Chappell Michael A, Kirk Thomas F, Craig Martin S, McConnell Flora A Kennedy, Zhao Moss Y, MacIntosh Bradley J, Okell Thomas W, Woolrich Mark W
Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, United Kingdom.
Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom.
Imaging Neurosci (Camb). 2023 Dec 5;1. doi: 10.1162/imag_a_00041. eCollection 2023.
Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article, we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL), and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities.
动脉自旋标记(ASL)磁共振成像现已成为一种成熟的无创性脑血流量定量方法,并越来越多地应用于各种神经成像领域。随着标准ASL采集协议的广泛应用,人们对能够提供更高定量精度以及灌注以外血流动力学信息的先进方法的兴趣日益浓厚。在本文中,我们介绍了BASIL工具箱,这是一种用于分析FMRIB软件库(FSL)中包含的ASL数据的研究工具,并解释了其在各种典型用例中的操作。BASIL并非作为临床工具提供,本文也无意指导ASL的临床应用。该工具箱围绕基于贝叶斯模型的推理算法构建,旨在从各种可能的ASL输入数据中量化灌注和其他血流动力学指标,如动脉通过时间,尤其利用了更先进的多延迟采集中可用的信息。最简单的情况下,BASIL工具箱提供了一个图形用户界面,可提供大多数用户所需的分析选项;通过命令行工具,它为需要定制分析的用户提供了更多定制选项。作为FSL的一部分,该工具箱利用了一系列互补的神经成像分析工具,以便ASL数据能够轻松地集成到神经成像研究中,并与其他模态一起使用。