Deformable registration of glioma images using EM algorithm and diffusion reaction modeling.
Deformable registration of glioma images using EM algorithm and diffusion reaction modeling.
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DOI：
10.1109/tmi.2010.2078833
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发表时间：
2011-02
影响因子：
10.6
通讯作者：
Davatzikos C
中科院分区：
文献类型：
--
作者：
Gooya A;Biros G;Davatzikos C
研究了脑胶质瘤图像的图谱配准问题。多参数成像模式（T1，T1-CE，T2和FLAIR）首先用于不同组织的分割，并使用监督学习计算每个组织类别的隶属度的后验概率图（PBM）。通过使用反应扩散方程对肿瘤生长进行建模，在初始正常图谱中生成类似的图。使用类Demons算法的可变形配准用于将患者图像与肿瘤承载图谱配准。联合估计的模拟肿瘤参数（如位置，质量效应和程度的浸润），空间变换是通过最大化的对数似然观测。在配准过程中使用期望最大化算法估计空间变换，并通过异步并行模式搜索（APPSPACK）优化与肿瘤模拟相关的其他参数。所提出的方法进行了评估统计模拟变形（SSD）创建的五个模拟数据集，和15个真实的多通道胶质瘤数据集。性能进行了定量和定性评估，并将结果与解决类似问题的替代方法ORBIT进行了比较。实验结果表明，该方法优于ORBIT方法，变形后的模板与患者图像具有更好的相似性。
This paper investigates the problem of atlas registration of brain images with gliomas. Multi-parametric imaging modalities (T1, T1-CE, T2, and FLAIR) are first utilized for segmentations of different tissues, and to compute the posterior probability map (PBM) of membership to each tissue class, using supervised learning. Similar maps are generated in the initially normal atlas, by modeling the tumor growth, using reaction-diffusion equation. Deformable registration using a demons-like algorithm is used to register the patient images with the tumor bearing atlas. Joint estimation of the simulated tumor parameters (e.g. location, mass effect and degree of infiltration), and the spatial transformation is achieved by maximization of the log-likelihood of observation. An Expectation-Maximization algorithm is used in registration process to estimate the spatial transformation and other parameters related to tumor simulation are optimized through Asynchronous Parallel Pattern Search (APPSPACK). The proposed method has been evaluated on five simulated data sets created by Statistically Simulated Deformations (SSD), and fifteen real multichannel glioma data sets. The performance has been evaluated both quantitatively and qualitatively, and the results have been compared to ORBIT, an alternative method solving a similar problem. The results show that our method outperforms ORBIT, and the warped templates have better similarity to patient images.