ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): ASL pipeline inventory

管道运输 管道(软件) 计算机科学 灵活性(工程) 可用性 过程(计算) 信息学 软件工程 人机交互 操作系统 工程类 统计 数学 电气工程 环境工程
作者
Hongli Fan,Henk‐Jan Mutsaerts,Udunna Anazodo,Daniel Arteaga,Koen P.A. Baas,Charlotte Buchanan,Aldo Camargo,Vera C. Keil,Zixuan Lin,Thomas Lindner,Lydiane Hirschler,Jian Hu,Beatriz E. Padrela,Mehdi Zeinalizadeh,David L. Thomas,Sudipto Dolui,Jan Petr
出处
期刊:Magnetic Resonance in Medicine [Wiley]
被引量:1
标识
DOI:10.1002/mrm.29869
摘要

Abstract Purpose To create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly available pipelines to guide novice ASL users in selecting their optimal pipeline. Methods Developers self‐assessed their pipelines using a questionnaire developed by the Task Force 1.1 of the ISMRM Open Science Initiative for Perfusion Imaging. Additionally, each publicly available pipeline was evaluated by two independent testers with basic ASL experience using a scoring system created for this purpose. Results The developers of 21 pipelines filled the questionnaire. Most pipelines are free for noncommercial use (n = 18) and work with the standard NIfTI (Neuroimaging Informatics Technology Initiative) data format (n = 15). All pipelines can process standard 3D single postlabeling delay pseudo‐continuous ASL images and primarily differ in their support of advanced sequences and features. The publicly available pipelines (n = 9) were included in the independent testing, all of them being free for noncommercial use. The pipelines, in general, provided a trade‐off between ease of use and flexibility for configuring advanced processing options. Conclusion Although most ASL pipelines can process the common ASL data types, only some (namely, ASLPrep, ASLtbx, BASIL/Quantiphyse, ExploreASL, and MRICloud) are well‐documented, publicly available, support multiple ASL types, have a user‐friendly interface, and can provide a useful starting point for ASL processing. The choice of an optimal pipeline should be driven by specific data to be processed and user experience, and can be guided by the information provided in this ASL inventory.

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