Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis
Rapid processing of PET list-mode data for efficient uncertainty estimation and data analysis
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DOI：
10.1088/0031-9155/61/13/n322
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发表时间：
2016-07-07
影响因子：
3.5
通讯作者：
Ourselin, S.
中科院分区：
文献类型：
--
作者：
Markiewicz, P. J.;Thielemans, K.;Ourselin, S.
在本技术说明中，我们提出了一种用于PET列表模式数据处理的快速和可扩展的软件解决方案，它允许将列表模式数据处理有效地集成到图像重建和分析的工作流程中。所有的处理都在图形处理单元（GPU）上执行，利用流和并发内核执行以及磁盘和CPU内存以及CPU和GPU内存之间的数据传输。这种方法可以快速生成多个自举实现，当与快速图像重建和分析相结合时，它可以在合理的时间框架内（例如，每次实现在五分钟内）评估任何图像统计和图像生成过程的任何组件（例如随机校正，图像处理）的不确定性。这在处理复杂的图像生成和处理链时特别有价值。软件输出如下内容：(1)对期望的随机事件数据进行降噪估计；(2) span-1和span-11的动态提示和随机信号图；(3)基于(1)和(2)的多个自举实现的方差估计，假设合理的计数水平可以接受的精度。此外，该软件还可以生成即时质量控制和粗略运动检测的统计数据和可视化，例如：(1)计数率曲线；(2)运动检测辐射分布的质心图；3)动态投影视图视频，用于快速视觉列表模式浏览和检查；(4)全归一化因子图。为了演示该软件，我们提供了一个使用西门子Biograph mMR扫描仪对F-18-florbetapir进行单次PET扫描的区域SUVR（标准吸收值比）计算的快速不确定性估计的上述处理示例。
In this technical note we propose a rapid and scalable software solution for the processing of PET list-mode data, which allows the efficient integration of list mode data processing into the workflow of image reconstruction and analysis. All processing is performed on the graphics processing unit (GPU), making use of streamed and concurrent kernel execution together with data transfers between disk and CPU memory as well as CPU and GPU memory. This approach leads to fast generation of multiple bootstrap realisations, and when combined with fast image reconstruction and analysis, it enables assessment of uncertainties of any image statistic and of any component of the image generation process (e.g. random correction, image processing) within reasonable time frames (e.g. within five minutes per realisation). This is of particular value when handling complex chains of image generation and processing.The software outputs the following: (1) estimate of expected random event data for noise reduction; (2) dynamic prompt and random sinograms of span-1 and span-11 and (3) variance estimates based on multiple bootstrap realisations of (1) and (2) assuming reasonable count levels for acceptable accuracy. In addition, the software produces statistics and visualisations for immediate quality control and crude motion detection, such as: (1) count rate curves; (2) centre of mass plots of the radiodistribution for motion detection; 3) video of dynamic projection views for fast visual list-mode skimming and inspection; (4) full normalisation factor sinograms. To demonstrate the software, we present an example of the above processing for fast uncertainty estimation of regional SUVR (standard uptake value ratio) calculation for a single PET scan of F-18-florbetapir using the Siemens Biograph mMR scanner.