NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.
NiftyPET: a High-throughput Software Platform for High Quantitative Accuracy and Precision PET Imaging and Analysis.
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DOI：
10.1007/s12021-017-9352-y
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发表时间：
2018-01
期刊：
影响因子：
3
通讯作者：
Ourselin S
中科院分区：
文献类型：
--
作者：
Markiewicz PJ;Ehrhardt MJ;Erlandsson K;Noonan PJ;Barnes A;Schott JM;Atkinson D;Arridge SR;Hutton BF;Ourselin S
我们提供了一个用于PET图像重建和分析的独立、可扩展且高通量的软件平台。我们专注于高保真度建模的采集过程，以提供高精度和精确的定量成像，特别是大轴向视场扫描仪。所有核心例程都使用Python包NiftyPET中的并行计算来实现，从而在任何处理阶段都可以轻松访问、操作和可视化数据。该平台的流水线从MR和原始PET输入数据开始，并分为以下处理阶段：（1）列表模式数据处理;（2）准确的衰减系数图生成;（3）探测器归一化;（4）正弦图和图像空间之间的精确正向和反向投影;（5）方差减小的随机事件估计;（6）散射事件的高精度全三维估计;（7）基于体素的部分体积校正;（8）区域和体素级图像分析。我们使用淀粉样蛋白大脑扫描来展示这个平台的优势，其中所有处理都是从Python中的单一统一计算环境执行的。高精度采集建模是通过跨度-1（无轴向压缩）射线跟踪真实，随机和散射事件。此外，该平台还提供任何图像衍生统计数据的不确定性估计，以促进对纵向研究中细微生理变化的鲁棒跟踪。该平台还通过将轴向视场限制为覆盖感兴趣区域的任何一组环来支持新的重建和分析算法的开发，从而使用真实的数据显著更快地执行全3D重建和校正。所有的软件都是开源的，并附有维基页面和测试数据。
We present a standalone, scalable and high-throughput software platform for PET image reconstruction and analysis. We focus on high fidelity modelling of the acquisition processes to provide high accuracy and precision quantitative imaging, especially for large axial field of view scanners. All the core routines are implemented using parallel computing available from within the Python package NiftyPET, enabling easy access, manipulation and visualisation of data at any processing stage. The pipeline of the platform starts from MR and raw PET input data and is divided into the following processing stages: (1) list-mode data processing; (2) accurate attenuation coefficient map generation; (3) detector normalisation; (4) exact forward and back projection between sinogram and image space; (5) estimation of reduced-variance random events; (6) high accuracy fully 3D estimation of scatter events; (7) voxel-based partial volume correction; (8) region- and voxel-level image analysis. We demonstrate the advantages of this platform using an amyloid brain scan where all the processing is executed from a single and uniform computational environment in Python. The high accuracy acquisition modelling is achieved through span-1 (no axial compression) ray tracing for true, random and scatter events. Furthermore, the platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies. The platform also supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster. All the software is available as open source with the accompanying wiki-page and test data.
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DOI：
10.1007/s00259-002-0796-3
发表时间：
2002-07-01
影响因子：
9.1
作者：
Burger, C;Goerres, G;von Schulthess, GK
通讯作者：
von Schulthess, GK
DOI：
10.1007/978-3-319-01712-9_1
发表时间：
2013-01-01
期刊：
LEVEL SET AND PDE BASED RECONSTRUCTION METHODS IN IMAGING
影响因子：
--
作者：
Burger, Martin;Osher, Stanley
通讯作者：
Osher, Stanley
DOI：
10.1007/bf02258426
发表时间：
1991-06-01
期刊：
EUROPEAN JOURNAL OF NUCLEAR MEDICINE
影响因子：
--
作者：
BAILEY, DL;JONES, T;SPINKS, TJ
通讯作者：
SPINKS, TJ
影响因子：
2.1
作者：
Doot, Robert K;McDonald, Elizabeth S;Mankoff, David A
通讯作者：
Mankoff, David A
影响因子：
10.6
作者：
Cardoso, M. Jorge;Modat, Marc;Ourselin, Sebastien
通讯作者：
Ourselin, Sebastien