4D Cone-beam CT reconstruction using a motion model based on principal component analysis.
4D Cone-beam CT reconstruction using a motion model based on principal component analysis.
复制标题
使用基于主成分分析的运动模型进行 4D 锥束 CT 重建。
DOI：
10.1118/1.3662895
复制
发表时间：
2011
期刊：
影响因子：
3.8
通讯作者：
Murphy,MartinJ
中科院分区：
文献类型：
--
作者：
Staub,David;Docef,Alen;Brock,RobertS;Vaman,Constantin;Murphy,MartinJ
PurposeTo提供一种新的4D锥形束CT（4DCBCT）重建算法的概念验证，并确定训练和优化算法的最佳方法。MethodsThe algorithm animates a patient fan‐beam CT（FBCT）with a patient specific parametric motion model，以便生成一个时间序列的变形CT（重建的4DCBCT），该时间序列通过体素尺度跟踪患者解剖结构在体素上的运动。通过要求通过变形的CT时间序列投射的投影与原始患者4DCBCT的投影匹配来约束运动模型。运动模型使用特征向量的基础，该特征向量是通过近似患者运动的位移向量场（DVF）的训练集的主成分分析（PCA）生成的。特征向量由4DCBCT期间记录的患者呼吸轨迹的参数化函数加权。该算法通过数值模拟进行了验证和测试。ResultsThe算法被证明可以为最复杂的模拟运动产生准确的重建结果，其中体素以伪周期模式移动，体素之间存在相对相移。测试结果表明，主成分特征向量训练DVF从一个新的2D/3D注册方法得到的DVF上的特征向量训练得到更好的结果比传统的注册4DCBCT阶段重建通过过滤backprojection.ConclusionsProof概念测试验证了4DCBCT重建方法的模拟数据的类型。此外，作者发现2D/3D配准方法是我们生成DVF训练集的最佳选择，而Neld-Mead单纯形算法是最稳健的优化例程。
PurposeTo provide a proof of concept validation of a novel 4D cone‐beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm.MethodsThe algorithm animates a patient fan‐beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation.ResultsThe algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo‐periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection.ConclusionsProof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder‐Mead simplex algorithm the most robust optimization routine.