Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.
Direct 4D reconstruction of parametric images incorporating anato-functional joint entropy.
复制标题
参数图像的直接四维重建结合了函数联合熵。
DOI：
10.1088/0031-9155/55/15/005
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发表时间：
2010-08-07
影响因子：
3.5
通讯作者：
Rahmim A
中科院分区：
文献类型：
--
作者：
Tang J;Kuwabara H;Wong DF;Rahmim A
我们开发了一种解剖引导的4D封闭算法，可以直接从投影数据中重建（几乎）不可逆示踪剂的参数图像。传统的方法是分别重建二维/三维PET数据，然后对重建图像帧序列进行图形化分析。该方法通过扩展系统矩阵以包含参数图像与测量数据之间的关系，保持了期望最大化（EM）算法的简单性和准确性。在EM框架中使用不同的隐藏完整数据公式实现了封闭形式的解决方案。在此基础上，以MR与参数PET特征的联合熵为先验，将该方法扩展到结合MR图像信息的最大后验重构。利用逼真的模拟噪声[11C]-纳特哚PET和MR脑图像/数据，对所提方法的定量性能进行了研究。在进行直接参数重建时，以及在使用MR-PET联合熵度量将算法扩展到其贝叶斯对偶时，证明了噪声与偏差性能方面的显着改进。
We developed an anatomy-guided 4D closed-form algorithm to directly reconstruct parametric images from projection data for (nearly) irreversible tracers. Conventional methods consist of individually reconstructing 2D/3D PET data, followed by graphical analysis on the sequence of reconstructed image frames. The proposed direct reconstruction approach maintains the simplicity and accuracy of the expectation-maximization (EM) algorithm by extending the system matrix to include the relation between the parametric images and the measured data. A closed-form solution was achieved using a different hidden complete-data formulation within the EM framework. Furthermore, the proposed method was extended to maximum a posterior reconstruction via incorporation of MR image information, taking the joint entropy between MR and parametric PET features as the prior. Using realistic simulated noisy [11C]-naltrindole PET and MR brain images/data, the quantitative performance of the proposed methods was investigated. Significant improvements in terms of noise versus bias performance were demonstrated when performing direct parametric reconstruction, and additionally upon extending the algorithm to its Bayesian counterpart using the MR-PET joint entropy measure.
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