Abstract：
Positron emission tomography (PET) molecular biomarkers and diffusion magnetic resonance imaging (dMRI) derived information show associations and highly complementary information in a number of neurodegenerative conditions, including Alzheimer\'s disease. Diffusion MRI provides valuable information about the microstructure and structural connectivity (SC) of the brain which could guide and improve the PET image reconstruction when such associations exist. However, this potental has not been previously explored. In the present study, we propose a CONNectome-based non-local means one-atep late maximuma posteriori(CONN-NLM-OSLMAP) method, which allows diffusion MRI-derived connectivity information to be incorporated into the PET iterative image reconstruction process, thus regularising the estimated PET images. The proposed method was evaluated using a realistic tau-PET/MRI simulated phantom, demonstrating more effective noise reduction and lesion contrast improvement, as well as the lowest overall bias compared with both a median filter applied as an alternative regulariser and CONNectome-based non-local means as a post-reconstruction filter. By adding complementary SC information from diffusion MRI, the proposed regularisation method offers more useful and targeted denoising and regularisation, demonstrating the feasibility and effectiveness of integrating connectivity information into PET image reconstruction.
摘要：
正电子发射断层扫描（PET）分子生物标志物和扩散磁共振成像（dMRI）衍生的信息显示了许多神经退行性疾病中的关联和高度互补的信息，包括老年痴呆症.弥散MRI提供了有关大脑微观结构和结构连通性的有价值的信息，当存在这种关联时，可以指导和改善PET图像重建。然而,这个potental以前没有被探索过。在本研究中,我们提出了一种基于CONNectome的非局部均值一步延迟最大后验(CONN-NLM-OSLMAP)方法，它允许将扩散MRI衍生的连接信息纳入PET迭代图像重建过程中，从而调整估计的PET图像。使用逼真的PET/MRI模拟体模对所提出的方法进行了评估，显示更有效的降噪和病变对比度改善，以及与用作替代正则器的中值滤波器和用作重建后滤波器的CONN-NLM相比的最低总体偏差。通过添加来自扩散MRI的互补结构连通性信息，提出的正则化方法提供了更有用和更有针对性的去噪和正则化，证明了将连通性信息集成到PET图像重建中的可行性和有效性。
