Mehranian Abolfazl, Belzunce Martin A, Niccolini Flavia, Politis Marios, Prieto Claudia, Turkheimer Federico, Hammers Alexander, Reader Andrew J
Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King's College London, St. Thomas' Hospital, London, United Kingdom.
Phys Med Biol. 2017 Jul 6;62(15):5975-6007. doi: 10.1088/1361-6560/aa7670.
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]florbetaben and [F]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.
在本研究中，我们探究多参数解剖功能（MR-PET）先验在脑PET数据的最大后验（MAP）重建中的应用，以解决在存在PET-MR不匹配情况时传统解剖先验的局限性。除了部分容积校正的益处外，还介绍并展示了这些先验对低计数PET数据重建的适用性，并与高计数数据的标准最大似然（ML）重建进行比较。研究了传统的局部蒂霍诺夫和总变差（TV）先验以及当前最先进的解剖先验，包括凯皮奥、带有鲍舍尔和高斯相似性核的非局部蒂霍诺夫先验，并在一个统一框架中呈现。高斯核使用基于体素和基于补丁的特征向量来计算。为了应对PET和MR的不匹配，将鲍舍尔和高斯先验扩展为多参数先验。此外，我们提出了一种改进的联合伯格熵先验，根据定义，它在PET数据的MAP重建中利用了所有参数信息。使用3D模拟以及[F]氟贝他宾和[F]FDG放射性示踪剂的两个临床脑数据集对这些先验的性能进行了广泛评估。对于模拟，在PET和MR图像之间故意引入了几种解剖功能不匹配情况，此外，对于FDG临床数据集，在PET数据中嵌入了两个PET特有的活性肿瘤。我们的模拟结果表明，在保留PET特有的病变方面，联合伯格熵先验远远优于传统解剖先验，同时仍能将功能边界与相应的MR边界进行重建。此外，高斯和鲍舍尔先验的多参数扩展导致边缘和PET特有的特征得到更好的保留，并且偏差-方差性能也有所改善。与模拟结果一致，临床结果还表明，基于体素特征向量的高斯先验、鲍舍尔先验和联合伯格熵先验是性能最佳的先验。然而，对于带有模拟肿瘤的FDG数据集，TV先验和所提出的先验能够保留PET特有的肿瘤。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重建可以得到与传统ML重建相当的图像质量，而传统ML重建的计数最多可达其5倍。总之，多参数解剖功能先验为解决传统先验的缺陷提供了一种解决方案，因此可能会提高MR引导的PET图像重建中的诊断置信度。
