PET image reconstruction using multi-parametric anato-functional priors
PET image reconstruction using multi-parametric anato-functional priors
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DOI：
10.1088/1361-6560/aa7670
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发表时间：
2017-08-07
影响因子：
3.5
通讯作者：
Reader, Andrew J.
中科院分区：
文献类型：
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作者：
Mehranian, Abolfazl;Belzunce, Martin A.;Reader, Andrew J.
在这项研究中，我们研究了应用多参数解剖功能（MR-PET）先验的最大后验（MAP）重建的脑PET数据，以解决传统的解剖先验的PET-MR不匹配的存在下的局限性。除了部分容积校正的好处，这些先验重建的低计数PET数据的适用性也被介绍和证明，比较高计数数据的标准最大似然（ML）重建。传统的局部Tikhonov和全变差（TV）先验和当前国家的最先进的解剖先验，包括Kaipio，非局部Tikhonov与Bowsher和高斯相似性核的先验进行了研究，并提出了一个统一的框架。高斯核计算使用体素和补丁为基础的特征向量。为了科普PET和MR失配，Bowsher和高斯先验被扩展到多参数先验。此外，我们提出了一个修改后的联合布尔格熵之前，根据定义，利用所有的参数信息的MAP重建PET数据。使用3D模拟和[F-18] florbetaben和[F-18] FDG放射性示踪剂的两个临床脑数据集对先验的性能进行了广泛评价。对于模拟，在PET和MR图像之间故意引入了几个解剖功能不匹配，此外，对于FDG临床数据集，将两个PET独特的活动性肿瘤嵌入PET数据中。我们的模拟结果表明，联合布尔格熵先验在保留PET独特病变方面远远优于传统的解剖先验，同时仍然用相应的MR边界重建功能边界。此外，高斯和Bowsher先验的多参数扩展导致边缘和PET独特特征的增强保留，以及改进的偏差方差性能。与模拟结果一致，临床结果还表明，高斯先验与基于体素的特征向量，Bowsher和联合布尔格熵先验是性能最好的先验。然而，对于具有模拟肿瘤的FDG数据集，TV和提出的先验能够保留PET独特的肿瘤。最后，一个重要的结果是证明了使用所提出的联合熵先验对低计数FDG PET数据集进行MAP重建可以获得与传统ML重建相当的图像质量，其计数高达5倍。总之，多参数解剖功能先验提供了一种解决方案，以解决传统先验的缺陷，因此可能会增加MR引导PET图像重建的诊断信心。
In this study, we investigate the application of multi-parametric anato-functional (MR-PET) priors for the maximum a posteriori (MAP) reconstruction of brain PET data in order to address the limitations of the conventional anatomical priors in the presence of PET-MR mismatches. In addition to partial volume correction benefits, the suitability of these priors for reconstruction of low-count PET data is also introduced and demonstrated, comparing to standard maximum-likelihood (ML) reconstruction of high-count data. The conventional local Tikhonov and total variation (TV) priors and current state-of-the-art anatomical priors including the Kaipio, non-local Tikhonov prior with Bowsher and Gaussian similarity kernels are investigated and presented in a unified framework. The Gaussian kernels are calculated using both voxel- and patch-based feature vectors. To cope with PET and MR mismatches, the Bowsher and Gaussian priors are extended to multi-parametric priors. In addition, we propose a modified joint Burg entropy prior that by definition exploits all parametric information in the MAP reconstruction of PET data. The performance of the priors was extensively evaluated using 3D simulations and two clinical brain datasets of [F-18] florbetaben and [F-18] FDG radiotracers. For simulations, several anato-functional mismatches were intentionally introduced between the PET and MR images, and furthermore, for the FDG clinical dataset, two PET-unique active tumours were embedded in the PET data. Our simulation results showed that the joint Burg entropy prior far outperformed the conventional anatomical priors in terms of preserving PET unique lesions, while still reconstructing functional boundaries with corresponding MR boundaries. In addition, the multi-parametric extension of the Gaussian and Bowsher priors led to enhanced preservation of edge and PET unique features and also an improved bias-variance performance. In agreement with the simulation results, the clinical results also showed that the Gaussian prior with voxel-based feature vectors, the Bowsher and the joint Burg entropy priors were the best performing priors. However, for the FDG dataset with simulated tumours, the TV and proposed priors were capable of preserving the PET-unique tumours. Finally, an important outcome was the demonstration that the MAP reconstruction of a low-count FDG PET dataset using the proposed joint entropy prior can lead to comparable image quality to a conventional ML reconstruction with up to 5 times more counts. In conclusion, multi-parametric anato-functional priors provide a solution to address the pitfalls of the conventional priors and are therefore likely to increase the diagnostic confidence in MR-guided PET image reconstructions.