Rahmim Arman, Cheng Ju-Chieh, Blinder Stephan, Camborde Maurie-Laure, Sossi Vesna
Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Phys Med Biol. 2005 Oct 21;50(20):4887-912. doi: 10.1088/0031-9155/50/20/010. Epub 2005 Oct 4.
Modern high-resolution PET is now more than ever in need of scrutiny into the nature and limitations of the imaging modality itself as well as image reconstruction techniques. In this work, we have reviewed, analysed and addressed the following three considerations within the particular context of state-of-the-art dynamic PET imaging: (i) the typical average numbers of events per line-of-response (LOR) are now (much) less than unity, (ii) due to the physical and biological decay of the activity distribution, one requires robust and efficient reconstruction algorithms applicable to a wide range of statistics and (iii) the computational considerations in dynamic imaging are much enhanced (i.e., more frames to be stored and reconstructed). Within the framework of statistical image reconstruction, we have argued theoretically and shown experimentally that the sinogram non-negativity constraint (when using the delayed-coincidence and/or scatter-subtraction techniques) is especially expected to result in an overestimation bias. Subsequently, two schemes are considered: (a) subtraction techniques in which an image non-negativity constraint has been imposed and (b) implementation of random and scatter estimates inside the reconstruction algorithms, thus enabling direct processing of Poisson-distributed prompts. Both techniques are able to remove the aforementioned bias, while the latter, being better conditioned theoretically, is able to exhibit superior noise characteristics. We have also elaborated upon and verified the applicability of the accelerated list-mode image reconstruction method as a powerful solution for accurate, robust and efficient dynamic reconstructions of high-resolution data (as well as a number of additional benefits in the context of state-of-the-art PET).
现代高分辨率正电子发射断层扫描（PET）比以往任何时候都更需要仔细研究成像模态本身以及图像重建技术的本质和局限性。在这项工作中，我们在当前最先进的动态PET成像的特定背景下，回顾、分析并探讨了以下三个方面：（i）现在每个响应线（LOR）的典型平均事件数（远）小于1；（ii）由于活度分布的物理和生物衰变，需要适用于广泛统计数据的强大而高效的重建算法；（iii）动态成像中的计算考量大大增加（即需要存储和重建更多的帧）。在统计图像重建的框架内，我们从理论上进行了论证，并通过实验表明，（在使用延迟符合和/或散射减法技术时）正弦图非负性约束特别容易导致高估偏差。随后，考虑了两种方案：（a）施加图像非负性约束的减法技术；（b）在重建算法中实现随机和散射估计，从而能够直接处理泊松分布的真符合事件。这两种技术都能够消除上述偏差，而后者在理论上条件更好，能够表现出更好的噪声特性。我们还详细阐述并验证了加速列表模式图像重建方法作为一种强大解决方案的适用性，该方法可用于对高分辨率数据进行准确、稳健和高效的动态重建（以及在当前最先进的PET背景下的许多其他优点）。
