ASL-BIDS, the brain imaging data structure extension for arterial spin labeling

Abstract

Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.

Three-dimensional pseudocontinuous arterial spin labeling perfusion imaging shows cerebral blood flow perfusion decline in attention-deficit/hyperactivity disorder children

“…The results in Table 2 showed that the volume and CBF values of brain regions such as frontal lobe, temporal lobe, and basal ganglia were lower in ADHD children than in healthy children, suggesting poor development of those brain regions and decreased CBF perfusion in ADHD children. The poor development of frontal lobe, temporal lobe, and basal ganglia in ADHD children may be due to the poor development of brain regions caused by longterm inadequate blood perfusion in these three brain regions, which ultimately leads to the development of clinical symptoms such as hyperactivity in ADHD children (42)(43)(44). The results showed that the gray matter volume and CBF values of brain regions such as frontal lobe, temporal lobe and basal ganglia were lower in ADHD children with ADHD than in healthy children, but only the volume of white matter was lower than in healthy children, and the difference in CBF values was not statistically significant, suggesting that the microstructural changes in the brain of ADHD children are mainly in the gray matter, which is probably because there is more blood flow in the gray matter and less blood flow in the white matter.…”

Current status in spatiotemporal analysis of contrast‐based perfusion MRI

“…62 In recent years, there has been increased focus on open science within the perfusion imaging community via the forming of the International Society for Magnetic Resonance in Medicine Open Science Initiative for Perfusion Imaging an initiative and activity of the International Society for Magnetic Resonance in Medicine Perfusion study group. 63 Contributions from Open Science Initiative for Perfusion Imaging and related projects cover challenges, code libraries, 64 standardized data formats, 65 and recommended lexicon naming conventions for DCE, DSC, and ASL. 66,67 Application of these and other software development guidelines [68][69][70] to new contributions within the field will help to accelerate the pace of progression.…”

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics – Methodological Overview and Use Cases

“…In recent years, considerable advancements have been made regarding both standardized ASL data storage as well as standardized perfusion analyses. One example is the extension of the Brain Imaging Data Structure (BIDS) for ASL data, in order to provide data storage standards that meet the need for structured image data organization, including also metadata beyond the image files (e. g., acquisition characteristics such as voxel sizes) [33,34]. Furthermore, software packages such as ExploreASL (written in MATLAB and based on Statistical Parametric Mapping [SPM]) have been developed recently, which may facilitate standardized analysis of ASL data across centers and scanners [35].…”