Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT
Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT
复制标题
DOI：
10.1002/mp.12190
复制
发表时间：
2017-05-01
期刊：
影响因子：
3.8
通讯作者：
Zhu, Lei
中科院分区：
文献类型：
--
作者：
Shi, Linxi;Tsui, Tiffany;Zhu, Lei
目的：锥形束计算机断层扫描（CBCT）的图像质量受到严重阴影伪影的限制，阻碍了其在放射治疗中的定量应用。在这项工作中，我们提出了一种利用规划CT （pCT）作为先验信息的图像域阴影校正方法，该方法对临床环境具有高度的适应性。方法：我们提出在pCT上通过稀疏采样进行阴影校正，该方法首先将瓦里安TrueBeam系统获得的首遍CBCT图像与pCT进行粗映射，瓦里安商业软件中嵌入的散射校正方法消除了一些图像误差，但CBCT图像仍然存在严重的阴影伪影。将映射的pCT和CBCT之间的差异图像视为遮阳误差，但为了避免携带pCT的虚假信息，只选择稀疏的遮阳样本进行经验约束校正，提出了一种基于傅里叶变换的局部滤波技术，以有效地处理稀疏数据，实现有效的遮阳校正。我们对一名拟人化骨盆幻像患者和17名计划接受放射治疗的患者进行了评估。（本文方法的代码和样本数据可从https://sites.google.com/view/linxicbct）Results下载：本文提出的阴影校正方法使幻体和患者的CBCT图像质量都得到了显著提高，接近于pCT图像的水平。在模体上，CBCT和pCT的空间不均匀性（SNU）差从74 HU减小到1 HU。在骨盆患者中，CBCT与pCT的SNU均方根差从83 HU降至10 HU，在胸腔患者中从101 HU降至12 HU。通过模拟CBCT和pCT对幻影的配准误差和对患者的错误配准，充分研究了所提出的阴影校正的鲁棒性。我们方法的稀疏采样方案成功地避免了校正后的CBCT中的错误结构，即使最大配准误差高达8毫米。结论：我们开发了一种有效的CBCT阴影校正算法，可以作为软件插件在临床数据上实现，而无需修改当前的成像硬件和协议。该算法直接应用于商用CBCT扫描仪的输出图像，计算效率高，内存负担小。(C) 2017年美国医学物理学家协会
Purpose: The image quality of cone beam computed tomography (CBCT) is limited by severe shading artifacts, hindering its quantitative applications in radiation therapy. In this work, we propose an image-domain shading correction method using planning CT (pCT) as prior information which is highly adaptive to clinical environment.Method: We propose to perform shading correction via sparse sampling on pCT. The method starts with a coarse mapping between the first-pass CBCT images obtained from the Varian TrueBeam system and the pCT. The scatter correction method embedded in the Varian commercial software removes some image errors but the CBCT images still contain severe shading artifacts. The difference images between the mapped pCT and the CBCT are considered as shading errors, but only sparse shading samples are selected for correction using empirical constraints to avoid carrying over false information from pCT. A Fourier-Transform-based technique, referred to as local filtration, is proposed to efficiently process the sparse data for effective shading correction. The performance of the proposed method is evaluated on one anthropomorphic pelvis phantom and 17 patients, who were scheduled for radiation therapy. (The codes of the proposed method and sample data can be downloaded from https://sites.google.com/view/linxicbct)Results: The proposed shading correction substantially improves the CBCT image quality on both the phantom and the patients to a level close to that of the pCT images. On the phantom, the spatial nonuniformity (SNU) difference between CBCT and pCT is reduced from 74 to 1 HU. The root of mean square difference of SNU between CBCT and pCT is reduced from 83 to 10 HU on the pelvis patients, and from 101 to 12 HU on the thorax patients. The robustness of the proposed shading correction is fully investigated with simulated registration errors between CBCT and pCT on the phantom and mis-registration on patients. The sparse sampling scheme of our method successfully avoids false structures in the corrected CBCT even when the maximum registration error is as high as 8 mm.Conclusion: We develop an effective shading correction algorithm for CBCT readily implementable on clinical data as a software plug-in without modifications of current imaging hardware and protocol. The algorithm is directly applied on the output images from a commercial CBCT scanner with high computational efficiency and negligible memory burden. (C) 2017 American Association of Physicists in Medicine