PET心肌灌注显像(Myocardial Perfusion Imaging，MPI)作为一种检测心肌缺血的非侵入性检查手段，可以为冠心病的早期诊断、危险度分层与后期治疗决策提供可靠的依据，较SPECT具有显著优势。其显像过程需要对注入示踪剂的心脏进行动态数据采集，然而，随时间采样增加，单帧光子计数减少，重建图像质量严重退化，直接影响后续定量分析的准确性与可靠性。鉴于此，本项目拟开展基于低秩与稀疏矩阵分解的心肌灌注动态PET图像重建方法研究，主要包括: (1)基于低秩与稀疏矩阵分解，将动态PET图像分解为背景与动态成分，为动态PET图像重建模型构建特有的低秩与稀疏约束；(2) 结合心肌灌注显像的动力学特性，分析动态成分在不同变换域中的稀疏表达，以优化稀疏约束构建；(3)开展PET心肌灌注显像分析与评价研究，优化重建模型与参数，以期提高PET图像质量，为冠心病患者的治疗提供准确可靠的依据。

Myocardial perfusion PET imaging, as a non-invasive examination method for myocardial ischemia detection, can provide improved diagnostic accuracy, risk stratification and treatment decision and has distinct advantage compared to SPECT. It requires dynamic cardiac PET scans. However, with the increased temporal sample in dynamic PET imaging, the detected counts of single frame decrease and the quality of the associated image is commonly deteriorated, which will affect the accuracy and reliability of the quantitative analysis...The object of this proposal is to develop algorithms for low-rank plus sparse matrix decomposition based dynamic myocardial perfusion PET image reconstruction, mainly including: (1)The dynamic PET images were decomposed into background and dynamic components by low-rank plus sparse decomposition, and the specific low-rank and sparse constraints were provided to the PET image reconstruction model;(2)Considering the kinetic characteristic of myocardial perfusion imaging, the sparse representation of dynamic component was studied in different transform domain to optimize the sparse constraint; (3) The analysis and evaluation metrics of myocardial perfusion imaging were studied to optimize the reconstruction model and parameters to improve the quality of reconstructed image and provide accuracy and reliability evidence for the treatment of coronary heart disease.