Optimising combined angiographic, structural and perfusion imaging using arterial spin labelling MRI

Thesis

Visualizing the dynamic blood flow in the brain is crucial for diagnosing and monitoring cerebrovascular diseases. Arterial spin labeling (ASL) MRI offers a non-invasive approach to simultaneously image both dynamic blood flow and tissue perfusion without contrast agents. However, ASL techniques face challenges such as limited spatial/temporal resolution, low signal-to-noise ratio (SNR), and susceptibility to motion artifacts. This thesis focuses on addressing these limitations to enhance the quality and clinical utility of ASL-based combined imaging of blood vessels, brain structure, and tissue perfusion.

Firstly, a novel, efficient two-stage curved cone trajectory is introduced for k-space sampling, improving the spatial resolution and SNR of both angiographic and perfusion images compared to conventional radial trajectories. The trajectory design allows for flexible parameter adjustments and is adaptable to various scanner hardware constraints. To further improve the temporal resolution of dynamic angiography, a subspace reconstruction method incorporating a kinetic model of blood flow is implemented. This approach achieves ultra-high temporal resolution, enabling detailed visualization and accurate quantification of blood flow dynamics. To mitigate motion artifacts, a novel motion correction technique is presented, utilizing subspace-based self-navigation to reconstruct robust navigator images. The proposed method effectively corrects motion artifacts, enabling accurate assessment of blood flow and perfusion even in the presence of subject motion.

This thesis contributes to the advancement of ASL-based imaging techniques by enhancing spatial and temporal resolution, improving SNR, and correcting for motion artifacts. The developed methods have the potential to improve the clinical diagnosis for neurovascular disease.

Authors: Shen, Q

• DOI: 10.5287/ora-vjgq11885
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English