PET image reconstruction using information theoretic anatomical priors.
PET image reconstruction using information theoretic anatomical priors.
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DOI：
10.1109/tmi.2010.2076827
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发表时间：
2011-03
影响因子：
10.6
通讯作者：
Leahy RM
中科院分区：
文献类型：
--
作者：
Somayajula S;Panagiotou C;Rangarajan A;Li Q;Arridge SR;Leahy RM
我们描述了一个非参数框架，用于将共同注册的解剖图像中的信息通过基于信息理论相似性指标的PRIORS重建正电子发射断层扫描（PET）图像重建。我们比较并评估从解剖学和宠物图像作为PET重建的先验中提取的特征向量之间的相互信息（MI）和联合熵（JE）的使用。规模空间理论提供了一个框架，用于分析不同级别的图像，我们使用这种方法来定义特征向量，该特征向量强调解剖学和功能性图像中的突出界限，并且对细节和噪声的重要性较小，而细节和噪声则不太可能在两个图像中相关。通过模拟解剖图像和功能性图像之间完美一致的最佳情况，以及具有真实磁共振图像的更现实的情况，并且具有部分强度和强度的平稳变化的宠物幻影，我们评估了基于MI和JE的PRIORS的性能，以与高斯二次二次提前相比，该先验不使用任何Anatomicalsomical。我们还使用F18 Fallypride（一种与多巴胺受体结合，因此主要定位在纹状体中的示踪剂，我们还将这种方法应用于临床脑扫描数据。我们提出了一种基于快速傅立叶变换来计算这些先验及其衍生物的有效方法，从而降低了其类似卷积的表达的复杂性。我们的结果表明，虽然对初始化和选择超参数敏感，但信息理论先验可以比二次先验重建具有更高对比度和较高定量的图像。
We describe a nonparametric framework for incorporating information from co-registered anatomical images into positron emission tomographic (PET) image reconstruction through priors based on information theoretic similarity measures. We compare and evaluate the use of mutual information (MI) and joint entropy (JE) between feature vectors extracted from the anatomical and PET images as priors in PET reconstruction. Scale-space theory provides a framework for the analysis of images at different levels of detail, and we use this approach to define feature vectors that emphasize prominent boundaries in the anatomical and functional images, and attach less importance to detail and noise that is less likely to be correlated in the two images. Through simulations that model the best case scenario of perfect agreement between the anatomical and functional images, and a more realistic situation with a real magnetic resonance image and a PET phantom that has partial volumes and a smooth variation of intensities, we evaluate the performance of MI and JE based priors in comparison to a Gaussian quadratic prior, which does not use any anatomical information. We also apply this method to clinical brain scan data using F18 Fallypride, a tracer that binds to dopamine receptors and therefore localizes mainly in the striatum. We present an efficient method of computing these priors and their derivatives based on fast Fourier transforms that reduce the complexity of their convolution-like expressions. Our results indicate that while sensitive to initialization and choice of hyperparameters, information theoretic priors can reconstruct images with higher contrast and superior quantitation than quadratic priors.