinical practicefor the do ctor. |
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