{"doi":"10.1162/imag_a_00041","title":"BASIL: A toolbox for perfusion quantification using arterial spin labelling","abstract":"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.","journal":"Imaging Neuroscience","year":2023,"id":321525,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":44,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9464,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":807950,"name":"Thomas Kirk","orcid":"0000-0002-3458-2104","position":1,"is_corresponding":false},{"id":762342,"name":"Martin Craig","orcid":"0000-0003-4956-4512","position":2,"is_corresponding":false},{"id":737751,"name":"Flora A. Kennedy McConnell","orcid":"0000-0002-5149-1511","position":3,"is_corresponding":false},{"id":491840,"name":"Moss Zhao","orcid":"0000-0002-0210-7739","position":4,"is_corresponding":false},{"id":259234,"name":"Bradley J. MacIntosh","orcid":"0000-0001-7300-2355","position":5,"is_corresponding":false},{"id":12953,"name":"Thomas W. Okell","orcid":"0000-0001-8258-0659","position":6,"is_corresponding":false},{"id":237762,"name":"Mark W. Woolrich","orcid":"0000-0001-8460-8854","position":7,"is_corresponding":false},{"id":762343,"name":"Michael A. Chappell","orcid":"0000-0003-1802-4214","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-19T01:07:32.611759Z","pmid":"40799705","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}