School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, Guangdong 510515, China.
School of Software, Guangdong Food and Drug Vocational College, Guangzhou, Guangdong 510520, China.
Comput Methods Programs Biomed. 2018 Feb;154:57-69. doi: 10.1016/j.cmpb.2017.10.020. Epub 2017 Oct 31.
The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images.
We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic Rb MP PET scan data, we optimized and compared its performance with other restoration methods.
The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms.
The proposed method is effective for restoration and enhancement of dynamic PET images.
由于数据被分为更短的帧，因此单个帧图像的空间分辨率有限，这对动态心肌灌注（MP）PET 成像的绝对定量提出了挑战。本研究旨在开发一种用于恢复和增强动态 PET 图像的方法。
鉴于图像特征几乎不能仅用单一约束来描述，我们建议图像恢复模型应该基于多个约束而不是单一约束。同时，用多个约束同时正则化图像可能是可行的，但不是最优的。幸运的是，MP PET 图像可以通过低秩加稀疏（L+S）分解分解为背景与动态分量的叠加。因此，我们提出了一种基于 L+S 分解的 MP PET 图像恢复模型，并将其表示为凸优化问题。开发了一种迭代软阈值算法来解决该问题。使用现实的动态 Rb MP PET 扫描数据，我们对其进行了优化，并与其他恢复方法进行了性能比较。
在广泛的 Rb MP PET 模拟中，所提出的方法在噪声与偏差性能方面在视觉和定量精度方面都有显著提高。特别是，MP PET 图像中的心肌缺陷在视觉和对比度与噪声的权衡方面都得到了改善。该算法还应用于在 GE Discovery PET/CT 上进行的 8 分钟临床心脏 Rb MP PET 研究，并与其他算法相比，显示出更高的定量精度（CNR 和 SNR）。
所提出的方法对于动态 PET 图像的恢复和增强是有效的。
