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Related Experiment Video

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Profiling Maternal Behavior Responses During Whole-Brain Imaging
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Inter-Shot Motion Correction of Segmented 3D-GRASE ASL Perfusion Imaging With Self-Navigation and CAIPI.

Minhao Hu1, Frederik J Lange1, Peter Jezzard1

  • 1Oxford Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.

Magnetic Resonance in Medicine
|May 24, 2026
PubMed
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Dynamic pseudo-continuous arterial spin labeling angiography using a 3D-radial multi-spoke spoiled gradient-recalled sequence.

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A new self-navigated motion correction method improves Arterial Spin Labeling (ASL) imaging quality. This technique enhances motion robustness in segmented 3D Gradient and Spin Echo (GRASE) ASL scans, outperforming older methods.

Area of Science:

  • Magnetic Resonance Imaging
  • Medical Imaging
  • Neuroimaging

Background:

  • Segmented 3D Gradient and Spin Echo (GRASE) is crucial for Arterial Spin Labeling (ASL) perfusion imaging.
  • Inter-shot motion in ASL imaging causes significant subtraction errors, compromising image quality.
  • Existing motion correction methods for ASL are often insufficient to address inter-shot motion artifacts.

Purpose of the Study:

  • To develop and evaluate a retrospective self-navigated inter-shot motion correction technique for segmented 3D-GRASE ASL.
  • To integrate Controlled Aliasing in Parallel Imaging (CAIPI) with motion correction for enhanced ASL imaging.
  • To assess the performance of the novel method against conventional techniques and explore CAIPI sampling strategies.

Main Methods:

Keywords:
3D segmented readoutCAIPI samplingarterial spin labelingperfusion imagingretrospective motion correctionself‐navigation

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  • Reconstruction of self-navigator images from multiple shots using SENSE (Sensitivity Encoding).
  • Incorporation of rigid-body motion estimation into a motion-compensated forward model for image reconstruction.
  • Evaluation of two CAIPI-sampled segmented 3D-GRASE trajectories and comparison with conventional inter-volume registration and alignedSENSE, using instructed head motion in healthy volunteers.

    • Main Results:

    • The developed method significantly reduced motion artifacts, outperforming conventional correction by up to 40.1% in temporal SNR.
    • It achieved performance comparable to alignedSENSE but with 80% less computational time.
    • Tag-control (T/C) inner loop acquisition demonstrated superior motion robustness across quantitative metrics.

    Conclusions:

    • Retrospective self-navigated inter-shot motion correction is effective for segmented 3D-GRASE ASL.
    • Utilizing CAIPI sampling and a T/C inner loop enhances image quality and motion robustness in ASL.
    • This approach offers a promising solution for improving the reliability of ASL perfusion imaging.