Patching cardiac and head motion artefacts in diffusion-weighted images.

Area of Science:

• Medical Imaging
• Neuroimaging
• Biophysics

Background:

• Motion artefacts are a significant challenge in diffusion-weighted imaging (DWI), compromising the accuracy of diffusion model estimations.
• Existing methods often fail to adequately address artefacts caused by head and cardiac motion.
• Accurate diffusion modelling is crucial for understanding brain microstructure and detecting neurological disorders.

Purpose of the Study:

• To develop and evaluate a novel, spatially informed, robust tensor estimation method for detecting and mitigating motion artefacts in DWI.
• To enhance the accuracy and reliability of diffusion model estimations in the presence of motion.
• To provide objective artefact-related weights for subsequent advanced diffusion model processing.

Main Methods:

• Implementation of a robust tensor estimation technique incorporating spatial information.
• Simulations to assess the method's robustness, accuracy, and sensitivity to artefact magnitude and frequency.
• Validation using real-world DWI datasets, including those with substantial motion artefacts.

Main Results:

• The proposed method demonstrates superior robustness and accuracy compared to previous techniques.
• Tensor estimates are more precise in artefact-free conditions and less affected by increasing artefact magnitude and frequency.
• Excellent performance was observed on real DWI data, even with high levels of motion that would typically render data unusable.

Conclusions:

• The developed robust tensor estimation method effectively addresses motion artefacts in DWI.
• This approach significantly improves the reliability of diffusion model estimations, even in challenging datasets.
• The method's outputs can inform advanced diffusion modelling, expanding its utility beyond diffusion tensor imaging.