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Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
Published on: May 31, 2024
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Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition,
Joseph G Woods1,2, Eric Achten3, Iris Asllani4,5
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Magnetic Resonance in Medicine
|April 10, 2024
Summary
Multi-timepoint arterial spin labeling (ASL) offers precise brain perfusion assessment by accounting for arterial transit time (ATT). This guideline details acquisition and quantification for improved clinical insights and wider adoption.
Area of Science:
- Neuroimaging
- Medical Physics
- Radiology
Background:
- Accurate cerebral perfusion assessment is crucial for neurological disorder diagnosis and treatment.
- Arterial spin labeling (ASL) is a key quantitative perfusion imaging technique.
- Existing guidelines need updating to incorporate advanced ASL methods.
Purpose of the Study:
- Provide comprehensive guidelines for multi-timepoint ASL brain imaging.
- Recommend protocols for acquisition and quantification.
- Address challenges to promote wider clinical adoption.
Main Methods:
- Review and detail multi-timepoint ASL acquisition protocols, including multiple label durations and post-labeling delays (PLDs).
- Propose an extended quantification model building on the 2015 consensus.
- Discuss post-processing techniques for enhanced data analysis.
Main Results:
- Multi-timepoint ASL enables accurate assessment of cerebral blood flow (CBF) and visualization of arterial transit time (ATT).
- Comparable precision to single-PLD ASL is achievable within similar scan times.
- Identified challenges in acquisition and post-processing that hinder widespread use.
Conclusions:
- Multi-timepoint ASL provides valuable insights into hemodynamic processes by characterizing ATT.
- Updated guidelines and extended quantification models are proposed for robust implementation.
- Significant potential clinical applications are expected, advancing beyond the 2015 consensus.

