Department of Electrical Engineering, Yale University, New Haven, Connecticut 06520, USA.
Med Phys. 2007 Jul;34(7):3005-17. doi: 10.1118/1.2745235.
In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were Theta(x):0.18(0.19) degrees, Theta(y):0.04(0.04) degrees, Theta(z):0.04(0.02) degrees, t(x):0.14(0.15) mm, t(y):0.09(0.05) mm, and t(z):0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.
在许多放射治疗诊所中，通过将患者在计划 3D CT 中的骨性解剖结构与每次治疗前采集的 2D 门户图像中的相应骨性解剖结构对齐，可以降低 3D 适形放射治疗和强度调制放射治疗的几何不确定性。本文旨在确定门户图像和计划 CT 的数字重建射线照片（DRR）中是否存在一个频带，其中骨性解剖结构比非骨性解剖结构占主导地位，以便门户图像和 DRR 可以进行适当的滤波，从而在自动化的 2D-3D 单门户基于强度的配准框架中实现高精度的配准。在仔细收集的金标准数据上测试了两种相似性度量，即互信息和 Pearson 相关系数，这些数据由在临床条件下使用千伏锥形束 CT（CBCT）和千伏门户图像在前后位（AP）视图中采集的一个人体模型获得，已知姿势，并在患者数据上进行了测试。结果表明，在配准过程中对门户图像和 DRR 进行滤波可以显著提高配准性能。没有滤波时，配准并不总是收敛，而有滤波时，配准总是收敛到准确的解。对于使用相关系数在人体模型上进行的位姿确定实验，恢复每个变换参数的绝对误差的平均值（和标准差）分别为：Theta(x)：0.18(0.19)度，Theta(y)：0.04(0.04)度，Theta(z)：0.04(0.02)度，t(x)：0.14(0.15)毫米，t(y)：0.09(0.05)毫米，t(z)：0.49(0.40)毫米。基于互信息的配准也得到了类似的小误差。对于患者数据，对叠加的配准图像进行目视检查表明，在所有情况下它们都正确对齐。本文提出的结果表明，通过在图像中关注刚性骨性结构，同时减少与软组织频率相似的伪影的影响，基于强度的方法可以实现稳健且准确的配准。
