NiftyPET：高通量图像重建和分析

NiftyPET是一个软件平台和Python名称空间软件包，其中包含子软件包，可实现高吞吐量PET图像重建，处理，处理和分析，并具有很高的定量精度和精确度。它的主要应用之一是使用淀粉样蛋白示踪剂在痴呆症中进行脑成像。有关使用NiftyPET重建的上述淀粉状蛋白PET图像（叠加在MR T1加权图像[*]上）的说明，请参见下文。

NiftyPET包括两个软件包：

nimpa：https : //github.com/NiftyPET/NIMPA（神经图像处理，处理和分析）
nipet：https： //github.com/NiftyPET/NIPET（定量PET神经成像）
核心例程是用CUDA C编写的，并嵌入在Python C扩展中，以在NVIDIA图形处理单元（GPU）上实现用户友好且高吞吐量的执行。该软件平台的科学方面包括在两个开放获取的出版物中：

NiftyPET：高通量软件平台，可实现高定量准确性和精确PET成像和分析神经信息学（2018）16:95。https://doi.org/10.1007/s12021-017-9352-y
快速处理PET列表模式数据，以进行有效的不确定性评估和数据分析物理医学与生物学（2016）。https://doi.org/10.1088/0031-9155/61/13/N322
NiftyPET在新颖图像重建开发中的示例应用：

Faster PET Reconstruction with Non-Smooth Priors by Randomization and Preconditioning Physics in Medicine & Biology (2019). https://doi.org/10.1088/1361-6560/ab3d07
Although, NiftyPET is dedicated to high-throughput image reconstruction and analysis of brain images, it can equally well be used for whole body imaging. Strong emphasis is put on the data, which are acquired using positron emission tomography (PET) and magnetic resonance (MR), especially using the hybrid and simultaneous PET/MR scanners.

This software platform covers the entire processing pipeline, from the raw list-mode (LM) PET data through to the final image statistic of interest (e.g., regional SUV), including LM bootstrapping and multiple independent reconstructions to facilitate voxel-wise estimation of uncertainties.

[*] The above dynamic transaxial and coronal images show the activity of 18F-florbetapir during the one-hour dynamic acquisition. Note that the signal in the brain white matter dominates over the signal in the grey matter towards the end of the acquisition, which is a typical presentation of a negative amyloid beta (Abeta) scan.
Documentation

Introduction
Aim
Software infrastructure
Installation
Dependencies
NiftyPET installation
Post-installation checks
Jupyter Notebook
Tutorials

Accessing and querying GPU devices
DICOM anonymisation
Anonymisation in NiftyPET
List-mode processing and motion detection
Motion detection
Basic PET image reconstruction
Initialisation
Sorting and classification of input data
Specifying output folder
Obtaining the hardware and object μ-maps
Static image reconstruction
Dynamic image reconstruction
Dynamic time frame definitions
Dynamic reconstruction
Time offset due to injection delay
Visualisation of dynamic frame timings
Corrections for quantitative PET
Decay correction
Open-source Data

Raw brain PET data
Citing the data
Bibliography

References
Acknowledgements

Acknowledgements