NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-04-01 DOI:10.1007/s12021-022-09616-0
Jieqing Jiao, Fiona Heeman, Rachael Dixon, Catriona Wimberley, Isadora Lopes Alves, Juan Domingo Gispert, Adriaan A Lammertsma, Bart N M van Berckel, Casper da Costa-Luis, Pawel Markiewicz, David M Cash, M Jorge Cardoso, Sebastién Ourselin, Maqsood Yaqub, Frederik Barkhof
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Abstract

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

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NiftyPAD -用于动态PET数据定量分析的新颖Python包。
当前的PET数据集越来越大,从而增加了对快速和可重复处理管道的需求。本文介绍了一个免费的、开源的、基于python的软件包NiftyPAD,用于静态、全时间或双时间窗动态脑PET数据的通用分析。NiftyPAD的关键创新之处在于,通过参考输入处理分析双时间窗口扫描,通过结合动脉自旋标记(ASL)衍生的相对灌注测量,缩短PET采集的药代动力学建模,以及可选的基于PET数据的运动校正。用NiftyPAD得到的结果与完善的软件包pet和QModeling进行了一系列动力学模型的比较。使用四种不同淀粉样蛋白示踪剂扫描的8名受试者的临床数据来验证计算性能。对于线性化的Logan和MRTM2方法,NiftyPAD实现了与pet的[公式:见文]相关性,具有绝对差异[公式:见文];对于基于基函数的SRTM和SRTM2模型,NiftyPAD与QModeling的[公式:见文]相关性,具有绝对差异[公式:见文]。对于最近发表的SRTM ASL方法,该方法在现有软件包中不可用,在不可置换结合电位方面,与全扫描SRTM观察到高度相关性,偏差可忽略不计([公式:见文本]),表明在NiftyPAD中可靠的模型实现。总之,这些发现表明,NiftyPAD是通用的、灵活的,并且可以与现有的用于定量动态PET数据的软件包产生可比较的结果。它是免费的(https://github.com/AMYPAD/NiftyPAD),并允许多平台使用。模块化的设置使得添加新功能变得容易,并且该包是轻量级的,具有最小的依赖关系,使其易于使用和集成到现有的处理管道中。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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