Dang Jun, You Tao, Sun Wenzheng, Xiao Hanguan, Li Longhao, Chen Xiaopin, Dai Chunhua, Li Ying, Song Yanbo, Zhang Tao, Chen Deyu
Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Department of Radiation Oncology, The Affiliated Hospital of Jiangsu University, Zhenjiang, China.
Front Oncol. 2021 Jan 18;10:568627. doi: 10.3389/fonc.2020.568627. eCollection 2020.
To incorporate the bilateral filtering into the Deformable Vector Field (DVF) based 4D-CBCT reconstruction for realizing a fully automatic sliding motion compensated 4D-CBCT.
Initially, a motion compensated simultaneous algebraic reconstruction technique (mSART) is used to generate a high quality reference phase (e.g. 0% phase) by using all phase projections together with the initial 4D-DVFs. The initial 4D-DVF were generated Demons registration between 0% phase and each other phase image. The 4D-DVF will then kept updating by matching the forward projection of the deformed high quality 0% phase with the measured projection of the target phase. The loss function during this optimization contains an projection intensity difference matching criterion plus a DVF smoothing constrain term. We introduce a bilateral filtering kernel into the DVF constrain term to estimate the sliding motion automatically. The bilateral filtering kernel contains three sub-kernels: 1) an spatial domain Guassian kernel; 2) an image intensity domain Guassian kernel; and 3) a DVF domain Guassian kernel. By choosing suitable kernel variances, the sliding motion can be extracted. A non-linear conjugate gradient optimizer was used. We validated the algorithm on a non-uniform rotational B-spline based cardiac-torso (NCAT) phantom and four anonymous patient data. For quantification, we used: 1) the Root-Mean-Square-Error (RMSE) together with the Maximum-Error (MaxE); 2) the Dice coefficient of the extracted lung contour from the final reconstructed images and 3) the relative reconstruction error (RE) to evaluate the algorithm's performance.
For NCAT phantom, the motion trajectory's RMSE/MaxE are 0.796/1.02 mm for bilateral filtering reconstruction; and 2.704/4.08 mm for original reconstruction. For patient pilot study, the 4D-Dice coefficient obtained with bilateral filtering are consistently higher than that without bilateral filtering. Meantime several image content such as the rib position, the heart edge definition, the fibrous structures all has been better corrected with bilateral filtering.
We developed a bilateral filtering based fully automatic sliding motion compensated 4D-CBCT scheme. Both digital phantom and initial patient pilot studies confirmed the improved motion estimation and image reconstruction ability. It can be used as a 4D-CBCT image guidance tool for lung SBRT treatment.
将双边滤波纳入基于可变形矢量场（DVF）的4D-CBCT重建中，以实现全自动的滑动运动补偿4D-CBCT。
首先，使用运动补偿同步代数重建技术（mSART），通过将所有相位投影与初始4D-DVF一起使用来生成高质量的参考相位（例如0%相位）。初始4D-DVF通过0%相位与其他各相位图像之间的Demons配准生成。然后，通过将变形后的高质量0%相位的前向投影与目标相位的测量投影进行匹配，不断更新4D-DVF。此优化过程中的损失函数包含投影强度差异匹配准则以及DVF平滑约束项。我们在DVF约束项中引入双边滤波核，以自动估计滑动运动。双边滤波核包含三个子核：1）空间域高斯核；2）图像强度域高斯核；3）DVF域高斯核。通过选择合适的核方差，可以提取滑动运动。使用了非线性共轭梯度优化器。我们在基于非均匀旋转B样条的心脏-躯干（NCAT）体模和四个匿名患者数据上验证了该算法。为了进行量化，我们使用：1）均方根误差（RMSE）和最大误差（MaxE）；2）从最终重建图像中提取的肺轮廓的骰子系数；3）相对重建误差（RE）来评估算法的性能。
对于NCAT体模，双边滤波重建的运动轨迹的RMSE/MaxE为0.796/1.02毫米；原始重建为2.704/4.08毫米。对于患者初步研究，双边滤波获得的4D-骰子系数始终高于无双边滤波的情况。同时，肋骨位置、心脏边缘清晰度、纤维结构等几个图像内容在双边滤波下都得到了更好的校正。
我们开发了一种基于双边滤波的全自动滑动运动补偿4D-CBCT方案。数字体模和初步患者研究均证实了其改进的运动估计和图像重建能力。它可作为肺部立体定向体部放疗（SBRT）治疗的4D-CBCT图像引导工具。
