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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1536752
This article is part of the Research Topic Advancing High-Resolution 3T MRI for Cognitive and Clinical Neuroscience View all articles

Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI

Provisionally accepted
Thomas Kirk Thomas Kirk 1,2*Georgia G Kenyon Georgia G Kenyon 2,3Martin S Craig Martin S Craig 1,2Michael A Chappell Michael A Chappell 1,2
  • 1 Quantified Imaging Limited, London, United Kingdom
  • 2 University of Nottingham, Nottingham, England, United Kingdom
  • 3 University of Adelaide, Adelaide, South Australia, Australia

The final, formatted version of the article will be published soon.

    Multiple-timepoint arterial spin labelling MRI is a non-invasive imaging technique that permits measurement of both cerebral blood flow and arterial transit time, the latter of which is an emerging biomarker of interest for cerebrovascular health. Quantification of arterial spin labelling data is challenging due to the low signal to noise ratio and non-linear tracer kinetics of this technique. In this work, we introduce a new quantification method called SSVB that addresses limitations in existing methods and demonstrate its performance using simulations and acquisition data. Simulations showed that the method is more accurate, particularly for estimating arterial transit time, and more robust to noise than existing techniques. On high spatial resolution data acquired at 3T, the method produced less noisy parameter maps than the comparator method and captured greater variation in arterial transit time on a cross-sectional cohort.

    Keywords: Perfusion, arterial transit time (ATT), Arterial Spin Label (ASL) MRI, cerebral blood flow (CBF), quantification

    Received: 29 Nov 2024; Accepted: 10 Jan 2025.

    Copyright: © 2025 Kirk, Kenyon, Craig and Chappell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Thomas Kirk, Quantified Imaging Limited, London, United Kingdom

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.